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It can help us better understand the opportunities and challenges renewable energy creates and learn from the experience of others. In this way, we will all, whether in politics, business, education, or at home, be better equipped to make, with confidence, the vital decisions that will lead to the energy system we seek for the futurea system that is at once secure, sustainable, and economically robust.From the Foreword by GNTHER OETTINGER, European Union Energy Commissioner
A comprehensive and timely review of the international experience in fostering the use of renewable energy sources in the electricity industry. Understanding the political, business, and technological drivers behind the clean energy revolution is important as many nations aim to enable low-carbon development paths that will bring economic prosperity to their citizens, while at the same time reducing human impacts on the environment.BLAS PREZ HENRQUEZ, executive director, Center for Environmental Public Policy at the Goldman School of Public Policy, University of California, Berkeley
This book should be devoured by thoughtful policymakers considering how renewables will fit into decarbonized electric systems. Bringing varied perspectives on this fundamental question, these scholars make a provocative and informative contribution to a topic of critical importance to governments around the world.SUE TIERNEY, managing principal, Analysis Group, Inc., USA
Reflecting its reliance on fossil fuels, the electric power industry produces the majority of the world's greenhouse gas emissions. The need for a revolution in the industry becomes further apparent given that "decarbonization" means an increasing electrification of other sectors of the economyin particular, through a switch from gasoline to electric vehicles. Of the options for producing electric power without significant greenhouse gas emissions, renewable energy is most attractive to policymakers, as it promises increased national self-reliance on energy supplies and the creation of new industries and jobs, without the safety and political concerns of nuclear power or the unproven technology of carbon capture and storage. Drawing on both economic theory and the experiences of the United States and EU member states, Harnessing Renewable Energy in Electric Power Systems addresses the key questions surrounding renewable energy policies. How appropriate is the focus on renewable power as a primary tool for reducing greenhouse gas emissions? If renewable energy is given specific support, what form should that support take? What are the implications for power markets if renewable generation is widely adopted? Thorough and well evidenced, this book will be of interest to a broad range of policymakers, the electric power industry, and economists who study energy and environmental issues. BOAZ MOSELLE is a director and J ORGE PADILLA a managing director at LECG, an international consulting group focusing on economic and financial analyses to provide objective opinions and advice regarding complex disputes and inform legislative, judicial, regulatory, and business decision makers. R ICHARD SCHMALENSEE is dean emeritus and professor of economics and management at MIT's Sloan School of Management, and director of the MIT Center for Energy and Environmental Policy Research.Energy / Economics / Climate
www.rffpress.orgRFF Press strives to minimize its impact on the environment
Washington, DC
First published in 2010 by RFF Press, an imprint of Earthscan Copyright Earthscan 2010 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except as expressly permitted by law, without the prior, written permission of the publisher. Earthscan LLC,1616 P Street, NW, Washington, DC 20036, USA Earthscan Ltd, Dunstan House, 14a St Cross Street, London EC1N 8XA, UK Earthscan publishes in association with the International Institute for Environment and Development For more information on RFF Press and Earthscan publications, see www.rffpress.org and www.earthscan.co.uk or write to email@example.com ISBN: 978-1-933115-90-0 Copyedited by Joyce Bond Typeset by KerryPress Ltd Cover design by Circle Graphics Library of Congress Cataloging-in-Publication Data Harnessing renewable energy in electric power systems : theory, practice, policy/edited by Boaz Moselle, Jorge Padilla, and Richard Schmalensee. p. cm. Includes bibliographical references and index. ISBN 978-1-933115-90-0 (hardback : alk. paper) 1. Electric power production. 2. Electric power productionEnvironmental aspects. 3. Electric power productionEconomic aspects. 4. Renewable energy sources. I. Moselle, Boaz. II. Padilla, Jorge 1983 III. Schmalensee, Richard. TK1001.H37 2010 333.7932--dc22 2010017827 A catalogue record for this book is available from the British Library At Earthscan we strive to minimize our environmental impacts and carbon footprint through reducing waste, recycling and offsetting our CO2 emissions, including those created through publication of this book. For more details of our environmental policy, see www.earthscan.co.uk. Printed and bound in the UK by MPG Books The paper used is FSC certified
The findings, interpretations, and conclusions offered in this publication are those of the authors. They do not necessarily represent the views of Resources for the Future, its directors, or its officers.
OfficersLawrence H. Linden, Chair Steven W. Percy, Vice Chair Philip R. Sharp, President Edward F. Hand, Vice PresidentFinance and Administration Laurel L. Harvey, Vice PresidentDevelopment and Corporate Secretary Mark A. Cohen, Vice PresidentResearch
About the Contributors List of Figures and Tables Acknowledgments Acronyms and Abbreviations Foreword Gnther Oettinger Chapter 1. Toward a Low-Carbon Future in Electricity? Boaz Moselle, Jorge Padilla, and Richard Schmalensee Part I. Technology Chapter 2. Renewable Energy Technologies for Electricity Generation Godfrey Boyle Part II. Renewables, Climate Change, and Energy Policy Chapter 3. Renewables Forecasts in a Low-Carbon World: A Brief Overview Erin T. Mansur Chapter 4. Renewable Generation and Security of Supply Boaz Moselle Chapter 5. Market Failure and the Structure of Externalities Kenneth Gillingham and James Sweeney Chapter 6. Renewable Energy, Energy Efficiency, and Emissions Trading Jos Goldemberg
5 7
31 33 51 69 93
Part III. Renewable Generation and Electric Power Markets Chapter 7. Electricity Wholesale Market Design in a Low-Carbon Future William W. Hogan Chapter 8. Energy Regulation in a Low-Carbon World Richard Green Chapter 9. Building Blocks: Investment in Renewable and Nonrenewable Technologies James Bushnell Chapter 10. Developing a Supergrid Christian von Hirschhausen Part IV. National Experiences Chapter 11. Renewable Electricity Generation in the United States Richard Schmalensee Chapter 12. The European Unions Policy on the Development of Renewable Energy Christopher Jones Chapter 13. UK Renewable Energy Policy since Privatization Michael G. Pollitt Chapter 14. Experience with Renewable Energy Policy in Germany Hannes Weigt and Florian Leuthold Chapter 15. Renewable Electricity Support: The Spanish Experience Luis Agosti and Jorge Padilla Conclusions: Whither Renewable Generation? Boaz Moselle, Jorge Padilla, and Richard Schmalenesee Index
Luis Agosti is a senior consultant at LECG, an economic and financial consulting firm, where he provides research and analysis in the field of applied microeconomics, including the economics of competition, regulation, and finance. He also belongs to the Energy Practice. During his years in consultancy, he has specialized in the electricity and gas sectors. His work has been focused in the analysis of regulation in the electricity and natural gas sectors, with special emphasis in the design of the wholesale power market in Spain. Godfrey Boyle is a professor of renewable energy and director of the Energy and Environment Research Unit in the Faculty of Mathematics, Computing, and Technology at the UK Open University. He is also a visiting professor at The Energy and Resources Institute University in New Delhi. He is coauthor and editor of Renewable Energy: Power for a Sustainable Future (2004) and Renewable Electricity and the Grid (Earthscan 2009). James Bushnell is an associate professor and the Cargill Chair in Energy Economics in the Department of Economics at Iowa State University, the director of the universitys Biobased Industry Center, and a research associate of the National Bureau of Economic Research. He has served as a member of the Market Monitoring Committee of the California Power Exchange and is currently a member of the Market Surveillance Committee of the California Independent System Operator. He has testified on regulatory and competition policy issues before numerous state and federal regulatory and legislative institutions and consulted on energy issues throughout the United States and internationally. He has written extensively on the regulation, organization, and competitiveness of energy markets. Kenneth Gillingham is an environmental and energy economist who worked for several years in Washington, DC, at the White House Council of Economic Advisers, Resources for the Future, and the Joint Global Change Research Institute. Following this, he was a Fulbright Fellow at the University of Auckland in New Zealand and is currently at Stanford University. He has published journal articles on topics such as solar energy policy, biofuels policy, energy efficiency, and transportation policy. Jos Goldemberg, a physicist, is a professor emeritus of the University of So Paulo, Brazil, of which he was the rector between 1986 and 1990. He has served as president of the Energy Company of the State of So Paulo and Brazils secretary of state for science and technology. He chaired the World Energy
Assessment of the United Nations Development Programme. Time magazine honored him as one of its Heroes of the Environment, and the Asahi Glass Foundation of Japan awarded him its 2008 Blue Planet Prize. Richard Green is the director of the Institute for Energy Research and Policy and a professor of energy economics in the Department of Economics at the University of Birmingham. He has held visiting positions at the Office of Electricity Regulation, University of California Energy Institute, and Massachusetts Institute of Technology. He is also an author or coauthor of numerous articles published in such journals as the Journal of Political Economy, Journal of Industrial Economics, Oxford Economic Papers, and Fiscal Studies. Christian von Hirschhausen is a full professor of economic and infrastructure policy at Berlin University of Technology and a research professor at DIW Berlin. Before that, he held the chair of energy economics and public sector management at Dresden University of Technology. He is the scientific director of several programs that cover a wide range of research in energy and related fields. His recent research on contracts and institutions in the utilities was published in The Energy Journal. Additional research focuses on the European economy, energy policy, and economic policy in transitional countries. William W. Hogan is the Raymond Plank Professor of Global Energy Policy at the John F. Kennedy School of Government, Harvard University, and the research director of the Harvard Electricity Policy Group, which is examining alternative strategies for a more competitive electricity market. He has been a member of the faculty of Stanford University, where he founded the Energy Modeling Forum, and is a past president of the International Association for Energy Economics. His current research focuses on major energy industry restructuring, network pricing and access issues, market design, and energy policy in nations worldwide. Christopher Jones was the director of New and Renewable Sources of Energy, Energy Efficiency and Innovation at the Directorate-General for Energy and Transport, European Commission, until January 15, 2010, when he became the head of the cabinet of Commissioner Andris Piebalgs, responsible for EU development policy. He is also a visiting professor of law at the College of Europe in Bruges, Belgium. He has authored or coauthored and edited several books and numerous articles on EU energy law and competition policy. He is also a member of the advisory or editorial boards of a number of academic journals. Florian Leuthold is a research associate at the chair of energy economics at Dresden University of Technology. His current research topics include renewable energy, environmental policies, and electricity market modeling. He coauthored several recent journal articles published in Energy Economics, Energy Policy, and Utilities Policy. Erin T. Mansur is an associate professor in the Department of Economics at Dartmouth College and a faculty research fellow at the National Bureau of Economic Research. He has coauthored numerous articles published in economics journals, including the American Economic Review, Review of Economics and Statistics, Journal of Law and Economics, Journal of Environmental Economics and Management, Journal of Industrial Economics, and Journal of Economics and Management Strategy. Boaz Moselle is an economist specializing in energy markets and a director of the economic and financial consulting firm LECG. He was previously a managing director at the UK energy regulator Ofgem. He has served as a consultant to governments and corporations in many countries on a wide range of energy policy, competition, and regulatory matters; testified before UK parliamentary commit-
tees on renewable generation, security of supply, and the structure of the British power market; and participated in a group advising the European Commission on energy, competitiveness, and environmental issues. He is a member of the Advisory Board of the Centre for Economic Learning and Social Evolution (ELSE) at University College London. He has written books and articles for scholarly journals, including the Electricity Journal; Journal of Risk and Uncertainty; Journal of Law, Economics and Organization; Journal of Economic Theory; Northwestern Journal of International Law & Business; Economic History Review; Peace Economics, Peace Science and Public Policy; and Proceedings of the Edinburgh Mathematical Society. Jorge Padilla is the European chief executive officer of LECG and advises companies on a variety of competition policy and regulatory issues, covering a wide range of industries, including the electricity and natural gas sectors. He is a research fellow of the Centre for Economic Policy Research and the Centro de Estudios Monetarios y Financieros and is or has been a member of the editorial boards of Competition Policy International, Review of Economic Studies, Spanish Economic Review, and Investigaciones Econmicas. He coauthored The Law and Economics of Article 82 EC (2006) and has written numerous articles on competition policy and industrial organization, published in such journals as the Antitrust Bulletin, Economic Journal, European Economic Review, International Journal of Industrial Organization, Journal of Economics and Management Strategy, Journal of Economic Theory, RAND Journal of Economics, Review of Financial Studies, and World Competition. Michael G. Pollitt is an assistant director of the Economic and Social Research Council (ESRC) Electricity Policy Research Group, as well as a reader in business economics at Judge Business School, University of Cambridge, the director of studies in management and economics, and a fellow of Sidney Sussex College. He is currently an external economic adviser to Ofgem, the UK energy regulator, and has also advised the ESRC, Norwegian Research Council, DTI, World Bank, and European Commission. Additionally, he has consulted for National Grid, AWG, Eneco, Nuon, Roche, and TenneT. Richard Schmalensee is the John C. Head III Dean Emeritus and the Howard W. Johnson Professor of Economics and Management of the Sloan School of Management at the Massachusetts Institute of Technology (MIT) and director of the MIT Center for Energy and Environmental Policy Research. He has served as a member of the Presidents Council of Economic Advisers and serves on the National Commission on Energy Policy and the National Academy of Sciences Committee on Americas Climate Choices. He is an expert on regulation, antitrust, and energy and environmental policy. James Sweeney is a director of the Precourt Energy Efficiency Center and a professor of management science and engineering at Stanford University. His professional activities focus on economic policy and analysis, particularly in energy, natural resources, and the environment. He has also advised in a number of energy litigations in natural gas, oil, and electricity industries in the United States and New Zealand. He was one of the editors of the three-volume Handbook of Natural Resource and Energy Economics (1985 1993). Hannes Weigt is a research associate at the chair of energy economics at Dresden University of Technology. His current research topics include renewable energy and environmental policies. He is also a coauthor of numerous journal articles published in Energy Economics, Energy Policy, Applied Energy, and the Electricity Journal.
Figures2.1 2.2 2.3 2.4 Evolution of price of PV modules and systems in selected IEA reporting countries, 19972007, allowing for the effects of inflation Projected convergence of utility prices and PV generation costs Incremental investment costs of a 20% wind contribution to U.S. electricity demand in 2030 Annual offshore wind capacity buildup in the United Kingdom, Sweden, Denmark, the Netherlands, and Germany, plus total cumulative installed capacity, 19892009 Marginal resource costs for different levels of renewable generation by sector in the United Kingdom in 2020 Day-ahead forecast of wind power output compared with monitored output, for one month in Germany Wind power worldwide capacity and annual growth rate, 19972008 Share of renewable electricity by EU country in 2007 and targets for 2010 Expected electricity generation and capacity from nonhydropower renewable sources with and without ARRA, 20062030 World coal reserves World uranium resources World gas reserves: top 10 countries Capacity credit values Economies of scale Illustrative incremental benefits from additional cumulative installations: LBD World primary energy supply shares of 516 exajoules World energy consumption, 18502000 World fossil-fuel CO2 emissions and growth rate, 19902005 15 15 18
19 22 26 35 37 43 54 54 55 61 76 87 93 94 95
2.5 2.6 3.1 3.2 3.3 4.1 4.2 4.3 4.4 5.1 5.2 6.1 6.2 6.3
6.4 6.5 6.6 6.7 6.8 6.9 6.10 6.11 6.12 6.13 6.14 6.15 6.16 6.17 6.18 8.1 9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8 9.9 9.10 10.1 10.2 10.3 10.4 10.5 10.6 10.7 10.8 10.9 10.10 11.1 11.2 11.3 11.4 11.5 11.6
Contributions of greenhouse gases to global warming in 2006 Annual growth rates of renewable energy capacity, 19982008 Contribution of renewables to the total energy supply Projection of renewable energy production up to 2030 Energy produced from distributed generation in European countries Evolution of energy intensity in a number of countries and regions, 19902005 Energy savings in the OECD countries, 19731998 Carbon intensity: CO2 emissions per GDP (PPP), 19702005 The evolution of industrial subsector energy intensities, 19741998 Carbon price, January 2006November 2007 Cap and ETS emissions in the European Union Distribution of CDM projects by type Carbon market estimates, 20022008 Investments in green stimuli Sector breakdown of global green stimuli Models of regulation The four WECC subregions CEMS load and wind production for August CEMS load and wind production for December Annual distribution of CEMS load net of wind Energy market prices for August Energy market prices for December Equilibrium capacity for energy-only market Highest 150 prices CA Highest 150 prices RMPP Equilibrium capacity investment with $25/ton CO2 Solar radiation in the United States JCSP08 wind energy scenario conceptual transmission overlay Integrating 200,000 MW of renewable energy in the U.S. grid (exemplified for wind power) Airtricitys European offshore supergrid proposal The North Sea wind power transmission ring The Desertec HVDC overlay network (physical map) Installed capacities for the BAU and TD scenarios, 20102050 HVDC grid in 2050 (TD scenario) Electricity price changes with reinforced interconnectors to Scandinavia in 2050 Required generation and transmission investment for the EEA-MENA supergrid Nonhydro renewable energy consumption as a percentage of total U.S. energy consumption, 19782008 Share of nonhydro renewable electricity in total generation, 19892006 U.S. electricity generation from nonhydro renewable energy, 19902007 Federal expenditure on R&D in renewable technologies, 19782007 Wind electricity capacity addition, 19942007 Nonhydro renewables in California, 19902007
96 97 98 98 99 100 101 101 102 104 105 107 107 108 108 140 164 166 167 167 169 169 170 172 172 175 184 186 188 188 189 190 192 195 196 196 210 211 211 213 216 221
11.7 11.8 14.1 14.2 14.3 14.4 14.5 14.6 14.7 14.8 14.9 15.1 15.2 15.3 15.4 15.5 15.6 15.7 15.8
Electricity generation by nonhydro renewable sources in California, 19902007 Electricity generation by nonhydro renewable sources in Texas, 19902007 Primary energy consumption in Germany, 2007 Energy R&D funding in Germany, 19742007 Wind turbine installations, 19902008 Electricity generation from RES, 19902007 Share of RES from total power generation in 2030 for different future scenarios Annual average wind output, 20002004, and expected revenues for onshore wind Estimated differential costs and avoided external costs as a result of German RES production Installed RES generation capacities, 19902007 Cost reduction for RES generation Effectiveness and efficiency of support schemes for onshore wind generation in Europe in 2006 Renewables mix in EU countries, 2007 Evolution of demand and share of the SR Distribution of SR production between fixed price and market options Renewable energy growth till 2020 and contribution to final energy consumption Effect of renewable energy promotion in the wholesale generation market Actual versus forecast generation from wind, November 2, 2008 Actual versus forecast generation from wind, January 2324, 2008
222 223 284 285 285 287 288 290 292 303 303 310 311 312 313 315 318 319 320
Tables2.1 2.2 2.3 2.4 2.5 3.1 3.2 3.3 3.4 3.5 3.6 4.1 4.2 5.1 Annual PV capacity installed/predicted: market results versus Solar Generation (SG) scenario predictions since 2001 Projected solar PV electricity output and detailed associated projections for 2030 in the SG V scenario Projected technically available, environmentally constrained, and economically competitive potential for wind energy in 27 EU countries in 2020 and 2030 Prices paid under proposed UK feed-in tariffs for electricity from 2010 and annual digression rates from 2010 onward Summary of economic characteristics of renewable electricity generation technologies Total installed capacity of wind energy in 2007 and 2008 Capacity of renewable resources by type and country in 2008 IEA world energy outlook predictions, showing generation by energy source Electricity generation by fuel type for the three WETO scenarios EPA analysis of the WaxmanMarkey Bill, showing generation by energy source, 2020 and 2025 A summary of model predictions for 2030 EU import dependence, 2005 Energy consumption and import dependence by 2020 Some potential policy instruments 16 16 18 23 24 35 36 40 41 44 46 52 58 80
5.2 6.1 6.2 6.3 6.4 6.5 6.6 6.7 8.1 9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8 9.9 9.10 10.1 10.2 10.3 10.4 10.5 11.1 11.2 11.3 11.4 11.5 11.6 11.7 12.1 13.1 13.2 13.3 13.4 13.5 13.6 13.7 13.8 13.9
Sources of market failure and some illustrative potential policy instruments Electricity, heat, and transport fuel from renewable sources Primary energy production from new renewables Additional rate of investment in power generation technologies needed in the electricity sector every year until 2050 The carbon market: Volumes and values for emissions trading, 20062007 Distribution of JI projects by type CDM projects grouped by type Volumes and values for CDM and JI, 20062007 EU electricity generation from renewable sources Summary statistics of demand Aggregate generation levels Thermal generation costs from EIA Thermal generation costs used in simulations Equilibrium results for energy-only market Capacity payments resulting from $1,000 price cap Investment levels with a capacity market Summary of the revenues of the hypothetical average wind turbine Wind revenues with carbon price at $25/ton Impact of intermittency on average thermal costs Characteristics of high-voltage transmission technologies Studies for large-scale integration of renewable energy sources JCSP08 main scenario assumptions and results Exogenous and endogenous parameters of the model Number of HVDC transmission cables and CSP generation capacity in MENA (TD scenario) Actual and potential NHR generation in the United States Leading NHR generation states Subsidies and support to electricity production by technology, 2007 State renewable portfolio standards (RPSs) State voluntary renewables goals Other state policies to promote NHR generation Leading wind generation states Member state targets Estimates of the likely economic potentials for different renewable technologies in the United Kingdom Examples of estimated costs of technologies for the United Kingdom in 2005 Costs of electricity sector decarbonization to 2020 RO targets and delivery against targets Banding of ROCs from April 1, 2009 Financial support for renewables in the United Kingdom (nominal) Support for renewable energy in 20072009 Renewable electricity generation in the United Kingdom, 19902008 Differences among leading wind countries in Europe
81 96 97 99 100 106 106 106 143 165 166 168 168 170 171 173 174 175 176 183 184 186 192 194 212 212 217 218 219 220 225 240 255 256 257 260 261 262 263 264 270
14.1 Gross employment figures of the RES sector in Germany 14.2 Impact of emissions trading and RES support on the German economy 14.3 Feed-in tariffs in Germany to EEG 2008 and EEG 2004 15.1 Generation mix in Spain, December 2008 15.2 Evolution of installed renewable and other SR capacity 15.3 Evolution of incentives for renewable generation 15.4 Capacity targets and current deployment status, September 2009 15.5 Implicit subsidies for the SR, 2008
This book would not have been possible without the support of Luis Agosti, David Black, and Dhiren Patki. Special thanks go to Anne Layne-Farrar. They are owed a substantial debt of gratitude. Thanks also go to Don Reisman at Resources for the Future, who published the English version of the book, and Juan Jose Pons from Marcial Pons, in charge of the Spanish edition of the book, for their support. The views and opinions expressed in the chapters of this book are the sole responsibility of their authors.
AC ACER AP ARRA a-Si AWEA AZNM BAU BEITC BMU BoS BPA Btu BWEA CA CAES CAISO CBA CCC CCGT CCS CDM CdTe CECRE CEMS CER CERT
alternating current Agency for the Cooperation of Energy Regulators average participation American Recovery and Reinvestment Act of 2009 amorphous silicon American Wind Energy Association southwest (ArizonaNew Mexico) region (of WECC) business-as-usual Business Energy Investment Tax Credit Federal Ministry for the Environment, Nature Conservation and Nuclear Safety of Germany balance-of-system Bonneville Power Administration British thermal unit British Wind Energy Association California region (of WECC) compressed-air energy storage California ISO costbenefit analysis; costbenefit analyses Committee on Climate Change combined cycle gas turbine carbon capture and storage Clean Development Mechanism cadmium telluride Centre for the Control of the Special Regime continuous emissions monitoring system certified emission reduction Carbon Emissions Reduction Target
CH4 CHP CIGS CIS CO2e CPUC CRW CSC CSP DC DLR DNA DNI DOE EEA EEA-MENA EEG EEX EIA EJ ENTSO-E EPA EPIA ERU ETS EUMENA EV EVA FACTS FERC FIT FTR GaAs GDP GHG Gt GW GWEC GWh GWP GWth HFCs HR HVAC
methane combined heat and power copper indium gallium diselenide copper indium diselenide carbon dioxide equivalent California Public Utilities Commission combustible renewables and waste current source converter concentrated solar power direct current German Aerospace Center designated national authority direct normal irradiance (U.S.) Department of Energy European Environment Agency European Economic AssociationMiddle East North Africa Erneuerbare Energien Gesetz (Renewable Energy Source Act) European Energy Exchange (U.S.) Energy Information Administration exajoule European Network of Transmission System Operators for Electricity (U.S.) Environmental Protection Agency European Photovoltaic Industries Association emission reduction unit (EU) Emission Trading System Europe, the Middle East, and North Africa electric vehicle Energy Ventures Analysis Flexible AC Transmission Systems Federal Energy Regulatory Commission feed-in tariff financial transmission right gallium arsenide gross domestic product greenhouse gas gigatons gigawatts Global Wind Energy Council gigawatt-hours global warming potential gigawatts-thermal hydrofluorocarbons heat rate high-voltage alternating current
HVDC IEA IER IET IGBT IGCC IHSGI IPCC IPP ISO ITC J JCSP08 JI kgCO2/kWh kWh kWy LBD LMP LNG LPG L-RES m/s MAP MAPP mcm/day MENA MISO MSP Mtoe MW MWh MWhRES N2O NEMS NERC NETA NFFO NFPA NHR NIMBY NOx NPV NREL
high-voltage direct current International Energy Agency Institute of Energy Economics and the Rational Use of Energy international emissions trading insulated gate bipolar transistors integrated gasification combined cycle IHS Global Insight Intergovernmental Panel on Climate Change independent power producer independent system operator investment tax credit joule Joint Coordinated System Plan 2008 joint implementation kilograms of CO2 per kilowatt-hour kilowatt-hour kilowatt-year learning by doing locational marginal pricing liquefied natural gas liquefied petroleum gas large-scale renewable energy sources meters per second Market Incentive Program Mid-Continent Area Power Pool million cubic meters per day Middle East and North African region Midwest ISO Mediterranean Solar Plan million tons of oil equivalent megawatts megawatt-hour megawatt-hour of renewable energy nitrous oxide National Energy Modeling System North American Electric Reliability Council New Electricity Trading Arrangements Non-Fossil Fuel Obligation Non-Fossil Purchasing Agency nonhydro renewable not-in-my-backyard nitrogen oxides net present value National Renewable Energy Laboratory
NWPP NYISO O&M OECD OGT OMA OR ORC OTC OTEC OWC PER PFCs PFER PJM POLES ppm PTC PURPA PV PVPS R&D RD RD&D RDL REC REE REPI REPTC RES RGGI RHI RMPP RO ROC RPS RRETC RTO RTP SEGS SERC SF6 SG
Northwest Power Pool region (of WECC) New York Independent System Operator operation and maintenance Organisation for Economic Co-operation and Development open-cycle gas turbines Office for Metropolitan Architecture Rgimen Ordinario (Ordinary Regime) organic Rankine cycle over-the-counter ocean thermal energy conversion oscillating water column Plan de Energas Renovables (Renewable Energy Plan) perfluorocarbons Plan de Fomento de las Energas Renovables (Plan to Promote Renewable Energy) PennsylvaniaNew JerseyMaryland Interconnection Prospective Outlook on Long-term Energy Systems parts per million production tax credit; parabolic trough concentrator Public Utilities Regulatory Policies Act (of 1978) photovoltaic Photovoltaic Power Systems research and development Royal Decree research, development, and demonstration Royal Decree-Law renewable energy certificate; renewable energy credit Red Elctrica de Espaa Renewable Energy Production Incentive Renewable Electricity Production Tax Credit renewable electricity standard Regional Greenhouse Gas Initiative Renewable Heat Incentive Rocky Mountain Power Pool region (of WECC) Renewables Obligation Renewables Obligation Certificate renewable portfolio standard Residential Renewable Energy Tax Credit regional transmission organization real-time pricing Solar Energy Generating Systems Southeastern Electric Reliability Council sulfur hexafluoride Solar Generation
SGIP SHPPs SO SO2 SOx SPP SR StrEG tcf tcm tCO2 TD TEAM TGC TOU TREC TSO TVA TWh UCTE UNFCCC VSC WECC WECS WEM WETO WTO
Self Generation Incentive Program small hydroelectric power plants system operator sulfur dioxide sulfur oxides Southwest Power Pool Rgimen Especial (Special Regime) Stromeinspeisegesetz (Electricity Feed-In Act) trillion cubic feet trillion cubic meters metric ton of CO2 technological development transmission expansion assessment methodology tradable green certificate time-of-use Trans-Mediterranean Renewable Energy Cooperation transmission system operator Tennessee Valley Authority terawatt-hours Union for the Co-ordination of Transmission of Electricity United Nations Framework Convention on Climate Change voltage source converter Western Electricity Coordinating Council wind energy conversion system World Energy Model World Energy Technology Outlook2050 World Trade Organization
Energy is the foundation stone of prosperity, security, and peace. Yet the European Unions economic dependence on fossil fuels, of which the lions share is imported, is exposing the EU to threats from climate change, global geopolitics, and resource competition from rising economies. These risks could undermine our industrial and social model and ultimately European stability itself. In response to these challenges, the 27 member states of the European Union unanimously agreed in 2007 to binding targets to change the basis of Europes energy supply. By 2020, the EU must reduce its greenhouse gas emissions by 20%and by 30% when the conditions are rightand increase the share of renewable energy in energy demand to 20%. Energy efficiency must improve by one-fifth. These targets are at the heart of the European Commissions broad economic strategy for the year 2020, known as Europe 2020. Meeting these targets not only is essential for the climate change challenge, but also will dramatically improve security of supply by making the EU economy more efficient, decoupling growth from resource use, and creating more than 1.5 million extra jobs. The EU economy will be strengthened, with savings of at least 60 billion ($81.6 billion) in decreased oil and gas imports, which can be invested in the domestic economy. To do this, we mustwithin just a few yearsgenerate more than one-third of our power from renewable sources of electricity. A significant part of our heating and transport must be based on renewable fuels. It is not enough to tinker around the edges. We need huge, practical, and concrete initiatives involving society as a whole. And we need to persuade our international partners to follow the same track. The benefits are easy to see: lower imports, new jobs, cleaner air, more stable international energy markets and prices, lower energy bills, greater consumer empowerment. Renewable energy offers solutions to many of our problemssecurity, economic, and environmental. The switch to an economy with a large share of renewable and low-carbon sources is the only way to ensure sustainable economic growth that brings benefits to all parts of society. Never before have businesses and consumers had to face such a daunting task. New energy sources call for new networks to bring them to customers. Intermittent generation requires new approaches to balancing demand and supply. New and renewable technologies must become much more efficient and commercially attractive. At the same time, we have to deal with economic uncertainty and growing global competitiveness.
It is unprecedented, even in a buoyant economy, for markets alone to make the types of investments and projects our system needs. The costbenefit analysis for renewable energy is not simple for many investors. In addition, todays credit crunch risks undermining many advances industry has made in recent years. We also have to face up to the adverse impact on investment and investment planning that could ensue from the weak Copenhagen climate agreement. Moving to a low-carbon economy calls for investments in the order of billions of euros. But climate and security risks also have a cost, which could be several times higher. There is a growing consensus that the investments we make today will pay for themselves many times over in the future, in terms of cheaper energy, greater energy security, new businesses and markets, a cleaner environment, and climate change mitigation. We cannot afford to miss out on the huge technology and employment opportunities and geopolitical security of renewable power. Our 2020 targets are a stepping-stone toward the EU vision for a decarbonized power supply and transport sector by 2050. Let us not forget that the system we have in place today is largely a result of technologies and decisions from 40 years ago, or even more, so our 2050 timescale is not as ambitious as it sounds. It simply reflects the reality of how energy systems develop. To get there, the commission is pursuing a number of interrelated priorities: to implement fully and effectively an internal market in Europe; to enforce renewables and greenhouse gas emission targets and strengthen the legislative framework to give greater certainty to investors; to create a solid framework for new network infrastructure investments; to promote greater collaboration and cohesion in research, including industry-led initiatives; to boost energy efficiency, notably in buildings, appliances, and transport; and develop partnerships with other countries, both producers and other consuming nations. It is vital to inform, involve, educate, and motivate the widest possible public. Every citizen can play a part in the low-carbon revolution. This book is therefore both timely and important. It is also highly educational. It can help us better understand the opportunities and challenges renewable energy creates and learn from the experience of others. In this way, we will all, whether in politics, business, education, or at home, be better equipped to make, with confidence, the vital decisions that will lead to the energy system we seek for the futurea system that is at once secure, sustainable, and economically robust. Gnther Oettinger European Union Energy Commissioner
1Toward a Low-Carbon Future in Electricity?Boaz Moselle, Jorge Padilla, and Richard Schmalenseeide consensus exists that the worlds energy system requires a significant transformation in order to address the issue of climate change. In particular, the electric power system needs to switch from its carbon-intensive present, where most electricity is produced using gas and coal, to a low-carbon future where electricity is produced using some combination of renewable sources, such as hydro, wind, biomass, tidal, and solar energy, along with nuclear power and carbon capture and storage (CCS) technology, which would allow the continued use of fossil fuels, with the carbon dioxide (CO2) emitted from combustion being stored underground rather than escaping into the atmosphere. A radical change is needed because at present, power generation produces a major part of the worlds anthropogenic (or human-caused) carbon dioxide emissions, reflecting its reliance on fossil fuels, and moreover because decarbonization is likely to require increasing electrification of other sectors of the economyin particular, a switch to electric or plug-in hybrid vehicles. Of the options for producing electric power without significant greenhouse gas emissions, renewable generation technologies such as hydroelectricity, wind power, solar power, and biomass are generally more attractive to policymakers than either nuclear power, which brings with it con-
cerns about safety and waste storage as well as difficult political challenges, or CCS, a new and as yet unproven technology. Renewables are also perceived to have other important attractions: increased national self-reliance contributing to security of energy supply, industrial policy goals for countries that see themselves as leaders in developing new green technologies, and domestic job creation. This book therefore addresses a set of key questions concerning the role of renewable electricity generation in addressing climate change questions that are central to the current concerns of policymakers, the electric power industry, and economists who study energy and environmental issues:
At the highest level, is it right to focus on renewable power as a primary tool for reducing greenhouse gas emissions? Although environmentalists and many politicians are strong supporters of renewable energy, many economists question whether it should be singled out for support rather than being encouraged through a broad approach to reducing carbon emissions (e.g., via a tax on CO2 emissions). If renewable energy is to be given specific support, what form should that take? What is
the right balance between support for research and development and for deployment of renewable generation? Should all renewable technologies receive equal levels of support? If not, how should the system choose among technologies? What is the right mechanism for supporting renewable generation: straight financial payments, tax breaks, guarantees of prices at levels high enough to support investment, or a marketbased method involving the trading of green certificates? At a more technical levelbut of great importancewhat are the implications for power markets of the widespread adoption of renewable generation? Is this trend compatible with the shift (or aspiration) toward liberalized, competitive markets that is seen in the European Union and much of the United States? What are the implications for system reliability of the use of intermittent resources, such as wind and sunshine, for a large part of generation capacity? Will markets provide needed backup generation, or is regulatory intervention required?
The various chapters in this book provide objective analysis of these questions, drawing on economic theory and evidence, including the experiences of U.S. states and EU member states that have devoted considerable resources to the promotion of renewable generation over the past decade or more. The focus is on the application of rigorous economic analysis to derive insights that have practical implications for policy in this area. The book is the result of a cooperative effort among a set of authors that includes both academics and policymakers, with a range of specialties covering renewable technology, environmental and natural resource economics, energy economics and industrial organization, and with specific knowledge of the energy sector, electric power markets, and renewables policy in Europe, the United States, and globally. Although many books currently exist on climate change-related topics, most of these address different issues than this one. Many are focused on the scientific issues around global climate change
and its implications in such areas as agriculture, migration, or public health (see, e.g., Kruger 2006; Tester et al. 2005). Others focus on policy responses to climate change, often from a political science or international relations perspective (see, e.g., Dodds et al. 2009; Giddens 2009; Kalicki and Goldwyn 2005; Mitchell 2010; Stern 2009). Although a number of important texts have been written on the economics of global climate (e.g., Helm and Hepburn 2009), only a few address the role of renewable energy as part of climate change mitigation and the challenges that renewable generation poses for electricity markets (e.g., Grubb, Jamasb and Pollitt 2008). This book differs from others on renewables in breadth and depth. It deals with technology issues, wide policy questions (such as the impact of renewables support on the climate change and security of supply goals of most governments), and practical implementation issues (e.g., the implications for system design, market performance, and policy design of massive deployment of renewable energy). It also describes the renewables policies and experiences of the United States, the European Union, the United Kingdom, Germany, and Spain and examines the lessons that can be extracted from them. The book is organized into four parts. In Part I, Technology, Godfrey Boyle reviews in Chapter 2 each of the technologies covered in the book from both an economic and an engineering viewpoint, considering the impact of each on system operation, as well as its contribution to sustainability and economic efficiency. Part II, Renewables, Climate Change, and Energy Policy, focuses on the high-level energy policy concerns that underlie renewables policy. In Chapter 3, Erin Mansur discusses how widespread concerns about climate change and policymakers and voters preferences will affect the development of renewable energy use. Boaz Moselle in Chapter 4 looks at the other main driver of renewables policy, the desire to develop indigenous sources of energy to meet security of supply concerns. He analyzes the main security of supply issues that drive the European debate, notably the question of reliance on Russian gas imports. Next, in Chapter 5, Kenneth Gillingham
and James Sweeney delve into the economic motivation for renewable energy policies by highlighting the classes of relevant market failures. Key policy instruments are evaluated in the context of different market structures, with the goal of informing future policies in the renewable energy sector. Finally, Jos Goldemberg provides in Chapter 6 a broad economic analysis of the relationships among four important instruments that have become common in renewable energy policy: subsidies to renewable energy, emissions trading, the promotion of energy efficiency, and the Clean Development Mechanism developed under the Kyoto Protocol. Part III, Renewable Generation and Electric Power Markets, focuses one level down, looking at the implications of renewable generation for electric power markets. In Chapter 7, William Hogan discusses the difficulties associated with deploying renewable electricity in large-scale wholesale electricity markets. He considers such issues as system design, transmission and distribution, and the development of smart grids, with special emphasis placed on the problems of integrating wind power into wholesale market design. Chapter 8, by Richard Green, considers the regulatory issues that will arise from the desire to integrate large-scale renewable energy deployment into existing electricity markets and discusses how these issues can be resolved. James Bushnell in Chapter 9 studies the intersection of two important trends: the restructuring of electricity markets and the growth of environmental regulation. He provides an empirical study that elucidates how the increasing penetration of intermittent renewable generation will alter the economics of investment in conventional thermal generation. Finally, in Chapter 10, Christian von Hirschhausen addresses the development of a supergrid, which is currently being debated in the context of harnessing renewable energy. Using one of the more advanced concepts for a supergrid in the European/North African region, the discussion also highlights the practical obstacles in the way of the realization of such a project. Part IV, National Experiences, offers insights from the experiences of various countries, with individual chapters on the United States,
the European Union, the United Kingdom, Germany, and Spain. The aim here is to capture lessons both from the different policies and policy instruments that have been applied and from differences in the policy concerns and debates across the various countries. In Chapter 11, Richard Schmalensee analyzes the development of renewable energy policies and deployment in the United States. He compares and contrasts the growth of renewable electricity deployment in Texas and California, highlighting the ability of well-tailored policy to advance the growth of renewable energy use, and discusses the difficulties of integrating wind power into the existing market framework. Christopher Jones in Chapter 12 provides an in-depth analysis of the emergence of the European Unions renewables policy, in the context of its wider climate change and energy security concerns. He describes the policy tools adopted and analyzes some of the key issues arising, such as the relationship with the EU Emissions Trading Scheme and the role of trading within the renewables promotion framework. The remaining case studies concern three EU member states. Michael Pollitt in Chapter 13 examines the United Kingdoms renewable energy policy in the context of its overall decarbonization and energy policies. He explores the shortcomings of the UK renewables policy to date and suggests policy changes that would better suit the countrys institutional and resource endowments. In Chapter 14, Hannes Weigt and Florian Leuthold discuss the development of renewable energy policy and deployment in Germany. In particular, they examine the implications of current policy for market design and future development, with a particular focus on the growing share of wind energy. Finally, Chapter 15, by Luis Agosti and Jorge Padilla, analyzes the regulatory schemes and market structure that have made possible the rapid growth of the Spanish renewables sector in recent years, as well as the consequences of this growth for the Spanish gas and power markets. The chapter focuses in particular on the impact of the large-scale deployment of wind generation on system operation and balancing, wholesale prices, competition, and investment incentives.
It is now widely accepted that a low-carbon world is technically feasible, and that renewables are likely to have a significant long-term role to play in the future energy system. The relevant question today is not whether renewables should be supported, but rather how to harness these technologies so that they can effectively and efficiently contribute to the ultimate goal of mitigating harmful climate change.
ReferencesDodds, Felix, Andrew Higham and Richard Sherman. Foreword by Achim Steiner. 2009. Climate Change and Energy Insecurity. London: Earthscan. Giddens, Anthony. 2009. The Politics of Climate Change. Cambridge, UK: Polity Press.
Grubb, Michael, Tooraj Jamasb, and Michael Pollitt, eds. 2008. Delivering a Low Carbon Electricity System. Cambridge, UK: Cambridge University Press. Helm, Dieter, and Cameron Hepburn, eds. 2009. The Economics and Politics of Climate Change. Oxford: Oxford University Press. Kalicki, Jan H., and David L. Goldwyn, eds. 2005. Energy & Security. Baltimore: Johns Hopkins University Press. Kruger, Paul. 2006. Alternative Energy Resources: The Quest for Sustainable Energy. Hoboken, NJ: WileyBlackwell. Mitchell, Catherine. 2010. The Political Economy of Sustainable Energy. Basingstoke, UK: Palgrave Macmillan. Stern, Nicholas. 2009. A Blueprint for a Safer Planet. London: The Bodley Head Ltd. Tester, Jefferson W., Elisabeth M. Drake, Michael J. Driscoll, Michael W. Golay and William A. Peters 2005. Sustainable Energy: Choosing Among Options. Cambridge, MA: MIT Press.
Part ITechnology
2Renewable Energy Technologies for Electricity GenerationGodfrey Boyle
t is tempting to imagine that the 17th-century poet and mystic William Blake was thinking of renewable energy when, in The Marriage of Heaven and Hell, he declared that Energy is Eternal Delight (Blake 1790). Renewable energy is usually described in more prosaic terms, however. According to the International Energy Agencys definition, Renewable energy is derived from natural processes that are replenished constantly (IEA 2002). Similarly, Twidell and Weir (1986) define it as energy obtained from the continuous or repetitive currents of energy recurring in the natural environment. How, then, should renewable energy technologies be defined? The following working definition may suffice: renewable energy technologies are those technologies that enable constantly replenished renewable energy flows to be harnessed to produce power in forms useful to humanity on a sustainable basis. The sun is the source of the vast majority of the power that drives the abundant and varied sources of renewable energy that are available to humanity on the earth. The total quantity of solar power incident on our planet is approximately four orders of magnitude greater than our current rate of use of fossil and nuclear fuels. Solar radiation can be used directly, to provide heating, lighting, and hot water in buildings and
to generate electricity. The sun also powers the worlds weather systems and is thus the indirect source of hydro, wind, and wave power. It also drives the process of photosynthesis in plants and so is the energy source underlying biofuels in their various forms. Two other terms that should be defined here at the outset are energy and power. Energy is defined as the capacity to do work: that is, to move an object against a resisting force. In everyday language, the word power is often used as a synonym for energy, but this is not strictly correct. Power is defined as the rate of doing workthat is, the rate at which energy is converted from one form to another or transmitted from one place to another. The main unit of measurement of energy is the joule (J), and the main unit of measurement of power is the watt (W), which is defined as a rate of one joule per second.
Godfrey Boyle
the suns radiant energy, aided by the atmospheric greenhouse effect, that maintains the earths surface at a temperature warm enough to support human life. The sun has a surface temperature of 6,000 degrees Celsius (C), maintained by continuous nuclear fusion reactions between hydrogen atoms within its interior. This is a relatively slow process, and the sun should continue to supply power for another five billion years. The sun radiates huge quantities of energy into the surrounding space, and the tiny fraction intercepted by the earths atmosphere 150 million kilometers (km) away is nonetheless equivalent to about 15,000 times humanitys present rate of use of fossil and nuclear fuels. Even though approximately one-third of the intercepted energy is reflected away by the atmosphere before reaching the earths surface, this still means that a continuous and virtually inexhaustible flow of power, amounting to some 9,000 times our current rate of consumption of conventional fossil and nuclear fuels, is available in principle to human civilization.
Nonsolar RenewablesTwo other sources of renewable energy, tidal and geothermal energy, are not dependent on solar radiation. Tidal energy is sometimes confused with wave energy, but its origins are quite different. It arises from the gravitational pull of the moon (plus a small contribution from the sun) on the worlds oceans. The source of geothermal energy is heat from within the earth caused mainly by the decay of radioactive materials within the earths core.
tially at solar, wind, and wave power, then turning to biofuels, hydro, tidal, and geothermal energy. It then analyzes the costs and economics of renewable electricity, and finally examines the challenges involved in integrating renewable electricity sources into current and future electricity supply systems.1
at the top of the pond will remain at ambient levels, around 30C, the difference in these temperatures can be used to power an organic Rankine cycle (ORC) heat engine, which in turn can power a generator to produce electricity. An ORC engine operates on a cycle similar to that used in conventional steam turbines, except that the working fluid is not water (turned to steam) but an organic fluid, such as pentane or butane, which evaporates at lower temperatures. The vapor then drives a turbine. This cycle enables power to be produced from much lowertemperature sources than those used with steam turbines, though also with much reduced efficiency. The low efficiency means that solar ponds need to be large to generate significant quantities of electrical power. (They are perhaps more suited to low-temperature heat production.) They also operate effectively only at low latitudes because the absorbing area is horizontal. In addition, the maintenance costs of replenishing the surface water and maintaining the salt gradient are significant. Ocean Thermal Energy Conversion (OTEC) In warm tropical regions of the worlds oceans, the temperature of water at the surface can exceed 25C, whereas at depths of around 1,000 meters, the water temperature can be below 5C. This temperature differential can be used to drive a turbine employing an organic Rankine cycle heat engine, which in turn can power a generator to produce electricity, in what is called ocean thermal energy conversion (OTEC). The potential resource of solar energy stored in the surface layers of the worlds oceans and available to be tapped by OTEC systems is huge. Such systems should also be able to produce power on a continuous basis. Between the 1970s and the 1990s, several experimental plants were tested in Hawaii, and another in Japan. However, the engineering challenges involved in pumping huge quantities of water through extremely long, large-diameter pipes from the ocean depths to the surface are formidable, and considerable energy is required simply to pump the water.
The U.S. firm Lockheed Martin is currently developing plans for a new OTEC system taking advantage of modern materials and technology to generate power on a scale of approximately 100 megawatts (MW). Given adequate funding, the company foresees that a pilot 10 MW plant could be operating off Hawaii by 2013 (James 2009; Lockheed Martin n.d.). Solar Chimney The solar chimney concept involves heating large volumes of air in an extremely large greenhouse and allowing the warmed air to rise through a very tall chimney. The stream of rising air powers a wind turbine at the base of the chimney. Such systems need to be very large if they are to generate significant quantities of power. In a 50-kilowatt experimental project at Manzanares in Spain, the greenhouse-type solar collector was 240 meters in diameter and the chimney was 195 meters high. Compared with concentrating solar collectors, the energy conversion efficiency of solar chimney systems is low, and the land area they occupy is correspondingly greater. They can produce power using diffuse as well as direct solar radiation, however, and some energy storage can be provided though heating the floor of the greenhouse (see Schlaich 1995).
in turn drives an electrical generator. Relatively high temperatures (around 400C) can be attained, enabling fairly high energy conversion efficiencies (about 20%) to be achieved. The worlds longest-operating concentrating solar power plants are the nine Solar Energy Generating Systems (SEGS) established by the Luz Energy Corporation in Californias Mojave Desert between 1984 and 1990. They use parabolic trough concentrators occupying several square kilometers of land, with a combined output of 350 MW. The plants were designed to help meet Californias summer peak air-conditioning load. At night or in low-sun conditions, a natural gas boiler can be used to supply auxiliary steam to the turbines, enabling them to continue to supply power. Another, more recent, large parabolic trough concentrating solar system in the United States is the 64 MW Solar One, installed in the Nevada desert by Acciona Energy in 2007. The Israeli company Solel has signed a power purchase agreement with the California utility Pacific Gas and Electric to install some 553 MW of parabolic trough concentrating solar capacity in the Mojave Desert by 2011 (Hopwood 2009). Europes largest parabolic trough concentrating solar electric plants are the Andasol installations in southern Spain, each rated at 50 MW. The first is in commercial operation, the second entered its testing phase in 2009, and the third is due to be commissioned in 2011.They are located on the Guadix plateau in the province of Granada, an area with clear skies and very high annual solar radiation levels. Andasol 1 has 312 rows of collectors, totaling more than 500,000 square meters in area, and has an estimated annual output of 180 GWh. It includes a molten salt-based heat storage system that allows power production to continue for up to 7.5 hours after sunset. A recent development in concentrator technology involves the use of lower-cost linear Fresnel reflectors instead of parabolic mirrors. These consist of flat or slightly curved mirrors arrayed in long rows and aligned so that they focus the suns rays onto a long tube, with the aid of a small secondary reflector. Water in the tube is turned directly into high-pressure, hightemperature steam, which can be used to power a
turbine. An experimental plant was set up in Almeria, Spain, in 2007 by researchers from the Fraunhofer Institute for Solar Energy Systems, based in Freiburg, Germany. The German company Novatec Biosol commissioned PE1, a 1.4 MW plant using Fresnel technology, in Spain in May 2009 and has commenced work on a larger, 30 MW plant. Power Towers An early example of the power tower approach from the 1980s and 1990s was the large installation at Barstow, California, now decommissioned. This used a field of tracking mirrors, or heliostats, to reflect the suns rays onto a boiler at the top of a central tower. The first Solar One plant used hightemperature synthetic oils to transfer the heat to a boiler to raise steam. The later phase of the project, Solar Two, included high-temperature heat storage using molten salt, allowing electricity to be produced on a more continuous basis. More recently, Abengoa Solars 20 MW PS20 power tower near Seville in Spain (an uprated version of the companys earlier 10 MW PS10 design, installed nearby) became operational in summer 2009. The PS20 uses 1,255 movable heliostats, each 120 square meters in area, to focus the suns rays onto a receiver and heat exchanger at the top of a 162-meter tower. The hightemperature solar heat produces steam to drive a turbine, which in turn powers a generator to produce electricity. Some of the steam is stored in insulated tanks, enabling generation to continue at night (Marsh 2009). The company is planning to construct larger power towers of up to 50 MW capacity as part of a solar electricity generating complex totaling 300 MW and incorporating a variety of solar electricity technologies, including parabolic trough and dish Stirling systems (described in the next section). In the United States, Brightsource Energy, using its Luz Power Tower 550 system, aims to construct a number of power towers, each of 100 MW capacity, in projects for the utilities Southern California Edison and Pacific Gas and Electric. The aim is to have some 900 MW operational by 2013 (Hopwood 2009). Major concentrating
solar power (CSP) plants are also planned in Israel, the United Arab Emirates, and other Middle Eastern countries (Renew 2009). Dish Stirling Solar Concentrators Another approach to concentrating solar power is to place a suitable small engine, such as a Stirling engine, at the focus of a dish-type solar concentrator. Dish Stirling solar systems can operate at around 1,000C and deliver high energy conversion efficiencies, approaching 30%. The dish tracks the sun on two axes: azimuth and elevation. Practically, each individual unit can be constructed only on a relatively small scale, around 10 to 15 MW.
Photovoltaic cells consist, in essence, of a junction between thin layers of two different types of semiconductor, known as p-type (positive) and n-type (negative). Semiconductors are materials whose electrical properties are intermediate between those of conductors, which offer little resistance to the flow of electric current, and insulators, which inhibit the flow of electricity. The semiconductors are usually made from silicon, although PV cells can be made from other materials. N-type semiconductors are made from crystalline silicon that has been doped with tiny quantities of an impurity, usually phosphorus, in such a way that the doped material possesses a surplus of free electrons. Because electrons possess a negative electrical charge, silicon doped in this way is known as an n-type semiconductor. P-type semiconductors are doped with very small amounts of a different impurity, usually boron, which causes the material to have a deficit of free electrons. These missing electrons are called holes. Because the absence of a negatively charged electron can be considered equivalent to a positively charged particle, silicon doped in this way is known as a p-type semiconductor. A pn junction can be created by joining these dissimilar semiconductors. This sets up an electric field in the region of the junction, which will cause negatively charged particles to move in one direction and positively charged particles to move in the opposite direction. When photons of light of a suitable wavelength fall within the pn junction, they can transfer their energy to some of the electrons in the material, causing an electric current to flow across the junction.
electronics industry. Polycrystalline silicon essentially consists of small grains of monocrystalline silicon. Solar cell wafers can be made directly from polycrystalline silicon in various ways. These include the controlled casting of molten polycrystalline silicon into cube-shaped ingots, which are then cut into thin, square wafers and fabricated into complete cells in the same way as monocrystalline cells. Polycrystalline PV cells are easier and cheaper to manufacture than their monocrystalline counterparts but are less efficient. Commercially available polycrystalline PV modules, sometimes called multicrystalline or semicrystalline, can attain energy conversion efficiencies of around 14%, whereas monocrystalline module efficiencies can exceed 17%. Gallium Arsenide Another material suitable for PV is gallium arsenide (GaAs), a so-called compound semiconductor. It has a crystal structure similar to that of silicon but consisting of alternating gallium and arsenic atoms. In principle, it is highly suitable for use in PV applications because it has a high light absorption coefficient, so only a thin layer of material is required. GaAs cells are more efficient than those made from monocrystalline silicon. They can also operate at relatively high temperatures without substantial reduction in efficiency, which makes them well suited to use in concentrating PV systems. But GaAs cells are more expensive than silicon cells, partly because the production process is not so well developed and partly because gallium and arsenic are not abundant materials. They are often used when very high efficiency is required, regardless of cost, as in many space applications. Thin-Film Silicon PV Solar cells can also be made from very thin films of silicon, in a form known as amorphous silicon (a-Si), in which the silicon atoms are much less ordered than in the crystalline forms described above. Amorphous silicon cells are much cheaper to produce than those made from crystalline silicon. This form of silicon is also a better absorber
of light, so thinner, and therefore cheaper, films can be used. There are advantages in the manufacturing process as well: it operates at a much lower temperature than that for crystalline silicon, so less energy is required; it is suited to continuous production; and it allows quite large areas of cells to be deposited onto a wide variety of both rigid and flexible substrates, including steel, glass, and plastics. But a-Si cells are currently much less efficient than their single-crystal or polycrystalline silicon counterparts. Commercially available a-Si modules achieve stable efficiencies in the range of 4% to 8%. A-Si cells are already widely used as power sources for a variety of consumer products, such as calculators, where the requirement is for low cost rather than high efficiency.
Concentrating PV SystemsThe energy output of PV cells can be increased by using mirrors or lenses to concentrate the incoming solar radiation onto the cellsan approach similar to that described above in the section on parabolic trough concentrators. The concentration ratio can vary from as little as two to several hundred or even several thousand times. The concentrating system must have an aperture equal to that of an equivalent flat plate array to collect the same amount of incoming solar energy. In concentrating PV systems, the cells are often cooled, either passively or actively, to prevent overheating. Systems with the highest concentration ratios use sensors, motors, and controls to allow them to track the sun on two axesazimuth (horizontal orientation) and elevation (vertical tilt)ensuring that the cells always receive the maximum amount of solar radiation. Systems with lower concentration ratios track the sun on a single axis and can have simpler tracking mechanisms.
Other Thin-Film PV Technologies Among the many other thin film technologies, some of the most attractive are those based on compound semiconductors, in particular, copper indium diselenide (CuInSe2, usually abbreviated to CIS), copper indium gallium diselenide (CIGS), and cadmium telluride (CdTe). Modules based on all of these technologies are in production from various manufacturers. Thin-film CIGS cells have attained the highest laboratory efficiencies of all thin-film devices, around 17%, and CIGS modules with stable efficiencies over 10% are commercially available. Cadmium telluride modules can be made using a relatively simple and inexpensive process, and efficiencies over 10% are claimed. However, the modules contain cadmium, a highly toxic substance, so stringent precautions need to be taken during manufacture, use, and eventual recycling. Other thin-film and innovative technologies entering production or in development include multijunction PV cells, in which different pn junctions are tuned to absorb light from different parts of the solar spectrum, and photoelectrochemical cells using dye-sensitized layers of titanium dioxide. Still at the R&D stage are thirdgeneration photovoltaic systems based on nanotechnology or using organic materials.
Grid-Connected, Building-Integrated PVIn most parts of the developed world, grid electricity is easily accessible and can provide a convenient backup to PV or other fluctuating renewable energy supplies. In these grid-connected PV systems, a grid-commutated inverter, or synchronous inverter, transforms the DC power from the PV arrays into AC power at a voltage and frequency that can be accepted by the grid, while debit and credit meters measure the amounts of power bought from or sold to the utility. PV arrays can be built into the roofs of houses. They can also be integrated into the roofs and walls of commercial, institutional, and industrial buildings, replacing some of the conventional wall
cladding or roofing materials that would otherwise have been needed and reducing the net costs of the PV system. In the case of some prestigious office buildings, the cost of conventional cladding materials can exceed the cost of cladding with PV. Commercial and industrial buildings normally are occupied mainly during daylight hours; this correlates well with the availability of solar radiation. Thus the power generated by PV can significantly reduce an organizations need to purchase power from the grid at the retail price and in some countries can be sold to the grid at a premium price (discussed in the section on Costs and Economics of Renewables below).
and clearer skies, than northern Europe. In such regions, the majority of the radiation is direct, making tracking and concentrating systems effective and further increasing the annual energy output. The price of electricity from such PV installations is likely to be less than half of that from comparable non-tracking installations. Kurokawa et al. (2007) give a detailed analysis of the feasibility and economics of very large-scale photovoltaic power plants in various regions of the world, including the Mediterranean, Middle East, Gobi Desert, and Oceania. Their report also examines the conditions under which very largescale PV installations in deserts could contribute to sustainable community development in the surrounding regions and includes a comparison of the relative merits of concentrating solar thermal and concentrating PV systems.
[ /kWh] 1.0
0.8 900 h/a* 0.44 /kWh 0.6 1800 h/a*: 0.22 /kWh 0.4
Figure 2.1. Evolution of price of PV modules and systems in selected IEA reporting countries, 1997 2007, allowing for the effects of inflation
*h/a: Hours of sun per annum 900 h/a corresponds to northem countries of Europe 1800 h/a corresponds to southern countries of Europe Note: the black band indicates that the market support programs will be necessary until about 2020 in some markets.
per watt. The operation and maintenance (O&M) costs of PV installations are very low, and thus the main driver of the cost per unit of electricity produced is the capital cost. As the cost of electricity from PV systems continues to reduce and the cost of grid electricity from conventional fossil and nuclear sources continues to increase, many analysts expect the two to converge in the near future, leading to grid parity for PV power (see Figure 2.2).
nologies, coupled with further improvements across the PV supply chain. Looking beyond Europe to the potential for world-scale PV development, EPIA and Greenpeace International (2008) point out that the projections in their earlier Solar Generation reports (20012007) consistently underestimated the actual growth in world PV production (see Table 2.1). Growth in recent years has greatly exceeded expectations. The EPIA/Greenpeace projections for future world PV growth, shown in Table 2.2, therefore may not be as optimistic as they might seem. They suggest that by 2030, PV could be supplying some 2,600 terawatt-hours (TWh). This is 8.9% of the total world electricity demand forecast by the International Energy Agency in its Reference Scenario for that year; or 13.8% of world electricity demand if the energy efficiency improvements envisaged in Greenpeaces own Energy Revolution scenario (2005) are implemented.
Table 2.1. Annual PV capacity installed/predicted: market results versus Solar Generation (SG) scenario predictions since 2001 (in MW)Year Market result SG I 2001 SG II 2004 SG III 2006 SG IV 2007 SG V 2008 2001 334 331 2002 439 408 2003 594 518 2004 1,052 659 2005 1,320 839 986 2006 1,467 1,060 1,283 1,883 2007 2,392 1,340 1,675 2,540 2,179 1,700 2,190 3,420 3,129 4,175 2,150 2,877 4,630 4,339 6,160 2,810 3,634 5,550 6,650 6,950 2008 2009 2010
Source: EPIA/Greenpeace 2008 Note: The 2008 market result was 5,568 MW installed, again greatly exceeding the SG V 2008 projection of 4,175 MW
Table 2.2. Projected solar PV electricity output and detailed associated projections for 2030 in the SG V scenarioGlobal solar electricity output in 2030 8.9% of global electricity demand from PVa 13.8% of global electricity demand from PVb
Detailed projections for 2030 PV systems cumulative capacity Electricity production Grid-connected consumers Off-grid consumers Employment potential Market value Cost of solar electricity Cumulative CO2 savingsSource: EPIA/Greenpeace 2008 a Demand forecast from IEA Reference Scenario b Demand forecast from Greenpeace Energy (R)evolution scenario
1,864 GW 2,646 TWh 1,250 million 3,216 million 10 million jobs 454 billion ($618 billion) per annum 713 euro cents (1018 U.S. cents) per kWh depending on location 8,963 million metric tons of CO2
wood) in PV array support structures can also improve the overall energy payback time of PV systems.
Wind PowerWhen solar radiation enters the earths atmosphere, because of the curvature of the earth, it warms different regions of the atmosphere to differing extentsmost at the equator and least at the poles. Because air tends to flow from warmer to cooler regions, this causes what we call winds.
It is these air flows that are harnessed in windmills and wind turbines to produce power. Wind power, in the form of traditional windmills used for grinding corn or pumping water, has been in use for centuries. But in the past few decades, the use of wind turbines for electricity generation has been growing rapidly. This discussion will concentrate on modern, horizontal-axis wind turbines, installed on land and offshore. It can be shown, using basic physical principles, that the power in a stream of flowing air that is, in the windis proportional to the cube of the wind velocity. It can also be shown that the power produced by a wind turbine is proportional to the area swept by the blades, which for a conventional horizontal-axis turbine is proportional to the square of the rotor diameter. A typical modern wind turbine starts producing power at wind speeds of around 5 meters per second (m/s). As wind speed increases, the power output increases steadily until it levels off at the rated power of the turbine, its maximum power output level. The wind speed at which it does this is called the rated wind speed. The turbine is designed to continue producing power at this constant level at wind speeds above this rated level until it reaches an upper limit, the shutdown wind speed. At this speed, the turbine is designed to stop rotating to avoid damage from excessive forces. If a wind turbine could produce power continuously at its rated capacity for a year, its annual capacity factor would be 1.0. Clearly this is impossible, as the wind does not blow all the time. In practice, annual capacity factors range from about 0.2 to 0.4, depending on the frequency distribution and magnitude of the wind speeds at the site where a turbine is located.
jected electricity needs by 2020. As Table 2.3 shows, even when environmental and economic considerations are taken into account, the economically competitive potential, onshore and offshore, still amounts to three times the projected 2020 electricity demand and seven times that for 2030. At the end of 2008, the EUs installed wind capacity was 65 GW, producing around 142 TWh of electricity, just over 4% of Europes needs (EEA 2009). The EU has set a target to produce 20% of its primary energy from renewable sources by 2020, which will involve a substantial contribution from wind-generated electricity. Details of wind energy developments in Germany and Spain, the EU countries with the largest installed wind generating capacity, are discussed in Chapters 14 and 15.
Table 2.3. Projected technically available, environmentally constrained, and economically competitive potential for wind energy in 27 EU countries in 2020 and 2030Year Technical potential Onshore Onshore Offshore Offshore Total Total Constrained potential Onshore Onshore Offshore Offshore Total Total Economically competitive potential Onshoreb Onshoreb Offshore Offshore Total Total 2020 2030 2020 2030 2020 2030 2020 2030 2020 2030 2020 2030 2020 2030 2020 2030 2020 2030 TWh 45,000 45,000 25,000 30,000 70,000 75,000 39,000 39,000 2,800 3,500 41,800 42,500 9,600 27,000 2,600 3,400 12,200 30,400 Share of 2020 and 2030 demanda 1113 1011 67 7 1720 1718 1011 9 0.70.8 0.8 1012 10 23 6 0.60.7 0.60.8 3 7
Source: EEA 2009 a European Commission projections for energy demand in 2020 and 2030 (European Commission 2008a, b) are based on two scenarios: business as usual (4,078 TWh in 2020 and 4,408 TWh in 2030) and EC Proposal with RES trading (3,537 TWh in 2020 and 4,279 TWh in 2030). The figures here represent the wind capacity relative to these two scenarios; e.g., onshore capacity of 45,000 TWh in 2020 is 11 to 12.7 times the size of projected demand. b These figures do not exclude Natura 2000 areas
base-case No New Wind Scenario. This would represent less than 0.06 cent (6 one-hundredths of 1 cent) per kilowatt-hour of total generation by 2030, or roughly 50 cents per month per household. (NREL 2008) The projections of the costs the Department of Energy made for its 20% Wind Scenario versus the No Wind Scenario, as described above, are shown in Figure 2.3.
$3,000 $2,500 $2,000 $1,500 $1,000 $500 $0 20% wind Wind 0&M costs No new wind Fuel costs Conventional 0&M costs Capital 0&M costs
Figure 2.3. Incremental investment costs of a 20% wind contribution to U.S. electricity demand in 2030
1,000 900 Annual capacity (MW) 800 700 600 500 400 300 200 100 0 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 UK Sweden Other (Finland, Belgium, China) Cumulative global capacity Denmark Netherlands Germany
100 0
Figure 2.4. Annual offshore wind capacity buildup in the United Kingdom, Sweden, Denmark, the Netherlands, and Germany, plus total cumulative installed capacity, 19892009
decade was 30% per annum. The wind industry now employs some 400,000 people worldwide (GWEC 2009). According to a major study by the GWEC called Wind Force 12, by 2020, the world wind industry could be providing some 3,054 TWh of electricity, around 12% of world demand. This would involve an installation rate of about 158 GW per annum, culminating in a total installed capacity of 1,254 GW by 2020. The investment required would be approximately 80 billion ($109 billion) per annum, involving the creation of 2.3 million job-years of employment. The study estimates that the generation cost of electricity from wind in 2020 could reduce to 2.45 euro cents (U.S. 3.34 cents) per kWh (GWEC 2009). This may appear to be a best-case scenario but cost reductions of this order could be feasible, given the economies achievable through very large-scale production coupled with increases in turbine size and further improvements in turbine technologies.
GW of offshore capacity by 2020, though this may be constrained (Carbon Trust 2008; DECC 2009).
Wave PowerWhen winds blow over the worlds oceans, they cause waves. The power in such waves as they gradually build up over very long distances can be great, as anyone watching that power eventually being dissipated on a beach will appreciate. A wide variety of technologies for harnessing the power of waves have been developed over the past few decades (see Duckers 2004). One example is the oscillating water column (OWC). Here the rise and fall of the waves inside an enclosed chamber alternately blows and sucks air through an air turbine, which is coupled to a generator to produce electricity. Another promising wave energy conversion system, currently undergoing tests off Portugal, is the Pelamis (sea snake), developed by the Scottish company Ocean Power Delivery. It is oriented head-on to the wave front and consists of a series of long, floating steel tubes connected by articulating hydraulic joints. The varying pressure in the hydraulic joints, as the passing waves move the cylinders relative to one another, powers hydraulic motors, which in turn drive electrical generators.
Offshore WindFigure 2.4 shows the recent buildup of offshore wind generating capacity in Europe. Initially Denmark was the leading country, but since 2008 the United Kingdom has taken the lead, with some 1,000 MW installed by 2009. The United Kingdom has ambitious plans to install up to 29
BiofuelSince prehistoric times, human beings have harnessed the power of fire by burning wood to create warmth and light and to cook food. Wood is created by photosynthesis in the leaves of plants. Photosynthesis is a process powered by solar energy, in which atmospheric carbon dioxide and water are converted into carbohydrates (compounds of carbon, oxygen, and hydrogen) in the plants leaves and stems. These, in the form of wood or other biomass, can be used as fuels called biofuels. Wood is still widely used as a fuel in many parts of the developing world. In some countries, other biofuels, such as animal dung, are also used. These traditional biofuels are estimated to supply around 11% of world primary energy. If the forests that provide wood fuel are replanted at the same rate as they are cut down, then wood fuel use is in principle renewable. When forests are managed sustainably in this way, the CO2 absorbed in growing replacement trees should equal the CO2 given off when the original trees are burned. A significant contribution to supplies now comes from modern bioenergy power plants. These feature the clean, high-efficiency combustion of straw, forestry wastes, or wood chips from trees grown in special plantations. The heat produced is used either directly or for electricity generation, and often for both purposes. Municipal wastes, a large proportion of which are biological in origin, are also widely used for heat or electricity production. Liquid wastes can also be processed in anaerobic digesters to produce methane, which can then fuel an internal combustion engine to produce electricity and heat. As a recent IEA report observes: Technologies for producing heat and power from biomass are already well-developed and fully commercialised, as are first-generation routes to biofuels for transport. A wide range of additional conversion technologies are under development, offering prospects of improved efficiencies, lower costs and improved environmental performance. However, expansion of bioenergy also poses some
challenges.The potential competition for land and for raw material with other biomass uses must be carefully managed. The productivity of food and biomass feedstocks needs to be increased by improved agricultural practices. Bioenergy must become increasingly competitive with other energy sources. Logistics and infrastructure issues must be addressed, and there is need for further technological innovation leading to more efficient and cleaner conversion of a more diverse range of feedstocks. (IEA 2009, 2)
HydroelectricityAnother energy source that has been harnessed by humanity for many centuries is the power of flowing water, which has been used for milling corn, pumping, and driving machinery. The original source of hydroelectric power is solar energy, which warms the worlds oceans, causing water to evaporate from them. In the atmosphere, this forms clouds of moisture, which eventually falls back to earth in the form of precipitation. Rain flows down through mountains into streams and rivers, where its flow can be harnessed using water wheels or turbines to generate power. During the 20th century, hydropower has grown to become one of the worlds principal electricity sources. In 2008, it contributed just over 6% of world primary energy (BP 2009).
Tidal PowerThe physical principles underlying tidal energy are different from those underlying hydropower, but the technologies used in tidal barrages are similar to those employed in hydroelectric plants. Tides are caused chiefly by the gravitational pull of the moon on the oceans, although the suns gravity also plays a minor role. The output from tidal energy systems is variable but highly predictable. The principal technology for harnessing tidal energy essentially involves building a low dam, or barrage, across the estuary of a suitable river. The barrage includes inlets that allow the rising sea levels to build up behind it. When the tide has reached maximum height, the inlets are closed,
and the impounded water is allowed to flow back to the sea in a controlled manner, via a turbinegenerator system similar to that used in hydroelectric schemes. The worlds largest tidal energy scheme is at La Rance, France, with a capacity of 240 MW. There are a few other, smaller tidal plants in various countries, including Canada, Russia, and China. The United Kingdom has one of the worlds best potential sites for a tidal energy scheme, in the Severn Estuary. If built, the largest of the various proposed Severn schemes would be rated at around 8,600 MW, much larger than any other single UK power plant, and could provide about 5% of current UK electricity demand (see DECC 2009; SDC 2007). Another, newer tidal energy technology involves the use of underwater turbines, rather like submerged wind turbines, to harness the strong tidal and oceanic currents that flow in certain coastal regions. In the United Kingdom, a 10 kW prototype tidal current turbine was tested at Loch Linne, Scotland, in 1994; a larger, 300 kW prototype was tested off the Devon coast in 2002; and another 300 kW prototype is currently operational in Strangford Lough, Northern Ireland. The technology is still under development, but its prospects are promising.
the earth, it is a renewable energy source. But in many cases, this is not so: the geothermal heat is in effect being mined and will be depleted locally in perhaps a few years or decades. In regions of the world where geothermal aquifers are not readily accessible, it may nevertheless be possible to harness the earths geothermal heat using enhanced geothermal, or hot dry rock, technologies. These involve drilling down to considerable depths to access the hot rocks, then fracturing them using high-pressure water. This creates a form of heat exchanger into which cold water from the surface can be pumped and from which hot water can be extracted for heating or electricity production. The EU currently has a pilot project to develop this technique at Soulz, France (Dettmer 2009).
Geothermal EnergyGeothermal energy is not derived from solar radiation. Its source is the earths internal heat, which originates mainly from the decay of longlived radioactive elements far below the surface. The most useful geothermal resources occur where underground bodies of water, or aquifers, can collect this heat, in areas where volcanic or tectonic activity brings it close to the surface. The resulting hot water, or in some cases steam, is used for electricity generation where its temperature is sufficiently high to make this feasible, such as in Italy, Iceland, New Zealand, the United States, and the Philippines, and for direct heating in many other countries. If geothermal heat is extracted in a particular location at a rate that does not exceed the rate at which it is being replenished from deep within
120 10010% Transport 12% Transport
80 605.5% Heat 8.4% Heat 8% Transport
40 20 0 20
142 145
TWhSources: UK Department of Energy and Climate Change (DECC) and Department for Transport (DfT) internal analysis, based on Redpoint/ Trilemma 2009; NERA/AEA 2009; and Element/Pyry 2009
Figure 2.5. Marginal resource costs for different levels of renewable generation by sector in the United Kingdom in 2020
and marketing, plus the profits made by the various intermediaries in the distribution chain from producer to consumer. Calculations of the estimated future capital costs of renewable energy plant, or of the energy they generate, are often adjusted for the effects of inflation and are thus expressed in terms of costs or prices in a given yearfor example, in 2009$. In a world of floating exchange rates, if cost calculations involve a conversion from one currency to another, they will be valid for only a limited time. In calculating the future capital costs of renewable energy equipment produced in substantial quantities, such as wind turbines or solar PV panels, analysts often assume that a reduction in unit costs will occur in the future as a result of the economies gained through volume production sometimes called the learning curve effect. It will be obvious from the above considerations that the wide variety of ways in which costs and prices can be calculated can give rise to confusion.
the resource estimated to be available from selected renewables at various levels of cost. Figure 2.5 is a recent example (DECC 2009). This chart covers costs for heat and transport fuel, as well as for electricity. From this figure, it can be seen that 25% of UK electricity could be produced from renewables by 2020 for approximately 60 ($90) per MWh. Producing 28% of electricity from renewables would increase this to about 70 ($105) per MWh, and further increasing the proportion to 32% would cost around 100 ($150) per MWh. The bar at the far right in the figure shows the cost of supplying 2% of electricity from smallscale renewable sources, estimated at about 150 ($225) per MWh. The price of electricity generation from small-scale sources varies widely, as can be seen from Table 2.4, which shows the prices proposed under new feed-in tariffs for small- and medium-scale electricity generation (under 5 MW) in the United Kingdom from 2010. The highest price is for domestic-scale photovoltaics under 4 kW, at 36.5 pence (54.8 cents) per kWh; the lowest is for medium-scale biomass, hydro, and wind plants, at 4.5 pence (6.8 cents) per kWh. These tariffs are designed to provide a reasonable rate of return to investors in the various
168 174
2% small-scale electricity
10% Heat
Table 2.4. Prices paid under proposed UK feed-in tariffs for electricity from 2010 and annual digression rates from 2010 onwardTechnology Anaerobic digestion Anaerobic digestion Biomass Biomass Biomass Hydro Hydro Hydro Hydro PV PV PV PV PV PV Wind Wind Wind Wind Wind Wind Existing microgenerators transferred from renewables obligation Scale Electricity only CHP < 50 kW 50 kW5 MW CHP < 10 kW 10100 kW 100 kW1 MW 15 MW < 4 kW (new build) < 4 kW (retrofit) 410 kW 10100 kW 100 kW5 MW Stand-alone system < 1.5 kW 1.515 kW 1550 kW 50250 kW 250500 kW 500 kW5 MW Proposed initial tariff (p/kWh) 9.0 11.5 9.0 4.5 9.0 17.0 12.0 8.5 4.5 31.0 36.5 31.0 28.0 26.0 26.0 30.5 23.0 20.5 18.0 16.0 4.5 9.0 Annual digression (%) 0 0 0 0 0 0 0 0 0 7 7 7 7 7 7 4 3 3 0 0 0 N/A
Source: DECC 2009 Note: p/kWh = pence per kilowatt-hour; 1 pence = 1.5 cents (U.S.) as of this writing
technologies and should therefore give a reasonably good indication of the current costs/prices of electricity from small- to medium-scale plants in UK conditions.
place more costly power from conventional plants. When wind power is contributing significant quantities of energy, it can also reduce the spot price of fossil fuels. Because it requires no fuel, wind energy if used significantly also reduces a nations vulnerability to imported fossil-fuel price increases, with beneficial effects on GDP, and because of its fixed cost, it reduces the system risk in a nations generation portfolio. (For further details, see Auwerbuch 2009; Bruce 2009). Some of the key economic and technological characteristics of the renewable electricity generating technologies described above are summarized in Table 2.5: their cost drivers (the relative roles of capital, O&M, and fuel costs); their levels of maturity; the potential for scale economies; and
Solar ponds Ocean thermal Solar chimney Concentrating solar: trough Concentrating solar: power tower Concentrating solar: dish Stirling PV: crystalline PV: thin film Concentrating PV Wind onshore Wind offshore Wave Hydro (large) Biofuel electricity Tidal barrage Tidal stream Geo aquifers Geo HDRSource: Authors estimates
Capital, O&M Capital, O&M Capital Capital, O&M Capital, O&M Capital, O&M Capital Capital Capital, O&M Capital Capital, O&M Capital, O&M Capital Capital, O&M, fuel Capital Capital, O&M Capital Capital
High Med Low Med Med Med Med Med Med High Med Low High High? High Low High Low
the potential for near-term technological advances. The factors that are likely to lead to reductions in the cost of electricity from renewables vary widely among technologies: in some areas, significant potential exists for technological breakthroughs that could result in major cost reductions; in other, more mature areas, steady incremental improvements will gradually reduce costs. In addition, many renewable technologies benefit from economies of scale, including the economies of physical scale, such as those achievable from building larger wind turbines, and the economies of series production, which are achievable irrespective of physical scale.
power, are dispatchablein other words, like conventional power plants, their output can, within limits, normally be made available quickly to electricity system operators in response to demand. The outputs of other renewables, such as those based on wind, wave, and solar power, are not so readily dispatchable: they are weatherdependent and therefore variableunless they incorporate storage, as is the case with some solar thermal-electric systems. Wind, wave, and solar power are sometimes described as intermittent, but a more accurate term for their output is variable. Tidal power is in an intermediate category: its output, though variable, is highly predictable. But the variability of these renewable sources is not the major problem that some have suggested. National electricity systems are already designed to cope with the major fluctuations in both demand and supply that occur on various timescales, from minutes to years. Introducing a variable renewable source introduces an additional
element of uncertainty into the system, but this is manageable by system operators in a manner similar to the way in which the uncertainty in both demand and conventional supplies is already managed. With low levels of penetration of variable renewables on the system, the additional level of uncertainty is small; it becomes larger as the penetration level increases. For simplicity, this discussion will concentrate on the integration of wind power into electricity systems; the integration of other renewables, such as solar and wave power, is amenable to similar solutions. The topic of renewable electricity integration is covered in much greater detail in Boyle (2007). In existing national electricity systems, various kinds of backup power supplies are already provided. In essence, these consist of short- and longterm supplies. Short-term backup supplies are provided to maintain frequency and voltage stability on the network. These include fossil-fueled spinning reserve plants, operating at below their rated power but able to increase output at very short notice; hydropower plants, including pumped storage schemes, the output of which can be made available quickly; and large diesel or gas turbine generators, which are able to be brought online within a few minutes. Long-term backup supplies are those required to ensure security of supply in the event of major disruptions, such as the simultaneous failure of several large power stations. They include older fossil-fueled plants, usually less efficient than modern plants and therefore infrequently used, that can be brought back into operation if necessary within a few hours or days. In addition, many national systems have interconnectors to the electricity systems of nearby countries; these can be called on to provide additional power if needed. How much would the additional backup supplies needed for substantial levels of wind contribution cost? A recent UK Energy Research Centre study (UKERC 2006) reviewed a wide range of evidence from around the world and concluded that for a 10% to 20% contribution of wind to the UK system, the additional cost would be 2.5 to 3 ($3.76 to $4.50) per MWh, rising to 3 to 3.5 ($4.50 to $5.26) per MWh for a 20% to 45%
wind contribution. These costs are relatively small in relation to total generation costs.
Wind ForecastingThe cost of providing additional backup to cope with the additional variability of substantial quantities of wind generation can be significantly reduced through the use of wind forecasting methods. Figure 2.6 shows an example of a dayahead wind forecast for one month in Germany, using techniques developed at the University of Kassel. These employ a combination of techniques: numerical weather predictions; analysis of physical processes; statistical time series analysis; and learning techniques based on artificial neural networks (Lange et al 2007). As can be seen, the difference between forecast and monitored power output is relatively small. This enables backup plants to be scheduled efficiently and at minimum cost.
StorageIt is often asserted that energy storage is needed to cope with the variability of wind power and other fluctuating renewable energy sources, though this is not necessarily the case. Various large-scale storage technologies are available, ranging from pumped hydro schemes to compressed-air storage to vanadium redox (reduction-oxidation) battery storage, but their costs are high. However, electricity storage in large, integrated national electricity supply systems is desirable only if it costs less than providing more conventional backup suppliesor if storage can provide additional benefits to the system operator or to users, as may be the case with battery storage in electric vehicles. Electricity storage in electric vehicle (EV) batteries may become increasingly attractive as such vehicles enter widespread use in order to reduce urban air pollution and emissions of greenhouse gases. EV batteries would be charged mainly at night, when electricity systems normally have spare capacity, and could readily absorb any surpluses of power from wind or other renewables. The batteries would be discharged
16 14 12 10 8 6 4 2 0 1.01Source: Lange et al (2007)
Power (GW)
Figure 2.6 Day-ahead forecast of wind power output compared with monitored output, for one month in Germany
when the vehicles are driven during the day, which should not make substantial demands on the electricity system, though some daytime charging would be inevitable.
ments of Germany, France, Belgium, the Netherlands, Luxembourg, Denmark, Sweden, Ireland, and the United Kingdom are developing plans, to be announced at the end of 2010, to build the first phase of the supergrid in the North Sea region. Even greater smoothing effects may be achievable if wind power sources can be integrated over a still larger area. Gregor Czisch of the University of Kassel has modeled a series of detailed scenarios in which he has combined the monthly wind power production of good wind sites in the EU and Norway with sites in Northern Russia, western Siberia, southern Morocco, and Mauritania. When the combined output is compared with the average monthly electricity demand in the EU and Norway, supply and demand are quite well matched, except for a small shortfall around SeptemberOctober (see Czisch 2006; Murray 2009). A similarly ambitious proposal, the Desertec Industrial Initiative, has been put forward by the Trans-Mediterranean Renewable Energy Cooperation (TREC) and German Aerospace Center (DLR). The proposal, backed by 12 major European companies including Munich Re, the worlds largest reinsurance company, involves linking a wide range of renewable electricity sources right across Europe, the Middle East, and
North Africa (EUMENA), again using HVDC links to minimize power losses in long-distance electricity transmission. The system would eventually combine the outputs of wind, biofuel, hydro, concentrating solar, photovoltaic, and geothermal power plants. The combined output would be sufficient to meet the total electricity needs of the entire area (see Desertec 2009; D-MTEC 2009). These proposals have recently been endorsed in principle by the European Union in its Mediterranean Solar Plan (FerreroWaldner 2009).
ConclusionsRenewable sources are in principle capable of supplying all of humanitys energy requirements many times over. Many relatively new technologies for renewable electricity generation, such as solar and wind power, are maturing and growing rapidly. Some others, such as hydroelectricity, are already mature. Renewables are already making significant inputs into the electricity supplies of some countries and have the potential to provide much more power worldwide in coming decadesprovided that appropriate policy support measures are implemented (for examples, see REN21 2007, 2009). The current costs of power from renewables are often higher than from conventional sources, but the costs of the former are steadily falling and those of the latter are likely to rise. In addition, the costs of conventional fossil fuels do not take into account the external costs to society of their use, such as their contribution to global climate change. If all such costs were included, most renewable energy sources would already be costcompetitive (see Sovakool and Watts 2009). Moreover, substantial future cost reductions may occur for many renewables as a result of mass production and improvements in technology.
Note1. The subject of renewable energy is covered in much greater detail in Boyle (2004).
Its Impact on the Market for Renewable Energy. Zurich: Swiss Federal Institute of Technology. Duckers, L. 2004. Wave Energy. In Renewable Energy: Power for a Sustainable Future, 2nd ed., edited by G. Boyle. Oxford: Oxford University Press, 298340. EEA (European Environment Agency). 2009. Europes Onshore and Offshore Wind Energy Potential:An Assessment of Environmental and Economic Constraints. Technical report 6/2009. Copenhagen: EEA. Element/Pyry. 2009. Design of a Feed-In Target for Sub-5 MW Electricity in GB: Final Report. London: Pyry, 118pp. EPIA (European Photovoltaic Industries Association). 2009. Set for 2020: Executive Summary. Brussels: EPIA. EPIA/Greenpeace. 2008. Solar Generation V2008: Solar Electricity for Over One Billion People and Two Million Jobs by 2020. Brussels: EPIA. European Commission (EC). 2008a. European Energy and Transport Trends to 2030 (update 2007). http:// ec.europa.eu/dgs/energy_transport/figures/ trends_2030_update_2007/ energy_transport_trends_2030_update_2007_en.pdf . 2008b. Model Based Analysis of the 2008 EU Policy Package on Climate Change and Renewables. Brussels: Report to DG ENV. Everett, R. 2004. Solar Thermal Energy. In Renewable Energy: Power for a Sustainable Future, 2nd ed., edited by G. Boyle. Oxford: Oxford University Press, 51. EWEA (European Wind Energy Association). 2009. Wind Energy:The Facts: Executive Summary. Brussels: EWEA. Ferrero-Waldner, B. 2009. The Mediterranean Solar Plan: A Necessity, Not an Option. Speech 09/60 by EU commissioner for External Relations and European Neighbourhood Policy, European Union Sustainable Energy Week, February 2009, Brussels. Greenpeace. 2005. Energy Revolution:A Sustainable Pathway to a Clean Energy Future for Europe. Brussels, Greenpeace International. GWEC (Global Wind Energy Council). 2009. Global Wind 2008 Report. Brussels: GWEC. Hopwood, D. 2009. The New Hot Ticket. Engineering and Technology 15 (Sept. 1225): 4851. Hurley, B. Hughes, P. and Giebel, G. 2007. Reliable Power, Wind Variability and Offshore Grids in Europe. In Renewable Electricity and the Grid: The Challenge of Variability, edited by G. Boyle. London: Earthscan, 181199. IEA (International Energy Agency). 2002. Renewable Energy: Into the Mainstream. Paris: IEA.
. 2008. Trends in Photovoltaic Applications: Survey Report of Selected IEA Countries between 1992 and 2007. Paris: IEA. . 2009. BioenergyA Sustainable and Reliable Energy Source:A review of Status and Prospects, Executive Summary. Paris: IEA. James, T. 2009. Energy from the Deep. Engineering and Technology 4 (14): 5053. Knies, G., Timm, G., Fhr, F., Wolff, G., Schn, M., Straub, M., Nokraschy, H. and Steinmetz, O. (eds). 2009. An Overview of the DESERTEC Concept. DESERTEC Red Paper. Berlin: DESERTEC Foundation. Kurokawa, K., Komoto, K., van der Vleuten, P. and Faiman, D. (eds). 2007. Energy from the Desert. London: Earthscan/International Energy Agency. Lange, B., Rohrig, K., Schlgl, F., Cali, U. and Jursa, R. 2007. Wind Power Forecasting. In Renewable Electricity and the Grid: The Challenge of Variability, edited by G. Boyle. London: Earthscan, 95120. Lockheed Martin. No date. Ocean Thermal Energy Conversion (OTEC). www.lockheedmartin.com/ products/OTEC/(accessed 26 March 2010). Marsh, G. 2009. Power Stations: Can Solar Join the Big Hitters? Renewable Energy Focus, (MayJune): 5058. Murray, J. 2009. Wind Holds Key to European Super Grid. Interview with Dr Gregor Czisch. Real Power 18 (Oct.Dec.): 3132. NERA/AEA (NERA Economic Consulting/AEA Technology). 2009. The UK Supply Curve for Renewable Heat. Report for the Department of Energy and Climate Change, July 2009, Ref: URN 09D/689 (DECC). London: NERA Economic Consulting. NREL (National Renewable Energy Laboratory). 2008. 20% Wind Energy by 2030. NREL Report DOE/GO-1020082567, July. National Renewable Energy Laboratory. Redpoint/Trilemma. 2009. Implementation of the EU 2020 Renewables Target in the UK Electricity Sector: RO Reform. Report for the Department of Energy and Climate Change, June 2009, URN 09D/702. London: Redpoint Energy Ltd. REN21 (Renewable Energy Policy Network for the 21st Century). 2007. Renewables Global Status Report. Paris: REN21. . 2009. Renewables Global Status Report: 2009 Update. Paris: REN21. Renew. 2009. CSP Moves Ahead to 3GW. Renew 180: 12. Schlaich, J. 1995. The Solar Chimney: Electricity from the Sun. Stuttgart: Edition Axel Menges.
SDC (Sustainable Development Commission). 2007. Turning the Tide: Tidal Power in the UK. London: SDC. Sovakool, B., and C. Watts. 2009. Going Completely Renewable: Is It Possible (Let Alone Desirable)? Electricity Journal 22 (4): 95111.
Twidell, J., and A. Weir. 1986. Renewable Energy Resources. London: E & FN Spon. UKERC (UK Energy Research Centre). 2006. The Costs and Impacts of Intermittency. London: UKERC.
Part IIRenewables, Climate Change, and Energy Policy
3Renewables Forecasts in a Low-Carbon World: A Brief OverviewErin T. Mansurhis chapter examines how concerns for climatecoupled with policymakers and voters other preferencesare expected to affect the growth of renewables, particularly in the United States and European Union (EU). It begins with a review of the recent growth in renewables and a discussion of current carbon policies, then looks at predictions of levels of investment in renewables in a low-carbon world. Within the next 20 years, some authors predict that 20% to 40% of electricity will come from renewable resources, in an attempt to mitigate the impacts of climate change. Given the wide range of assumptions regarding carbon policies, economic growth, the responsiveness of the economy to carbon policy, innovation in renewables, and other modeling approaches, it is hard to generalize among these black box models. Their complexity and continuous updating make a detailed peer review process necessary, though this is beyond the scope of this chapter. Under continued dependence on fossil fuels in a business-as-usual scenario, climate scientists predict significant increases in average temperature and sea level, as well as many other climatic responses. Climate change is expected to adversely affect many economic sectors, including agriculture, forestry, insurance, health, tourism, and energy. Furthermore, it is likely to have many
notable nonmarket effects, such as the loss of flora and fauna and an increase in human morbidity and mortality. The Intergovernmental Panel on Climate Change (IPCC 2007) and Stern (2006) review the science and economic consequences of climate change. These concerns may be addressed through several mechanisms for mitigating climate change. Broadly, we may change how we produce and use electricity, space heating, transportation, and other energy applications; we may sequester greenhouse gases (for example, by reducing deforestation or increasing reforestation); or we may even consider climate geoengineering.1 Within the electricity sector, methods for reducing greenhouse gases, such as carbon dioxide (CO2), include both demand-side (conservation and energy efficiency) and supply-side options. Supply options include switching from coal to less carbon-intensive conventional technologies such as natural gas, large hydroelectric, and nuclear power; continuing to use coal but reducing the carbon emissions with abatement technologies such as carbon capture and storage (CCS); or, the focus of this book, turning to alternative renewable sources of electricity, including wind, solar, geothermal, and small hydropower. From an economic perspective, greenhouse gases are global, stock externalities. (Chapter 5
Erin T. Mansur
covers this topic in more depth.) In brief, an externality is a cost (or benefit) incurred by others not involved in a market transaction. Global externalities are those where the location of the source of the pollutant is irrelevant to the damages. For example, a coal-fired power plant in China may be the source of released carbon dioxide emissions, but the effects of these emissions will be experienced all over the world. Furthermore, these effects will be the same as had the carbon dioxide emissions come from natural gas power plants in, say, England. Stock pollutants are long-lived, and their damages from current emissions may continue for years to come. For example, carbon dioxide emitted today will remain in the upper atmosphere for 100 years or more (IPCC 2007).2 For stock pollutants, the optimal policy is clear in theory: each ton of emissions released is charged the net present value of all future damages that it causes.3 Specifically, a pollution tax can achieve the efficient outcome. A cap-and-trade regulation can also be efficient if the permit price equals this tax. A pollution tax on greenhouse gases would give firms and consumers an incentive to change behavior in many ways: switching to less carbonintensive technologies, conserving electricity, driving less, and so forth. Firms investing in renewables would be just one of many responses. Are renewables the best option for addressing climate change? In order to answer this question, one would need to know the relative marginal costs, both private and external, incurred by each option that reduces greenhouse gas emissions. Whether or not producing electricity from renewable resources is the cheapest way to reduce greenhouse gas emissions, many government agencies and other authors predict that renewables will be a major contributor to a low-carbon future. Investing in renewables is a popular response to climate concerns, in part, perhaps, because of the other externalities that it addresses. First, other energy sources have additional negative externalities such as conventional air pollution, water pollution, and nuclear waste. Note that if regulation results in marginal social costs equaling marginal social benefits of these other energy sources, these externalities will be inter-
nalized and no further regulation is required. Second, positive technological spillovers may result from investing in renewables from which other industries, or firms in the same industry, benefit.4 Learning benefits within a firm, which may account for the largest gains, are not positive externalities. Finally, subsidizing technology is seemingly more politically palatable than taxing firms and consumers for polluting. This chapter provides an overview of current electricity sources and energy and climate policies affecting the recent growth in investment in renewables, then looks at the direction of future carbon policies. This is followed by a review of the literature on predictions of investment in renewables under various low-carbon scenarios.
140,000 120,000 Capacity (MW) 100,000 80,000 60,000 40,000 20,000 0 1996 1998 2000 2002 2004 2006 2008
Annual growth
Figure 3.1. Wind power worldwide capacity and annual growth rate, 19972008 (in MW)
While renewables are still a small share of total electricity production, they have seen enormous growth in the past two decades. In 1990, the Organisation for Economic Co-operation and Development (OECD) countries had a total capacity of 2.4 gigawatts (GW) of wind power and 0.5 GW of solar, including both photovoltaics (PV) and thermal (IEA 2004).6 By 2008, however, wind capacity had reached 17 GW in Spain alone (WWEA 2009). Table 3.1 reports installed wind
capacity for the 10 countries with the most wind capacity as well as worldwide totals for 2007 and 2008. Worldwide, wind capacity reached 121 GW in 2008, an increase of 29% over the previous year (WWEA 2009). This annual growth rate is typical of this industry over the past decade. Figure 3.1 plots annual worldwide capacity and growth rate from 1997 to 2008.
Table 3.1. Total installed capacity of wind energy in 2007 and 2008 (in GW)Country U.S. Germany Spain China India Italy France UK Denmark Portugal Rest of world TotalSource: WWEA 2009
2007 16.8 22.2 15.1 5.9 7.9 2.7 2.5 2.4 3.1 2.1 13.1 93.9
2008 25.2 23.9 16.7 12.2 9.6 3.7 3.4 3.3 3.2 2.9 17.1 121.2
Change 8.4 1.7 1.6 6.3 1.7 1.0 0.9 0.9 0.0 0.7 4.0 27.3
This impressive growth rate has also been exhibited in other renewables as well. As recently as 2003, worldwide capacity in solar PV was only 1.3 GW, but as of 2008, it had reached 13 GW. This tenfold increase can be attributed primarily to Germany and Spain, which account for twothirds of the worlds solar PV capacity. Overall, total renewables (excluding large hydropower) are now at 280 GW (REN21 2009). Table 3.2 reports the capacity of various renewable resources by country. Historically, the two major impediments to renewables have been costs and intermittency.The cost of wind power has dropped substantially over the past decade (IEA 2004). Now, with the production tax credit (discussed below), wind power costs less than 5 cents per kWh in the United States and can compete with the cost of building new fossil-fuel-fired power plants (Wiser and Bolinger 2008). Photovoltaic solar power is an order of magnitude more expensive: the average installed cost (excluding direct financial incentives or tax credits) in 2007 was $7.60 per watt in the United States (Wiser et al. 2009), or about 30 to 50 cents per kWh, depending on the interest rate and capacity factor. These costs have fallen substantially over the past few decades. Even from summer 2008 to summer 2009, they fell another 40% with the entry of China into the market
(Galbraith 2009). Although dramatically cheaper than in the past, solar PV remains an expensive option for addressing climate change (Borenstein 2008). In comparison, the average installed cost of wind in 2007 was $1.70 per watt in the United States (Wiser and Bolinger 2008). The IPCC (2007) projects that the costs of renewables will continue to fall, and that by 2030, renewables (including wind, solar PV, and biomass) will see capital costs under $1.20 per watt. (See Chapter 2 for further discussion on renewable technologies and their costs.)
Table 3.2. Capacity of renewable resources by type and country in 2008 (in GW)Developing countries 24 65 25 0.1 4.8 0 0 119
World Wind Small hydropower Biomass Solar PV Geothermal Solar thermal Ocean (tidal) 121 85 52 13 10 0.5 0.3
With the passage of the American Recovery and Reinvestment Act of 2009 (ARRA; see U.S. Congress 2009), wind, small hydropower, geothermal, biomass, municipal solid waste, and marine energy sources may be eligible to earn a subsidy of $21/MWh under the production tax credit (PTC). Many of these renewable resources can forgo the PTC and become eligible for the 30% investment tax credit (ITC).7 As a third option, a Department of Treasury cash grant can be selected in place of the PTC or ITC. Metcalf (2009b) provides greater detail on the tax incentives for renewables. U.S. wind investment is quite elastic to changes in the user cost of capital (Metcalf 2009a): the federal PTC has been a substantial driver of wind investment over the past two decades. How these incentives are expected to affect renewable investment is addressed in the next section. A second major driver of renewable investment in the United States is the set of state-level renewable portfolio standards (RPSs, discussed in Chapter 11). To date, 28 states and the District of Columbia have implemented mandatory programs that require utilities to purchase a certain percentage of their power from renewable sources; the definition of the technologies that
comply with the regulation differs from state to state (see DSIRE 2009). Markets for renewable energy credits (RECs), or green tags, have arisen within some states. These allow firms to comply with the regulations by either directly investing in renewables or buying credits from other compliant sources. In Europe, some countries have chosen renewable standards as a policy instrument to achieve the targets in the Renewable Electricity Directive (2001/77/EC). As in many U.S. REC markets, several European countries have implemented tradable certificates in order to reach these goals (European Commission 2009; Nielsen and Jeppesen 2003). Figure 3.2 reports the share of renewable electricity by country for large hydropower and other renewables in 2007. Notably, over half the electricity generated in Sweden is from large-scale hydropower or other renewables, and more than a quarter of Denmarks power is from nonhydro renewables. The figure also shows the 2010 objectives. Although these targets are greater than the share of electricity generation from renewable resources in 2007 for many countries, some, such as Denmark, have just reached the target, and Germany and Hungary have even exceeded it.
Source: Authors calculations based on European Commission 2009 and Eurostat 2009
Figure 3.2. Share of renewable electricity by EU country in 2007 and targets for 2010
In order to comply with these standards, many countries have used feed-in tariffs that have spurred investment in renewables. Many of these subsidies are on the order of hundreds of euros per MWh. For example, some solar power producers in Spain receive 340 ($463) per MWh under Real Decreto 1578/2008. In 2008, Germanys solar feed-in tariff was even greater: some solar producers received 574 ($782) per MWh under the Erneuerbare-Energien-Gesetz.8 As a result, Spain and Germany have seen dramatic growth in renewable investments (see Tables 3.1 and 3.2). (Chapters 13 to 15 discuss the EU experience, focusing on the United Kingdom, Germany, and Spain.) In addition to renewable standards and feed-in tariffs, the EU has implemented a multinational, multi-industry cap-and-trade policy for greenhouse gases. The EU Emission Trading System (ETS) began in 2005 and regulates about 2 billion metric tons of CO2 each year. With permit prices fluctuating between 10 and 30 ($13 and $41) a ton, this market is valued at around 37 billion ($50 billion) per year.9 (Chapter 6 further discusses emissions trading.) In addition to costs, another major issue that renewable sources face is intermittency. Some renewable resources, such as wind and solar power, produce only some of the time and cannot be depended on to produce reliably. Intermittency will not be completely solved until a cheap, reliable mechanism of storing power is developed (NREL 2008). However, at a level of investment that is predicted over the next few decades (between 20% and 30% of electricity generation), geographic averaging and natural gas capacity that serves as backup reserve are likely to dampen the issue of intermittency. This may require new investment in natural gas power, especially in areas where renewables primarily produce off-peak (Campbell 2009). Complete dependence on current nonhydroelectric renewable technologies seems questionable, however, because of intermittency issues.10 A related issue concerns the development of grids. In the United States, many of the locations that have the most potential for renewables are far from population centers where electricity
demand is greatest. Vajjhala et al. (2008) discuss how state RPSs would provide significantly different incentives for improving the transmission network than would a federal standard, which would allow for greater cost savings but would require more of a transmission build-out. State policies would be less cost-effective, as renewables would be built in many states that have high costs, but would require less investment in new transmission. (Chapters 7, 9, and 11 further discuss the important issues of intermittency and designing the transmission grid.)
2012. In June 2009, the new ETS Directive (2009/29/EC) committed the EU to reducing greenhouse gas emissions by 20% of 1990 levels by 2020 (see European Parliament and Council 2009 for more details). Furthermore, Directive 2009/28/EC states that 20% of the EUs total energy consumption must be from renewable resources by 2020. Note that this is a much larger commitment than in the United States, which seeks to have 20% of electricity production from renewables by 2020. In the United States, and in the rest of the world, about 40% of total energy consumption is used to generate electricity. So 20% of electricity generation in the United States is only 8% of total energy consumption. These policies focus on the near future. In order to attain a low-carbon world, however, some believe we will need an even greater investment in renewables.The next section of this chapter examines the level of investment in renewables that is expected if we have carbon policies either like those described above or that call for even more dramatically reduced emissions. The focus remains on the United States and EU, but some models examine worldwide levels of investment in renewables.
ability, technology, prices, and so forth. A careful comparison of the models requires an in-depth examination of each assumption and the modeling approach taken. However, the intent here is to give a brief overview of the findings so the reader can gain an appreciation of the range of estimates in the literature, rather than to provide a meta-analysis or pick a preferred model. The range of models discussed presents perspectives of worldwide, U.S., and EU investment over the next 20 to 40 years.
A Global PerspectiveThe IEA (2007) uses its World Energy Model (WEM) to examine long-run forecasts of emissions, energy supply and demand, and energy sources under various scenarios. In its annual report, the World Energy Outlook, IEA gives three scenarios: a reference or business-as-usual scenario; an alternative policy scenario that would include reducing greenhouse gas emissions by 19% relative to the reference case by 2030; and a 450 stabilization scenario. This last scenario is a lowcarbon world in which carbon dioxide equivalent concentrations would be stabilized at levels ranging from 445 to 490 parts per million (ppm) (IPCC 2007). This would require emissions of carbon dioxide equivalent (CO2e) to peak in 2015 and fall by up to 85% of 2000 levels by 2050.13 For the year 2030, the reference scenario implies 42 gigatons (Gt, or billion metric tons) of CO2 emissions, the alternative policy scenario implies 34 Gt, and the stabilization scenario implies 23 Gt. In a low-carbon world, nonhydropower renewable resources would double their expected generation in 2030 relative to a business-as-usual scenario (see Table 3.3). This would include adding over 1,400 TWh per year from wind and solar power. Even in the reference case, the amount of wind power is expected to increase fivefold from current levels.14 Overall, the IEA predicts that 40% of electricity could be produced from renewable resources, about half of which would be from nonhydropower resources. The U.S. Department of Energy also publishes a global perspective of renewable investments in its International Energy Outlook. In the
Table 3.3. IEA world energy outlook predictions, showing generation by energy source (in TWh)2030 reference case 4,842 840 1,287 173 161 12 2,473 7,315 35,384 21% 2030 stabilization case 6,608 2,056 2,464 219 406 28 5,173 11,781 29,300 40%
Source Hydro Biomass Wind Geothermal Solar Tidal (wave) Nonhydro renewables Total renewables Total generation Percent renewableSource: IEA 2007, Table 5.6
Growth rate 36% 145% 91% 27% 152% 133% 109% 61% 17%
business-as-usual scenario, the DOE predicts a possible 6,724 TWh of renewables (21% of total generation) by 2030, including 1,951 TWh of nonhydroelectric generation (6.1% of total generation). Of this, most of the nonhydropower will be in the OECD countries (1,417 TWh).
A European PerspectiveThe European Commissions World Energy Technology Outlook2050 (WETO) forecasts three cases of world energy out to 2050: a reference case, carbon constraint case, and hydrogen case (European Commission 2006). The report uses a worldwide simulation model of various energy sectors, known as Prospective Outlook on Longterm Energy Systems (POLES), to look at both EU and global energy issues. The POLES model accounts for world population and GDP growth, as well as technological advancement lowering the costs of energy.15 Table 3.4 reports WETOs predicted electricity generation by fuel type for each scenario in 2030 and 2050. In the reference model, WETO forecasts that total renewables will account for 21% of electricity production by 2030 and 25% by 2050. By 2050, solar and wind are expected to produce more than half of the electricity generated from renewables, with wind (6,433 TWh per year), particularly offshore, becoming even larger than hydropower (4,853 TWh per year) worldwide.
This represents a 25-fold increase in wind power from 2008 levels. The increase to 1,493 TWh from solar is even larger in magnitude (the United States and EU produced about 4.5 TWh last year). Nuclear power is also predicted to increase rapidly. The reference case includes modest carbon policy akin to what is currently in place in those countries with such policy. For this case, the WETO reports substantial investment in renewables for the world as a whole. Nonetheless, carbon dioxide emissions are projected to be more than double todays levels by 2050 in this scenario. The reference case is also reported by region. In Europe, WETO predicts even greater renewable penetration: 26% of electricity production could be from renewables in both 2030 and 2050. Solar, biomass, and wind power will account for approximately two-thirds of that generation. North America is similar, with an expected 20% of all electricity coming from solar, wind, or biomass. The carbon constraint case seeks to stabilize carbon dioxide concentrations, though at levels greater than in the IEA study: the WETO case aims to stabilize CO2e at 650 ppm. In 2030, WETO predicts 8,823 TWh of renewable production, about half from hydropower and a quarter each from wind and biomass. In 2050, wind is predicted to be dominant, accounting for 7,336 TWh of the 17,439 TWh that are renewable. This
Table 3.4. Electricity generation by fuel type for the three WETO scenarios (in TWh)Panel A: year 2030 Energy source Coal Gas Biomass Nuclear Hydro and geothermal Solar Wind Hydrogen Total electricity Percent renewable Panel B: year 2050 Energy source Coal Gas Biomass Nuclear Hydro and geothermal Solar Wind Hydrogen Total electricity Percent renewable Reference 19,066 9,072 2,246 14,866 4,853 1,493 6,433 811 60,040 25% Reference 12,689 8,760 1,372 6,328 4,148 91 1,880 39 36,295 21% World Carbon 9,114 9,438 1,684 6,449 4,284 213 2,642 56 34,587 26% World Carbon 9,016 9,640 2,649 19,862 5,128 2,326 7,336 1,477 57,812 30% Hydrogen 9,371 8,959 2,584 21,426 4,998 2,058 6,799 898 57,377 29% Reference 1,860 1,337 328 2,931 738 344 817 190 8,608 26% Hydrogen 8,205 8,851 1,644 8,834 4,226 183 2,417 44 35,039 24% Reference 1,551 1,319 258 1,447 697 17 545 9 5,932 26% Europe Carbon 969 1,545 315 1,432 706 29 608 15 5,673 29% Europe Carbon 781 1,492 361 3,612 746 593 859 321 8,803 29% Hydrogen 633 1,465 360 3,942 743 591 838 243 8,845 29% Reference 3,976 1,408 598 2,014 764 308 1,115 73 10,337 27% Hydrogen 794 1,540 311 1,597 702 28 604 12 5,642 29% Reference 2,944 1,836 417 1,088 711 13 428 4 7,560 21% North America Carbon 1,208 2,281 469 967 732 68 728 9 6,548 30% Hydrogen 1,491 2,186 474 981 730 55 727 7 6,714 30%
North America Carbon 1,415 1,841 680 2,509 784 590 1,337 196 9,407 36% Hydrogen 1,525 1,738 685 2,474 782 523 1,352 120 9,233 36%
implies that renewables will account for 30% of total electricity production, with wind (12.7%), solar (4.0%), and biomass (4.6%) all playing important roles. North America and Europe are expected to have similar levels of nonhydro renewable penetration: 14.2 and 9.8% wind, 6.3 and 6.7% solar, and 7.2 and 4.1% biomass for North America and Europe, respectively. The hydrogen case continues with carbon policies to stabilize greenhouse gas concentrations but also assumes substantial breakthroughs in
hydrogen technology. In particular, breakthroughs in the performance and cost of the distribution and consumption sectors of the hydrogen economy are identified as being of greatest importance. This scenario changes the mix of fossil fuels to nuclear power and changes transportation fuels: hydrogen provides 13% of final energy consumption (primarily for transport) in this case, compared with the 2% share in the reference case.
With respect to renewables, however, the hydrogen scenario is qualitatively similar to the carbon constraint case.
A U.S. PerspectiveThe DOEs Energy Information Administration (EIA) publishes the Annual Energy Outlook, which reports long-run projections of U.S. energy supply and demand. The EIA (2009a) uses the National Energy Modeling System (NEMS) to forecast scenarios relevant to investments in renewables. The reference case for 2030 predicts an increase in renewable capacity in the United States of 57 GW (+/ 10 GW, depending on scenarios assumptions regarding costs). This accounts for about 22% of all new capacity constructed over the next 22 years. Regardless of the cost scenario, the EIA expects 730 TWh of renewable generation in 2030. Nonhydropower resources are predicted to account for about 14% of U.S. total electricity generation (or 430 TWh). Costs do not change predictions much because the state renewable portfolio standards are assumed to bind. The mix of renewables does change, however: the EIA forecasts greater use of wind power relative to biomass if capital costs fall. The EIA compares its reference case with some other studies. Namely, it notes that IHS Global Insight (IHSGI 2008) predicts similar levels of growth in renewables, with slightly more conservative estimates from Energy Ventures Analysis (EVA 2008). In the reference case, by 2015, the EIA predicts generation from hydroelectric and other renewables, including net imports, to be 555 TWh, while IHSGI predicts 537 TWh and EVA predicts 420 TWh, just slightly more than the 374 TWh generated in 2007. By 2030, the predictions increase to 758 TWh (EIA), 864 TWh (IHSGI), and 535 TWh (EVA). These correspond to 15%, 17%, and 11% of total generation for each model, respectively. EIAs projections of capacity are also in line with alternative models. In addition to IHSGI and EVA, the Institute of Energy Economics and the Rational Use of Energy (IER 2008) also forecasts renewable capacity for 2015 and 2030. The total capacity for hydroelectric and other renewables
was 131 GW in 2007. In 2015, it is expected to increase to 157 GW (EIA), 160 GW (IHSGI), 115 GW (EVA), and 208 GW (IER). By 2030, the capacity is predicted to be 191, 232, 128, and 312 GW, respectively. In summary, the EIA reference case seems consistent with several other predictions by industry and academia. The EIA predicts several counterfactuals relevant to a low-carbon future relative to a reference case that predated the recent stimulus bill, ARRA. The first counterfactual is to look at the effect of the bill, which extends two important subsidies: the PTC and the ITC. Figure 3.3 compares annual investment in nonhydropower renewables from 2006 to 2030 under the baseline reference case and under the ARRA. In 2012, the policy is expected to increase nonhydropower renewables from 195 to 310 TWh, a 59% increase. By 2030, total renewables are expected to account for 15% of electricity generation in the United States (two-thirds of which are from nonhydroelectric resources). The increase in renewable capacity from the ARRA is even more substantial. By 2015, an additional 40 GW of renewable capacity is expected, mostly from wind (35 GW). The EIA anticipates that by 2030, wind power capacity (68 GW) will nearly rival that of conventional hydropower (78 GW) in the United States. Only some of the increase in renewables that is attributed to the ARRA is the result of simply extending the PTC. In a simulation that extends the PTC through 2019, the EIA predicts that wind power would increase by 19 GW (while biomass, municipal solid waste, and geothermal power would be unchanged) by 2020 relative to the reference scenario. Although an increase of 19 GW may seem large, especially given that the United States had only 25 GW wind power capacity in 2008 (WWEA 2009), the EIA expects that the ARRA will increase renewable power by more than 33 GW in 2020, with wind accounting for all of the gain. The ARRA allows developers to convert the PTC into federal grants, thereby avoiding a major hurdle: namely, that only those with significant tax liability can benefit from the tax credit.
80 Capacity (GW)
200 Baseline TWh ARRA TWh Baseline GW ARRA GW 2010 2015 2020 2025
Figure 3.3. Expected electricity generation and capacity from nonhydropower renewable sources with and without ARRA, 20062030
In a third scenario, the EIA examines a lowcarbon case: the LiebermanWarner bill, 2191 (LW110). The EIA expected that the bill would result in the electric power sectors emissions falling by more than 50% relative to 2007 levels. Furthermore, the bill would have increased renewable generation in 2030 from 730 to 1,063 TWh (or 22% of total electric generation). Renewable capacity would nearly double from the reference case (57 to 103 GW). Nuclear power and advanced coal with carbon capture and storage (CCS) would also increase substantially: from 13/1 GW of nuclear/CCS capacity in the reference case to 47/99 GW in the LW110 case.
emissionsnamely renewables, nuclear power, and advanced coal with CCSwould rise for the business-as-usual scenario of 14% of primary energy to 18% by 2020, 26% by 2030, and 38% by 2050. Using the detailed Integrated Planning Model, EPA accounts for both the cap-and-trade program and the RES. Table 3.5 reports the expected investment in various sources of electric power under the reference case as well as under the WaxmanMarkey bill. The reference case is based on the EIAs Annual Energy Outlook 2009 (EIA 2009a). Relative to the reference case, the climate bill would increase electricity generated by renewables (excluding hydropower) by only 3% to 5%. Including hydropower, the EPA model predicts approximately zero change in generation from renewables in 2025. This status quo is due to a reduction in total electricity consumption, in part because of higher electricity prices but also because of economic incentives to conserve. One way utilities can comply with the RES is through energy efficiency programs. Even though EPA does not predict that the bill will result in substantial incremental investment in renewables, the reference case includes 364 TWh produced by renewables in 2025. In contrast, current production from renewables is 124 TWh (EIA 2009b). In other words, based on cur-
Table 3.5 EPA analysis of the WaxmanMarkey Bill, showing generation by energy source, 2020 and 2025 (in TWh)Panel A: year 2020
Energy source Coal Advanced coal (w/ CCS) Natural gas/oil Nuclear Hydropower Other renewables Total renewables Total electricity Percent renewablePanel B: year 2025
Reference case 2,222 14 703 816 290 334 624 4,379 14%
Energy source Coal Advanced coal (w/ CCS) Natural gas/oil Nuclear Hydropower Other renewables Total renewables Total electricity Percent renewableSource: EPA 2009a
Reference case 2,312 14 788 837 292 364 656 4,607 14%
rent policies, EPA expects renewables to triple over the next 17 years. In the reference case, nonhydroelectric renewables would account for 8% of total electricity generation (or 9% under the WaxmanMarkey bill). Including hydropower, this increases to 14% (16% with the bill). A final note regarding the WaxmanMarkey bill: according to EPAs model, the bill is expected to cause substantial growth in clean coal (advanced coal with CCS), on the order of 400% to 1,200%, relative to the reference case. In addition to the EPA study, the MIT Joint Program on the Science and Policy of Global Change has analyzed the bill (Paltsev et al. 2009). This analysis finds that annualized costs of the bill would be on the order of $400 per household with a permit price starting at just over $20 per ton of CO2 in 2015 and reaching $38 by 2030.
The bill is expected to increase renewables only slightly: the share of power from nonhydroelectric renewables would increase from 10% to 11%.
power). For comparison, the EU produced only 104 TWh from wind, 3.8 TWh from solar PV, and 212 TWh from nonhydropower renewables in 2007 (Eurostat 2009). As a share of overall gross electricity consumption in the EU countries, this is an increase from 6.5% in 2007 (16% if hydropower is included) to 15% (22%) in 2020. The authors also examine a policy scenario whereby best practices of currently available strategies are applied across all 27 EU countries.16 In this policy case, wind power is expected to produce 461 TWh, another 17.9 TWh from solar PV, and 928 TWh from total renewables (excluding large-scale hydropower), or 26% of expected total electricity generation. With hydropower, this increases to 1,234 TWh (34%). The authors conclude that following currently available best practices, renewables could account for a third of all electricity generation in the EU.
Other ModelsThe research of some academics, trade groups, and nonprofit organizations also has examined the question of what could be the long-run level of investment in renewables. Palmer and Burtraw (2005) use the RFF Haiku model of the U.S. electricity industry to analyze the effects of renewable portfolio standards (RPSs) and renewable energy production tax credits. In the reference case, the authors predict that in 2020, 151 TWh of nonhydropower could be generated, or only 3.1% of total generation (9.5% if hydropower is included). Under a general renewable production tax credit, which is set at current levels but applies to a much broader set of renewables, Palmer and Burtraw find that 729 TWh would be produced. This would result in 15% of electricity coming from nonhydropower renewables (with 43% of it from wind power). Total renewables, including hydropower, are predicted to account for 21% of electricity supply. Palmer and Burtraw look at several possible RPSs for 2020: 5%, 10%, 15%, and 20%. In each case, the standard is binding for nonhydropower renewables, and hydropower accounts for an additional 6.5% of total electricity generation. For the low-level RPS, nonhydropower renewables are dominated
by geothermal. However, a 20% RPS would require substantial investment in wind power, accounting for about half of all nonhydropower renewable generation (468 of 948 TWh). Many other studies have examined this question. For example, the Intergovernmental Panel on Climate Change (IPCC 2007) notes that renewable electricity, including hydropower, was 18% of world electricity supply in 2005, and that with a carbon price of $50 per ton of CO2e, it could reach a 30% to 35% market share by 2030. The Union of Concerned Scientists (Cleetus et al 2009) suggests a blueprint to reduce U.S. carbon dioxide emissions by 84% below 2005 levels by 2030. Through energy efficiency policies, the study suggests that electricity consumption could be reduced by 35% relative to the reference case. An RPS would result in wind, solar, geothermal, and biomass providing 40% of the total electricity, or about 21% of overall energy supply. The WWEA (2009) expects 1,500 GW of installed wind capacity producing 12% of electricity generation by 2020. The Energy Watch Group (Rechsteiner 2008) predicts that by 2025, the investment could be even greater, with 7,500 GW installed wind capacity producing 16,400 TWh a year. Furthermore, the study anticipates that total renewables could account for more than 50% of new power installations worldwide by 2019 and all of it by 2022. By 2037, it suggests that nonrenewable generation could be completely phased out.
ConclusionsWithin 20 years, some authors predict that 20% to 40% of electricity could be produced from renewable resources. Some predict even higher levels. Table 3.6 draws together the medley of forecasts outlined above to show the range of model predictions as well as some important regional and policy differences. Returning to an economists perspective, one would need to know the relative net marginal social benefits for each type of renewable resource, at each location and point in time, in order to determine whether a 20%, 30%, 40%, or
even 50% market share for renewable electricity is optimal. This chapter focused, however, not on what should be the level of investment in renewables, but rather on what modelers predict could be the level of investment, based on either policies that are currently being discussedsuch as the third phase of the EU Emission Trading System, EU Directive 2009/28/EC on renewables, or the U.S. WaxmanMarkey billor future policy objectives, such as stabilizing carbon dioxide equivalent concentrations at 450 or 650 ppm. By reviewing major studies on this topic, this chapter has provided a range of estimates of levels of investment in wind power, solar PV, nonhydropower renewables, and total renewables for the world overall, the EU, and the United States. The range of levels of investment reflects many differences regarding the assumptions in the models, the policies being analyzed, and the methodologies used by the researchers. This chapter has shown that although opinions are diverse on what might happen over the next 20 to 40 years, studies employing a variety of models and a range of assumptions regarding carbon policy forecast the likelihood of a significant increase in the role of renewables in meeting electricity demand.
Notes1. Geoengineering includes methods such as stratospheric sulfur aerosols, which manage solar radiation. These methods do not reduce greenhouse gases, but rather limit their effects on global warming. Some also use this term to include carbon sequestration. Not all greenhouse gases are as long-lived. Methane, for example, has a chemical lifetime in the atmosphere of approximately 12 years. Technically, a stock pollutant never expires; for example, lead is a true stock pollutant. However, the economic insights of stock pollutants help in thinking about regulating greenhouse gases. More precisely, the optimal level of pollution at a point in time will be where present value marginal benefits of pollution (the avoided compliance costs) are equal to the present value marginal costs of pollution (the marginal damages). For further discussion, see Tietenberg (2006). Fischer and Newell (2008) show that a policy promoting renewables may be more efficient than a carbon tax if these positive technology spillovers are large relative to the climate externalities. However, they find for a range of parameter values, a price on carbon is preferred even when there are knowledge spillovers. One terawatt-hour is 1 million megawatt-hours (MWh) or 1 billion kilowatt-hours (kWh). One
9. 10.
TWh approximately equals the annual consumption of about 89,000 U.S. residential customers. Electricity generation capacity is measured in watts (W), kilowatts (kW, 1,000 W), megawatts (MW, 1 million W), or gigawatts (GW, 1 billion W). Some renewables, including solar, wind and geothermal energy, first became eligible for tax credits with the passage of the 1978 Energy Tax Act. The EEG was further amended: as of January 2009, PV solar subsidies have been reduced to 330430 ($449$586) per MWh. The price on August 24, 2009 was 15.34 ($20.89) (Point Carbon n.d.). Some express concern that predictions of substantial levels of investment in the future are not feasible, given the issue of intermittency. In a recent paper, Heal (2009) reviews the literature on the economics of renewables. He shares these concerns and concludes that nuclear and CCS are likely to be important sources of electricity in a low-carbon future. It is unlikely that the WaxmanMarkey bill will pass into law as it was written when the House passed it in the summer of 2009. As of this writing, Congress has not passed any climate bill. The requirement is that 20% be from renewables or energy efficiency, but at least 75% of that amount must be through renewables. This constraint is expected not to be binding. CO2e is a measure for greenhouse gases whereby all gases are normalized by their global warming potential. In 2008, wind power produced 260 TWh (WWEA 2009); in the reference case, the IEA (2007) predicts a possible 1,287 TWh by 2030. The POLES model was developed by the Centre National de la Recherche Scientifique (CNRS) and is maintained by CNRS, UPMF University, Enerdata, and the Institute for Prospective Technological Studies. See Enerdata (2006). Best practices are defined as those strategies that have proven to be most effective in the past in implementing a maximum share of RES (Ragwitz et al. 2005).
ReferencesBorenstein, Severin. 2008. The Market Value and Cost of Solar Photovoltaic Electricity Production. Work-
ing paper 176. Berkeley, CA: University of California Energy Institute, Center for the Study of Energy Markets. Campbell, Arthur. 2009. Government Support for Intermittent Renewable Generation Technologies. Working paper. Cambridge, MA: Massachusetts Institute of Technology. econ-www.mit.edu/files/ 3563 (accessed August 17, 2009). DSIRE (Database of State Incentives for Renewables and Efficiency). 2009. Project of the North Carolina Solar Center and Interstate Renewable Energy Council funded by the U.S. Department of Energys Office of Energy Efficiency and Renewable. Energy. www.dsireusa.org/ (accessed February 20, 2010). EIA (U.S. Energy Information Administration). 2009a. Annual Energy Outlook 2009:With Projections to 2030. DOE/EIA-0383(2009). www.eia.doe.gov/oiaf/ aeo/pdf/0383(2009).pdf (accessed August 19, 2009). . 2009b. Short-Term Energy OutlookAugust 2009. www.eia.doe.gov/pub/forecasting/steo oldsteos/aug09.pdf (accessed August 29, 2009). Enerdata. 2006. POLES Model: Prospective Outlook on Long-term Energy Systems: A World Energy Model. www.eie.gov.tr/turkce/en_tasarrufu/uetm/ twinning/sunular/hafta_02/5_POLES_ description.pdf (accessed August 29, 2009). EPA (U.S. Environmental Protection Agency). 2009a. EPA Analysis of the American Clean Energy and Security Act of 2009 HR 2454 in the 111th Congress. www.epa.gov/climatechange/economics/ economicanalyses.html (accessed August 20, 2009). . 2009b. EPA Preliminary Analysis of theWaxmanMarkey Discussion Draft: The American Clean Energy and Security Act of 2009 in the 111th Congress. www.epa.gov/climatechange/economics/pdfs/ WM-Analysis.pdf (accessed February 26, 2010). European Commission. 2006. World Energy Technology Outlook2050. Directorate-General for Research EUR 22038. ec.europa.eu/research/energy/pdf/ weto-h2_en.pdf (accessed February 26, 2010). . 2009. The Renewable Energy Progress Report. Commission Staff Working Document SEC(2009) 503 final. http://eur-lex.europa.eu/LexUriServ/ LexUriServ.do?uri=COM:2009:0192:FIN:EN:PDF (accessed August 19, 2009). European Parliament and Council. 2009. Directive 2009/29/EC of the European Parliament and of the Council of 23 April 2009. eur-lex.europa.eu/ LexUriServ/LexUriServ.do?uri= OJ:L:2009:140:0063:0087: EN:PDF (accessed February 26, 2010).
Eurostat (European Commission Eurostat). 2009. Energy: Yearly Statistics 2007. EVA (Energy Ventures Analysis). 2008. FUELCAST: Long-Term Outlook. Fischer, Carolyn, and Richard G. Newell. 2008. Environmental and Technology Policies for Climate Mitigation. Journal of Environmental Economics and Management 55 (2): 142162. Galbraith, Kate. 2009. More Sun for Less: Solar Panels Drop in Price. NewYork Times, Aug. 26, B1. Heal, Geoffrey. 2009. The Economics of Renewable Energy. Working paper 15081. Cambridge, MA: National Bureau of Economic Research. IEA (International Energy Agency). 2004. Renewable Energy: Market and Policy Trends in IEA Countries. Paris: OECD Publishing. . 2007. World Energy Outlook 2007. Paris: OECD Publishing. . 2009. World Energy Outlook 2009 Fact Sheet. www.worldenergyoutlook.org/docs/weo2009/ fact_sheets_WEO_2009.pdf (accessed February 26, 2010). IER (Institute of Energy Economics and the Rational Use of Energy). 2008. TIAM Global Energy System Model. Stuttgart: University of Stuttgart. IHSGI (IHS Global Insight). 2008. Global Petroleum Outlook. Lexington, MA: IHSGI. IPCC (Intergovernmental Panel on Climate Change). 2007. Fourth Assessment Report. www.ipcc.ch/ publications_and_data/ publications_and_data_reports.htm#1 (accessed August 10, 2009). Martinot, Eric, Ryan Wiser, and Jan Hamrin. 2005. Renewable Energy Policies and Markets in the United States. Working paper. San Francisco: Center for Resource Solutions. Metcalf, Gilbert. 2009a. Investment in Energy Infrastructure and the Tax Code. Working paper. Medford, MA: Tufts University. . 2009b. Tax Policies for Low-Carbon Technologies. Working paper w15054. Cambridge, MA: National Bureau of Economic Research. Nielsen, Lene, and Tim Jeppesen. 2003. Tradable Green Certificates in Selected European Countries: Overview and Assessment. Energy Policy 31 (1): 314. NREL (National Renewable Energy Laboratory). 2008. 20% Wind Energy by 2030. Increasing Wind Energys Contribution to U.S. Electricity Supply. Technical paper. Golden, CO: NREL. Palmer, Karen, and Dallas Burtraw. 2005. CostEffectiveness of Renewable Electricity Policies. Energy Economics 27: 873894.
Paltsev, S., J.M. Reilly, H.D. Jacoby, and J.F. Morris. 2009. Appendix C: Cost of Climate Policy and the Waxman-Markey American Clean Energy and Security Act of 2009 (HR 2454). In The Cost of Climate Policy in the United States, edited by S. Paltsev, J.M. Reilly, H.D. Jacoby and J.F. Morris. MIT Joint Program Report Series No. 173, 121. Point Carbon. No date. Point Carbon home page. www.pointcarbon.com (accessed February 26, 2010). Ragwitz, Mario, Joachim Schleich, Claus Huber, Gustav Resch, Thomas Faber, Monique Voogt, Rogier Coenraads, Hans Cleijne, and Peter Bodo. 2005. Analysis of the EU Renewable Energy Sources Evolution up to 2020 (FORRES 2020). www.emu.ee/orb.aw/class=file/action=preview/ id=254843/FORRES_FINAL_REPORT.pdf 3563 (accessed August 25, 2009). Rechsteiner, Rudolf. 2008. Wind Power in Context: A Clean Revolution in the Energy Sector. Basel, Switzerland: Energy Watch Group and Ludwig-BoelkowFoundation. REN21 (Renewable Energy Policy Network for the 21st Century). 2009. Renewables Global Status Report: 2009 Update. Paris: REN21 Secretariat. Stern, Nicholas. 2006. The Economics of Climate Change: The Stern Review. Cambridge, UK: Cambridge University Press. Tietenberg, Thomas. 2006. Environmental and Natural Resource Economics. 7th ed. Reading, MA: AddisonWesley Longman. U.S. Congress. 2009. American Recovery and Reinvestment Act of 2009. 111th Cong., 1st sess. frwebgate.access.gpo.gov/cgi-bin/ getdoc.cgi?dbname=111_cong_bills&docid= f:h1enr.pdf (accessed August 25, 2009). U.S. Congress House Committee on Energy and Commerce. 2009. American Clean Energy and Security Act of 2009. 111th Cong., 1st sess., HR 2454. energycommerce.house.gov/Press_111/20090515/ hr2454.pdf (accessed August 20, 2009). Vajjhala, Shalini, Anthony Paul, Richard Sweeney, and Karen Palmer. 2008. Green Corridors: Linking Interregional Transmission Expansion and Renewable Energy Policies. Discussion paper 0806. Washington, DC: Resources for the Future. Wiser, Ryan, and Mark Bolinger. 2008. Annual Report on U.S.Wind Power Installation, Cost, and Performance Trends: 2007. LBNL-275E. Berkeley, CA: Lawrence Berkeley National Laboratory. Wiser, Ryan, Galen Barbose, and Carla Peterman. 2009. Tracking the Sun: The Installed Cost of
Photovoltaics in the U.S. from 19982007. LBNL1516E. Berkeley, CA: Lawrence Berkeley National Laboratory. WWEA (World Wind Energy Association). 2009. World Wind Energy Report, 2008.
4Renewable Generation and Security of SupplyBoaz Mosellekey question this book seeks to address is what justification exists for the promotion of renewable generation over other forms of lowcarbon generationin other words, for policies that specifically promote renewable generation rather than a technology-neutral approach such as a carbon tax or cap-and-trade mechanism. Simple economics suggests that the latter approach would be more effective in achieving carbon reductions at lowest cost, through competition between different carbon abatement mechanisms and technologies (e.g., renewable energy, nuclear, carbon capture and storage, reductions in non-generation sectors, energy efficiency). In the European Union (EU), one of the most common responses is that renewable generation merits specific support because it enhances security of supply by reducing dependence on imported fuels. Concerns about import dependence refer particularly (though not exclusively) to dependence on natural gas imports from Russia and Algeria, which many observers view as potentially unreliable because of political instability and, in the case of Russia, a willingness to use energy supplies as a tool of geopolitics.1 This concern has been greatly enhanced by interruptions in recent winters to the flow of gas from Russia into the EU via Ukraine, as a result of disputes between Russia and Ukraine.
At the same time, a commonly voiced concern with renewable generation is that it will endanger security of supply by leading to excessive dependence on intermittent sources such as wind and solar power. To some commentators, this argues against the promotion of renewable generation. To others, it implies the need for significant changes in power market design to ensure that sufficient backup capacity is available over various time frames. This chapter therefore focuses on these two questions, examining to what extent security of supply concerns related to import dependence warrant additional support for renewable generation relative to other forms of low-carbon technology, and to what extent security of supply concerns related to intermittency undermine the case for supporting renewables at all or necessitate major changes in market design. The focus is on the EU, where renewables deployment is most prominent on the policy agenda and is explicitly linked to security of supply by policymakers. However, many of the conclusionsin particular, those relating to intermittencycan be applied to other jurisdictions as well. The chapter begins by examining the issue of import dependence. It assesses the extent of the problem and analyzes whether there are market or
Boaz Moselle
other failures that warrant intervention, and if so, whether the promotion of renewable generation is the most efficient form of intervention to address the problem. It then focuses on the problems posed by intermittency, again assessing the problem and analyzing the case for policy intervention and the most appropriate form that intervention might take.
ence is a problem for the main fuels used for power generation and whether the promotion of renewable generation is the most appropriate policy response to any such problem.
Sources: European Commission 2008b, 65; Euratom 2008 Note: Mtoe = million tons of oil equivalent
were characterized by low gas prices for Ukraine and low transit charges for delivery of Russian gas to Europe.6 In March 2005, Russia claimed that Ukraine was not paying for gas and was diverting gas intended for transit to the EU (BBC 2006). On January 1, 2006, Russia retaliated by cutting off gas supplies passing through Ukrainian territory.7 Russia and Ukraine reached a preliminary agreement on January 4, and the supply was restored. The agreement provided for an increase in the nominal price of gas but did not provide an agreed pricing formula for future years or a transition period to higher prices.The new agreement was set to expire on December 31, 2008. The 2009 crisis began on January 1, when Gazprom cut off suppliers (again, it stopped supplying gas for Ukrainian consumption while the supply of gas that was theoretically to be transited through for European consumption continued). Initially disruption of supply to the EU was only minor, but by January 7, all supplies from Russia to the EU were cut, and supplies were not resumed until January 20. This was the most serious gas supply crisis ever to hit the EU, depriving it of 20% of its total gas supply (European Commission 2009a). Within days of the supply disruption, 12 countries were affected. They responded by drawing on storage, importing additional LNG supplies, and fuel-switching by the use of fuel oil and coal. Increased supplies were sourced from Russia via Belarus and Turkey, as well as from Norway and Libya. Gazprom is estimated to have lost sales of $2 billion (European Commission 2009a).
The reliance on imports creates a genuine security-of-supply risk. This is unlikely to be the case for a fuel that can be imported easily from a number of different countries that are politically stable, friendly, and geographically diverse.
There is good reason to think that the normal market response will not efficiently address any security-of-supply risks and that policy intervention can be expected to do better.
The first of these conditions is assessed below for each of the main fuels used for generation: coal, uranium, and natural gas. This is followed by a discussion of the potential for market or other failures that might justify intervention.
CoalGlobally, coal is much more abundant than oil or natural gas. There are proven coal reserves of 826 billion tons of coal, with a proven reserve-toproduction ratio of 122 years (BP 2009).8 Coal reserves are available in almost every country, with recoverable reserves in around 70 countries. Six countries together account for about 80% of coal reserves, as shown in Figure 4.1. Given that world coal reserves are spread across a politically and geographically diverse set of countries, large in number and including some of Europes closest political allies, the prospect of significant supply interruption seems relatively remote. It is therefore implausible to argue that dependence on coal imports is a significant threat to EU security of supply.
world, as Figure 4.2 illustrates. At the current rate of consumption, this would constitute about 100 years worth of supply. Uraniums extraordinarily high energy density makes it practical to maintain large stockpiles (Euratom 2008), reducing the risks associated with a short-term interruption in supply. This factor and the diverse range of supply sources suggest that dependence on uranium imports is not a significant security-of-supply risk for Europe, despite the high level of import dependence, unavoidable given that Europe has less than 2% of the worlds identified uranium resources (European Commission 2008b).
GasThe picture for natural gas is very different than that for coal or uranium. Prima facie there is good reason to consider that the EUs import dependence does represent a potential threat to security of supply. As noted earlier, the EU imports more than half of its gas, of which a large proportion comes from Algeria and Russia, and gas imports are predicted to increase in coming years (European Commission 2007) as output continues to decline in the main EU producing nations. Dependence on Algerian and Russian gas is of concern because of the absence or weakness of democratic institutions and transparent governance arrangements in these countries. Algeria has experienced recent civil war and ranks poorly on international league tables in terms of democracy
UraniumThe earth has 5.5 million metric tons of identified uranium resources, distributed widely around the
South Africa India 7.1% 3.7% United States 28.9% Australia 9.2%
China 13.9%Canada 8%
Other 18.2%
Kazakhstan 15%
and civil rights (World Audit 2010). Russia also has a low rank, and the poor climate for business investment raises questions as to whether new investment required to maintain and increase gas supply will be forthcoming. There is also a question as to how far the supply of gas is a commercial decision versus an instrument to exercise geopolitical influence. This means that the extent to which the supply of gas will respond to increased demand is unclear. In addition, analysts have noted that Russia needs to replace declining fields with new production from the Yamal Peninsula and offshore fields and to refurbish a large, aging high-pressure pipeline network (Stern 2005). As mentioned above, there is also a need to invest in the Ukrainian pipeline network or construct new pipelines to maintain transit capacity to Europe. Underlying these concerns is the absence of realistic alternative sources of natural gas. Relative to other fuels, the ability to bring gas from different sources is inherently limited by the more costly, capital-intensive and inflexible means of transportation required, in the form of longdistance pipelines or liquefied natural gas (LNG). Moreover, while gas remains abundant at global level, with world proven reserves as of 2007 standing at some 177 trillion cubic meters (tcm),9 equivalent to some 60 years of consumption at current rates (BP 2009), those reserves are concentrated in a small number of countries, as shown in Figure 4.3. Of these, just three countries, Russia, Iran, and Qatar, hold about 53% of the total. There is an unknown potential for European domestic gas supply to be boosted by unconventional or shale gas. In the United States, substantial discoveries of unconventional gas have been made.10 However, estimates of the potential for unconventional gas in Europe are lower. One study estimates that Europe has 29 tcm, whereas the United States has around 233 tcm of unconventional gas (Holditch 2007).11 Moreover, the ability to extract the resources will depend on environmental consents and the cost of extracting unconventional gas in Europe. In conclusion, it seems that gas import dependence is a potentially significant security-
Nigeria 2.8% Venezuela 2.6% Algeria 2.4% United Arab Emirates 3.5% Russia 23.4% United States 3.6% Saudi Arabia 4.10% Turkmenistan 4.3%
Qatar 13.8%
Iran 16.0%
of-supply risk for the EU. It is possible that a combination of LNG imports and the arrival of unconventional gas (either in Europe or in the United States but liberating LNG flows that could come to the EU) will mitigate the problem. It is also possible that the risk is overestimated because of the mutual dependence between the EU and its suppliers: revenue from gas sales is of great importance to both Russia and Algeria, and indeed, they have been known to express concern about security of demand from the EU, mirroring the EUs concerns about security of supply (see, e.g., Yenikeyeff 2006). Neither of these possibilities can be viewed as certain, however, and the risk is therefore a real one, albeit difficult to assess or quantify. The problem is particularly acute for eastern Europe. Estonia, Latvia, Lithuania, Bulgaria, Slovakia, and Finland are completely dependent on Russia for gas imports, while Greece, Hungary, and Austria are more than 80% dependent (European Commission 2008b). Among the seven new eastern European member states, dependence on Russian gas imports averages about 77% (European Commission 2009a). Eastern European commentators point to the experience in Lithuaniawhere oil supplies from Russia to the Mazeikiu refinery were halted because, it is claimed, Russia objected to its sale to a Polish refiner, PKN Orlenas a sign of the potential risks they face (Geropoulos 2007). The political temperature is clearly at its highest with regard to eastern Europe, given Russian resentment at its loss of influence there since the breakup of the Soviet Union.
judgments and use them to implement better policies. A case may therefore exist for intervention on essentially paternalistic grounds. The second problem is the issue of politically motivated supply interruptions. Arguably, the risk of supply interruption by a hostile state actor is greater the more disruptive the effect of the interruption.13 Thus, although ensuring against low rainfall in a hydro-dominated power system does not make rain more likely to fall, ensuring against gas supply interruptions in the EU actually reduces the threat of interruption, because if such interruptions are relatively painless, then a hostile state gets little strategic benefit from interrupting or threatening to interrupt supplies. If so, then individual investments in supply security (e.g., booking more gas storage or installing dual fuel capability at gas-fired power stations) create a positive externality, and as with any such externality, there will be an incentive to free ride: consumers will spend less than is socially optimal, because they face all the costs but only a small part of the benefits (the so-called tragedy of the commons). Moving down the chain, it follows that suppliers will not face appropriate incentives to ensure security, and the market will underprovide security. Third, experience shows that in conditions of energy scarcity, regulatory or political intervention will almost certainly prevent the market from functioning efficiently.The prospect of such intervention will therefore undermine investment incentives. For example, a private investor might consider investing in a gas storage facility even if the market already appears well supplied with gas storage, on the basis that it would offer a very high return in the low-probability event that a major gas shortage leads to prolonged spikes in spot gas prices. Experience in Great Britain suggests that these spikes could involve prices many times as high as under normal conditions,14 implying spectacular returns to anyone holding gas in storage. In reality, however, the investor will be aware that many regulators or governments have arrangements in place that suspend the price mechanism in such emergencies. Such an investor will also be aware that even if those mechanisms
are not yet in place, they could be introduced at short notice, and moreover, in the absence of price controls, they would be likely to suffer retribution if they were judged to have profiteered or price-gouged during a crisis.15 Conversely, market participants will also be aware that emergency arrangements typically involve the imposition of shared pain rules, which undermine private incentives to ensure against scarcity of supply. For example, scarce gas supplies might be allocated to all suppliers on a pro rata basis related to the size of their customer load. Such an outcome would do nothing to reward the supplier who had purchased gas from a more reliable source. Examples of these two tendencies nonreliance on the price mechanism and a shared pain approachcan be found in the gas emergency cash-out arrangements in Great Britain, which suspend the market-based determination of prices for the duration of the emergency (Ofgem 2006). The European Commissions proposed new legislation on gas security of supply provides for a variety of non-market-based measures including compulsory demand reduction and forced fuel switching (European Commission 2009c). A fourth problem is a more conventional market failure: because security of supply is to some extent a public good, markets will tend to undersupply it.16 Specifically, the issue arises for natural gas and electricity because they are transmitted via networks used by many consumers, and most individual consumers cannot be remotely interrupted when supplies are tight.17 Domestic and commercial consumers generally do not have real-time metering and are not exposed to higher spot prices when supplies are scarce. There is therefore no incentive for individual consumers to ensure against supply risks, for example by paying more to purchase from a supplier that has more gas in storage.18 In particular, with electricity the absence of remote disconnection means that in the event of a blackout, all consumers will lose supply in an area, even though in general it would be possible to set a priceif all consumers were exposed to realtime pricesthat would allow demand to match
supply. An individual consumer therefore has no incentive to purchase energy from more reliable sources, as the higher levels of security are spread across all consumers. Purchasing from a more reliable source creates a positive externality but almost no private benefit (see Joskow 2007). Again, this gives rise to free riding and underprovision of security by the market.
AssessmentHow material these problems are for security of supply is a difficult empirical question, both in absolute terms and because assessment should be against a counterfactual that is based on a realistic assessment of the likely intervention that the policy process would give rise to. Nonetheless, some simple observations are in order. First, the paternalistic argument that consumers are unlikely to make wise decisions is at least plausible. Extensive research in psychology and behavioral economics shows that human beings have particular difficulty making decisions involving low-probability events that are well outside their normal range of experience (Tversky and Kahneman 1992). However, the claim that government intervention will lead to better outcomes is more contentious (apart from any other consideration, governments are made up of human beings subject to the same biases as others). Second, the argument concerning politically motivated interruptions is also at least plausible. The key question for EU policymakers to answer is how likely Russia is to interrupt gas supplies for political reasons. On the one hand, instances have already occurred where Russia has cut off oil supplies to an EU member state for essentially political reasons. On the other hand, as noted earlier, profits from Gazprom are of great importance to Russia and members of its political elite, creating a relationship of mutual dependence between Russia and the EU. Third, the combination of regulatory and market failures described above seems to imply that all but the very largest consumers are cut off from any of the upside from investing in enhanced security of supply.
Finally, whatever the objective merits, it is also clear that governments are increasingly set on intervention in this area, at EU and national levels. In the context of this book, it is therefore appropriate to ask whether, assuming that policymakers are intent on intervention, the promotion of renewable generation is the best form of intervention to address the EUs concerns about import dependence and its impact on security of supply.
able energy, with a binding target of 20% of final energy consumption; and reduce greenhouse gas emissions, with a 20% reduction relative to 1990 levels. As the table shows, the combination of measures is predicted to reduce gas imports by about a quarter, relative to business-as-usual. However, this analysis also raises a number of questions. First, it is clear that much of the reduction in gas consumption reflects the impact of energy efficiency measures that reduce total energy consumption, rather than the displacement of gasfired generation by renewables. Indeed, one can see from the table (see the Impact of new policy rows), that the predicted reduction in gas consumption induced by the new energy policy (second column) is much larger than the induced increase in renewable energy (last column). Second, although the analysis does not allow one to separate out these two effects, it is likely that the impact of renewables on gas-fired generation is materially affected by the need for continued use of gas to provide flexible backup for intermittent renewables. Recent analysis by Capros et al. (2008) suggests that the new energy policy will reduce coal-fired generation signifi-
cantly more than gas-fired, both because of the impact of carbon prices and because renewable power requires extensive support by flexible reserve power, supplied mainly by gas units. Indeed, the analysis in Table 4.2 also shows the impact of the new policy on coal (solids) to be equal to or larger than the impact on gas. Third, it is unclear which gas sources are most likely to be affected by the reduction in gas imports. If the main effect of the policy is to displace imports of LNG from relatively friendly sources, then the effect on security of supply is small. However, this is likely to be the case. LNG is often viewed as the marginal source of gas, because of the relatively high cost of bringing LNG to the EU, and also because Algerian and Russian producers are to some extent captive suppliers, given the high cost of attempting to diversify away from their European customer base.19 Finally, the analysis described above suffers from a more fundamental flaw, in that the business-as-usual counterfactual is arguably something of a straw man. A more interesting counterfactual would be a scenario with a policy that involves the promotion of all forms of low-carbon energy on a technology-neutral basis: a carbon tax or cap-and-trade scheme (in this context, the EU ETS with a tighter cap) and no policies aimed specifically at promoting the large-scale deployment of renewables.20 The effect of such a policy would be to promote some combination of energy efficiency measures, nuclear power, coal-fired generation with carbon capture and storage (CCS), and renewables. The noteworthy point here is that of those four classes of technology, renewable generationat least in the forms of wind, solar, or wave powermay well be the least suited to enhancing security of supply, because as noted earlier many renewable generation technologies are intermittent and will likely be associated with continued extensive use of gas-fired generation as backup.21 It is therefore likely that they will displace less gas-fired output than equivalent amounts of nuclear power or coal-fired generation (or investments in energy efficiency). Although increased use of nuclear power or coalfired generation would probably entail increased
imports of uranium or coal, I have argued above that no significant security-of-supply issue should arise from such imports. In conclusion, therefore, a policy that promotes low-carbon generation in general would probably be more effective in addressing gas import dependency and enhancing security of supply than the current policies that specifically promote renewable generation.
IntermittencySome of the most prominent forms of renewable generationin particular wind but also solar and wave powerare variable in output, with the level of production determined by exogenous factors such as wind speed, and also unpredictable to a lesser or greater degree. Intermittency is the term generally used to refer to this combination of variability and relative unpredictability. Two concerns arise from the intermittent nature of renewable generation. A short-run concern is the impact on system balancing ensuring that supply and demand of power are matched on a second-by-second basis. A long-run concern is whether a liberalized power market can be relied on to produce enough investment to meet the much greater need for backup generationflexible capacity that will be used primarily when demand is high and wind output is low, and whose overall utilization will therefore be comparatively low.
System BalancingThe basic physics of electric power systems requires that production and consumption22 are matched on a second-by-second basis. In any power system, a system operator (SO) is responsible for continuously ensuring this balancing. The SO has short-term control of certain generating assets, which it uses close to and in real time to correct any difference between the amounts of electricity supplied to the system and the amount being consumed. Small deviations from perfect balance take place continuously and result in fluctuations in the
frequency of AC power. Certain generating units are configured to react automatically and instantaneously to these deviations. This so-called primary reserve acts as a first line of defense against imbalances. In case of larger deviations, after the immediate response of the primary reserve, generators providing the so-called secondary reserve increase or reduce injections within seconds, following the instructions of a central device in a process known as automatic generation control. Secondary reserve is a scarce resource, because it is provided by units with specific technical capabilities. As soon as possible, therefore, typically with a lag of minutes, injections by units providing so called tertiary reserve are increased or decreased, following the instructions of the system operator, and secondary reserve capacity is restored to the pre-deviation level. In a liberalized market, the SO generally contracts with generators, and sometimes large consumers, to procure these services.23 The nature of the reserve contracts varies, but for the purpose of this chapter, it is sufficient to note that the SO will pay plants to be available to provide balancing services, as well as for the provision of the services when called on. Clearly the task of system balancing becomes more difficult the greater the changes in the levels of output, especially if those changes are unpredicted or occur with only very short notice.24 The prospect of high levels of penetration of intermittent generation therefore gives rise to concern that the job of system balancing will become more costly and less certain of success: the SO will have to purchase more balancing services, and if it fails to purchase enough, it could find itself overwhelmed by unexpectedly volatile shifts in output from intermittent generation, endangering security of supply.25
ter 2 and references therein. In brief, it is clear that significant advances have been made in the ability to forecast wind speeds and the output from wind generation, such that while high levels of penetration of wind generation may add to the cost of system operation, they need not undermine system stability. Current evidence suggests this is the case at least for penetration up to 20% (i.e., with up to 20% of electric power being generated by wind). The issue is at present less clear for other intermittent sources, and in climates with cloudy skies, solar photovoltaic (PV) power may present greater challenges, as cloud cover means the variability in output can occur over seconds rather than hours (although geographic dispersion will mitigate this to some extent). Nonetheless, Boyle argues in Chapter 2 that they can probably be dealt with in similar fashion (for more details, see also Boyle 2007). The findings of a very comprehensive survey paper by Gross et al. are consistent with this conclusion: none of the 200+ studies [we have] reviewed suggest that introducing significant levels of intermittent renewable energy generation on to the British electricity system must lead to reduced reliability of electricity supply (2006, iv). However, these conclusions do assume that advances in forecasting will be effectively incorporated into system operation procedures. Chapter 11 notes the example of Texas, where a muchdiscussed emergency occurred in 2008 following a rapid reduction in wind output. The reduction had been predicted by commercially available forecasts, but the SO had not purchased those forecasts.
Cost ImplicationsThe same survey by Gross et al. (2006) also analyzes the cost implications of intermittency, looking at how much additional reserve capacity is likely to be required and how much this is likely to cost. The authors conclude that for penetrations of intermittent renewables up to 20% of electricity supply, additional system balancing reserves due to short term (hourly) fluctuations in
wind generation amount to about 510% of installed wind capacity. Globally, most studies estimate that the associated costs are less than 5/MWh of intermittent output, in some cases substantially less. Of course, an additional cost of 5 ($7.50) per MWh is a material issue,26 but that forms part of a larger set of questions about the cost-effectiveness of renewable generation and is not really a security-of-supply issue. All of this analysis assumes, however, that the necessary reserve will be there for the SO to call on. This naturally leads back to the question of investment incentives.
The size of this requirement will clearly depend on the level of penetration of intermittent generation, the technologies involved, the specific electricity system, relevant physical features (e.g., the geographic and temporal distribution of wind), and many other factors. This has been the object of many engineering studies. For the purpose of synthesis, it is convenient to summarize any such study in terms of its estimated capacity credit, which measures how much conventional thermal generation is displaced by a unit of intermittent generation. So, for example, a capacity credit of 20% means that adding 100 megawatts (MW) of intermittent generation would allow one to retire 20 MW of conventional generation while maintaining the same overall level of system security. A comprehensive survey of these studies can be found in Gross et al. (2006), whose summary of the estimates of the capacity credit from 19 of the studies is shown in Figure 4.4. Clearly a capacity credit in the range implied by these studies would add significantly to the total capital costs of the system. With regard to security of supply, however, the concern is that a liberalized market will not have sufficient investment to provide the required level of generation capacity.
Source: Gross et al. 2006, 43 Note: the shaded area refers to UK studies
requirements of new environmental legislation. So, for example, in the EU, the cost of adding scrubbers to coal-fired units by 2015, to comply with the Large Combustion Plant Directive, would have to be recovered through future profits, and this could be difficult if utilization is expected to be very low.
If there is too much capacity, then prices might fall to a level that induces plant mothballing or early retirement. Chapter 15 indicates that this situation may be developing in Spain. Generators with zonal or regional market power might have an incentive to retire some of these plants so as to raise peak prices and the price of reserve. Incentives for early retirement may be exacerbated by the costs of refurbishments, including those necessary to meet the
possible to show that at least in theory, scarcity rents are also necessary for plants with lower marginal cost, if they are to earn a sufficient return to cover their sunk costs. It is therefore necessary that prices in those hours be sufficiently high to provide an appropriate return on capital, i.e., one that will provide the right incentive for new generation in peaking plants. Prices at times of scarcity should generally be set either by demand-side response or by actions of the system operator in its procurement of operating reserves (Hogan 2005). If those mechanisms work appropriately, then it can be shown that in theory, the level of scarcity rents will be efficient, in the sense of ensuring that generators earn their cost of capital and have appropriate incentives for new investment. However, this outcome depends on the presence of flexible scarcity pricing mechanisms and the absence of market or regulatory imperfections that limit demand-side response or distort system operator decisions. In practice, such imperfections are endemic:
The development of mechanisms to allow demand-side participation has been generally rather slow in most electricity markets, limiting the potential for demand-side response to set prices at times of scarcity. The protocols followed by many system operators at times of scarcity do not lead to the appropriate level of scarcity pricing.28 The potential for prices to depart from generators marginal costs at times of scarcity is often limited by administrative measures constraining prices, to mitigate market power or for other reasons. For example, many centrally organized markets (pools) have explicit price caps in place,29 and some limit the offer prices as a part of the ex ante market power mitigation process. In many markets, regulators monitor prices and perform ex post investigations of price spikes, with a chilling effect on scarcity pricing.
underinvestment, particularly in peaking capacity. This issue is commonly referred to as the missing money problem. On the other hand, proponents of a more market-oriented approach argue or assert that in the absence of price caps, the market can in practice be expected to provide sufficient capacity, theoretical models notwithstanding. In Great Britain, this view underlies the existing market design, the so-called New Electricity Trading Arrangements (NETA), which does not have any price caps in place. Practical experience in the decade since NETA was put in place is somewhat ambiguous. Despite many claims of imminent crisis, the lights have stayed on. However, this has been achieved with very little new investment in generation, indicating that the system may have enjoyed an overhang of excess capacity from the preceding decade. In sum, there are theoretical reasons to believe that in the absence of some form of capacity mechanism, a competitive energy-only market without efficient scarcity pricing mechanisms may underdeliver on investment in reserve capacity (i.e., in flexible units that will experience low average utilization). Although the materiality of those concerns is open to debate, it is clear that any problems would be exacerbated considerably by the much greater need for such units that comes with high levels of penetration by renewable generation. Moreover, in practice, concerns about underinvestment are likely to be well founded because of the combination of explicit price caps and the implicit threat or shadow of future price regulation in most or all liberalized markets. Even in Great Britain, which until recently was viewed as the paragon of energy market liberalization, reregulation is now being openly discussed (see, e.g., Ofgem 2010). The example of Great Britain also illustrates a deeper problem with investment incentives in the context of current energy policy, of which policy toward renewables forms only a part. The nature of the policy response to climate change, particularly in the EU, means that all forms of investment in new generation capacity are heavily influenced by government intervention. Thus renewables, nuclear, and CCS each attract technology-specific
forms of support, whereas environmental regulation in the form of the EU Emissions Trading Scheme as well as non-climate-related measures30 affect the relative and absolute returns on different technologies. Investments are thus arguably subject to very high levels of political risk, and it is by no means clear that markets are able to assess and bear these risks. In conclusion, therefore, the possibility that competitive liberalized markets will struggle to provide sufficient peaking capacity to accommodate large amounts of intermittent generation is a very real one, for a variety of reasons. The biggest factor undermining investment incentives is the high level of uncertainty and political risk, which affects all generation investments to a lesser or greater degree, except for projects that can rely on explicit and iron-clad government guarantees. The design of wholesale power markets may need to change to reflect these concerns, by providing stronger and more reliable incentives for investment, such as in the form of capacity payments or similar mechanisms. Capacity payments are widely used in the United States and have had some application in Europe (in Spain, for example, as well as in England and Wales in the 1990s) (Perekhodtsev and Blumsack 2009). These are payments to generators that are additional to the revenue they receive from the sale of energy. Different countries have taken alternative approaches toward implementing such a mechanism. In Europe, the approach generally has been for the transmission system operator (TSO) to make payments to generation on the basis of its availability to generate, recovering those payments as a surcharge on transmission tariffs. In the United States, regulators have tended to place obligations on demand-side participants to contract forward for capacity via organized capacity markets. The level of the obligation then determines the demand for capacity in those markets, and that combines with supply to determine a capacity price. In either case, the details of design (including, for example, determining the appropriate level at which to place the price, or the quantity associated with an obligation) are extensive and potentially challenging (Harvey 2005). For the purposes of this chapter, it is sufficient to note that
high penetration of intermittent generation means that a number of EU regulators are likely to be addressing those challenges in the coming years. Finally I note one caveat: the picture may be somewhat different in countries where generation investment decisions are more naturally influenced by informal ties between industry and government. For example, in Germany needed investments may take place as the outcome of informal (or at least non-contractual) agreements between government and industry, rather than being either a pure market outcome or one induced by regulatory mechanisms such as capacity payments.
ConclusionsDependence on imported gas gives rise to real, albeit hard-to-quantify, security-of-supply issues for the EU, because of geopolitical concerns around both Russia and Algeria. Those problems are particularly acute for many of the new EU member states in eastern Europe, where dependence is highest and relations with Russia are most strained. Dependence on imported coal and uranium does not give rise to such concerns, because of the number, diversity, and friendliness of potential sources. Market outcomes may not provide an efficient level of protection against the security-of-supply risks associated with gas import dependence, because of a variety of market and regulatory failures. However, the promotion of renewable generation is not the best policy response. Increased promotion of all forms of low-carbon energy (including energy efficiency) would appear to be at least as effective in enhancing security of supply, at lower overall cost. Security-of-supply concerns related to intermittency and its impact on system operation and grid stability are exaggerated. In particular, recent improvements in wind forecasting mean that even rather high levels of penetration for wind generation can safely be accommodated by an efficiently run and appropriately regulated system operator. The impact is one of cost rather than a threat to
stability. High levels of penetration of intermittent generation do, however, raise real questions about market design and security of supplyin particular, whether existing energy-only markets will provide strong enough incentives for the investment needed in peaking generation to cope with periods where high demand coincides with low intermittent output. In principle, market mechanisms are sufficient to ensure the right levels of investment. In practice, however, the absence in most EU power markets of appropriate mechanisms for scarcity pricing, combined with very high levels of regulatory uncertainty and risk, suggests that there may be a need for some form of enhanced incentive such as capacity payments that reward generation for availability, except in markets where the level of investment is strongly influenced by implicit regulation and consensus-based decisionmaking involving industry, government, and other stakeholders.
8. 9. 10.
AcknowledgmentsThanks to Luis Agosti, David Black, Godfrey Boyle, Toby Brown, Guido Cervigni, Dmitri Perekhodtsev, and Dick Schmalensee for many helpful suggestions and input. All errors and omissions are mine.
Notes1. A parallel argument is made in the EU and the United States about the benefit of renewable fuels in reducing the risks arising from dependence on imported oil for transportation. The focus of this book, however, is on renewable generation. The bulk of the remainder was from renewables (14%). Russia provides 42% of the EUs gas imports, Norway 24%, Algeria 18%, and Nigeria 5%. A more recent forecast is even more dramatic, showing a fall from 166 Mtoe in 2010 to 113 Mtoe in 2019 (ENTSOG 2009); all figures
2. 3. 4.
converted from billion cubic meters (bcm) to Mtoe using 1 bcm = 0.90 Mtoe (www.bp.com/ conversionfactors.jsp). The 390 Mtoe figure assumes an oil price of $61 per barrel (bbl). A second business-as-usual scenario has an oil price of $100/bbl and forecasts net imports of 330 Mtoe (75% of total consumption). Some observers note that the actual price paid by Ukraine is higher than the contracted price because of an agreement due to arrangements to provide free gas in exchange for delivery of gas into Ukraine. However, even allowing for the additional cost, the price remains well below European levels. See Chow and Elkind 2009. In principle, Gazprom did not cut off supplies to the EU; it reduced the level of flows by the amount of gas that previously would have been intended for Ukraine, while continuing to flow gas for transit across Ukraine to the EU. However, it was easily predictable that Ukraine would continue to consume gas, with the effect of reducing transit flows significantly. Corresponding figures are 42 years for oil and 60 years for gas (BP 2009). The corresponding figure for 1987 was 70 tcm. The U.S. Energy Information Agency (EIA 2008) has reported increases in the level of proven gas reserves as a result of the development of unconventional gas resources. The Potential Gas Committee (2009) reported an increase in reserves, (including proven, possible and speculative reserves) in 2008 to the highest level in its 44-year history. Figures converted from trillion cubic feet (tcf) to tcm using 1 tcm = 35.3 tcf (www.bp.com/ conversionfactors.jsp). Here efficiency refers to the trade-off between cost and risk. Arrangements are efficient if the additional cost of investing to increase security outweighs the additional benefit (and the saving from spending less does not justify the increased level of risk). So either supply interruptions become more probable/frequent, or society pays a higher price to avoid them, in the form, for example, of higher national security costs or unwanted changes in foreign policy to appease the potential interrupter. This argument has been used in the past to justify the requirements for strategic oil storage. For example, in 2005, a combination of factors temporarily reducing supplies to the United King-
19. 20.
dom led to price spikes of up to 500% between February 23 and March 11 (Trade and Industry Committee 2005). Some U.S. states even have legislation specifically prohibiting price-gouging. For example, Florida Statute 501.160 states that during a state of emergency, it is unlawful to sell essential commodities for an amount that grossly exceeds the average price for such commodities during the preceding 30 days. A public good is a good that is non-rivalrous and non-excludable. This means that consumption of the good by one individual does not reduce availability of the good for consumption by others, and that no one can be effectively excluded from using the good. In other words, it is not possible for the system operator to cut off supply to individual consumers, other than very large consumers (who often have interruptible supply contracts that allow for such actions). Given metering technologies currently in place, it is not possible to create such an incentive. For example, gas meters typically record only cumulative consumption, and unless they were read on a daily basiswhich would clearly be impossibly costlythere would be no way to know how much has been consumed by an individual customer on a day when supplies were particularly scarce. This is a general analysis; individual import contracts can vary significantly. Another interesting counterfactual, and one that in principle should be the starting point for the design of any intervention, would be to use taxation to correct for any security-of-supply externalities. In theory, this might lead to different levels of taxation applied to gas from different sources, with Russian gas probably incurring the highest tax. In practice, this could create difficulties with World Trade Organization (WTO) rules, and it would also raise difficult questions about the quantitative assessment of the size of the externality. A more realistic approach would be to tax all gas. However, this would also be politically difficult because of the aversion of key member states (notably the United Kingdom) to EU-level taxes, and because the United Kingdom and the Netherlands are both major gas producers. This is not to assert that intermittency per se is a security-of-supply risk (see next section), but merely to observe that all else being equal, inter-
22. 23.
26. 27.
mittent generation will displace less gas-fired generation than will non-intermittent. Clearly this would not apply to hydro generation or to biomass. The potential for new hydro is relatively limited, however, and intermittent sources (in particular wind) are forecast to be the dominant form of new installed renewable generation capacity in the coming decade at least. Including consumption in the form of losses arising from transmission and distribution. With the exception of primary reserve, whose provision is typically an obligation placed by administrative means on generators connected to the system. An important distinction must be made here between wind and solar photovoltaic (PV) power. Wind variability occurs over a matter of hours and is relatively amenable to forecasting. Except in climates with cloudless skies, solar PV can vary over seconds and is therefore more difficult to forecast. This account has greatly simplified the complexities of running an electric power system. As well as the need to match total supply with total demand, the system has a number of other technical requirements, including so-called voltage regulation, and the need to respect transmission constraints. The latter task in particular is likely to become more costly and challenging with the addition of large amounts of new intermittent generation, as discussed in a number of the case study chapters in this book. As of February 2010, this is about 5.70 ($7.75) per MWh. This is in contrast to markets where generators also receive payments for being available to generate, via capacity payment mechanisms or capacity requirements and auctions, as discussed later in this chapter. Mechanisms allowing the scarcity of operating reserves to set the price in the energy market are not in place in most EU markets. Such mechanisms require an advanced level of integration between the markets for energy and the markets for reserves, as well as between the spot and balancing markets. Market designs allowing such integration can be seen mostly in the United States. See, e.g., Kranz et al. 2003. For example, the markets in Alberta and Ontario have price caps of C$1,000 ($979) and C$2,000 ($1,958) per MWh (Adib et al, 2008), and Texas (ERCOT) has a price caps of $2,250 (ERCOT 2008). In Europe, Nordpool caps the day-ahead
price at 2,000 ($2,720) per MWh (Nordpool 2008). As discussed in Chapter 15 of this book, Spain has a very low cap of 180 ($245) per MWh, but it is not an energy-only market, as generators also receive capacity payments. 30. Notably the Large Combustion Plant Directive (Directive 2001/80/EC) and the Industrial Emissions Directive (still under negotiation in the European Parliament at the time of this writing).
ReferencesAdib, P., E. Schubert, and S. Oren. 2008. Resource Adequacy: Alternate Perspectives and Divergent Paths. In Competitive Electricity Markets: Design, Implementation, Performance, edited by F. P. Sioshansi. Oxford, UK: Elsevier, 327362 BBC. 2006. Ukraine Gas Row Hits EU Supplies. January 1. Boyle, Godfrey, ed. 2004. Renewable Energy: Power for a Sustainable Future. 2nd ed. Oxford: Oxford University Press/Open University. . 2007. Renewable Electricity and the Grid: the Challenge of Variability, London: Earthscan BP. 2009. Statistical Review of World Energy. London: BP. June. Bushnell, James. 2005. Electricity Resource Adequacy: Matching Policies and Goals. CSEM Working paper 146. www.ucei.berkeley.edu/PDF/csemwp146.pdf (accessed February 26, 2010). Capros, P., L. Mantzos, V. Papandreou, N. Tasios. 2008. Model-Based Analysis of the 2008 EU Policy Package on Climate Change and Renewables. E3MLab/ NTUA. ec.europa.eu/environment/climat/pdf/ climat_action/analysis.pdf (accessed February 26, 2010). Chow, Edward, and Jonathan Elkind. 2009. Where East Meets West: European Gas and Ukrainian Reality. Washington Quarterly 32 (1) (January): 7792. CNE (National Energy Commission). 2010. Informacin Estadstica sobre las Ventas de Energa del Rgimen Especial (Statistical Information on Special Regime Sales, January). www.cne.es/cne/ Publicaciones?id_nodo=143&accion=1&solo Ultimo=si&sIdCat=10&keyword=&auditoria=F (accessed in January 2010). Cramton, Paul and Steven Stoft. 2005. A Capacity Market That Makes Sense. Electricity Journal 18 (7): 4354. EIA (U.S. Energy Information Administration). 2008. U.S. Crude Oil, Natural Gas, and Natural Gas Liq-
uids Proved Reserves. www.eia.doe.gov/oil_gas/ natural_gas/data_publications/ crude_oil_natural_gas_reserves/cr.html (accessed February 26, 2010). ENTSOG (European Network of Transmission System Operators for Gas). 2009. European Ten Year Network Development Plan 20102019. www.entsog.eu/download/ ENTSOG_TYNDR_MAIN_23dec2009.pdf (accessed February 26, 2010). ERCOT (Electric Reliability Council of Texas). 2008. www.ercot.com/news/press_releases/2008/ pr_print_1_173287_173287 (accessed January 6, 2010). Euratom. 2008. Annual Report. Luxembourg: European Union. European Commission. 2000. Towards a European Strategy for the Security of Energy Supply. COM/2000/0769 final. http://europa.eu/ legislation_summaries/energy/ external_dimension_enlargement/l27037_en.htm (accessed February 26, 2010). . (2007). European Energy and Transport Trends to 2030 Update 2007. Luxembourg: European Commission. . 2008a. EU Security and Solidarity Action Plan. http://eur-lex.europa.eu/LexUriServ/ LexUriServ.do?uri=COM:2008:0781:FIN:EN:HTML (accessed February 26, 2010). . 2008b. Second Strategic Energy Review: Europes Current and Future Energy Position. http://europa.eu/energy/strategies/2008/ 2008_11_ser2_en.htm (accessed February 26, 2010). . 2009a. The January 2009 Gas Supply Disruption to the EU: An Assessment. Commission staff working document. http://ec.europa.eu/energy/ strategies/2009/doc/ 2009_ser2_documents_travail_service_part1_ver2.pdf (accessed February 26, 2010). . 2009b. Proposal for Regulation to Safeguard Security of Gas Supply. http://ec.europa.eu/energy/ security/gas/new_proposals_en.htm (accessed February 26, 2010). . 2009c. Proposal for Regulation to Safeguard Security of Gas Supply and Repeal Directive 2004/67/EC. http://eur-lex.europa.eu/ LexUriServ/ LexUriServ.do?uri=CELEX:52009PC0363:EN:NOT (accessed February 26, 2010). Geropoulos, Kostis. 2007. Druzhba: The Not-SoFriendly Russian Oil Pipeline. www.neurope.eu/
articles/Druzhba-The-notsofriendly-Russian-oilpipeline/73863.php (accessed February 25, 2010). Gross, Robert, Philip Heptonstall, Dennis Anderson, Tim Green, Matthew Leach, and Jim Skea. 2006. The Costs and Impacts of Intermittency:An Assessment of the Evidence on the Costs and Impacts of Intermittent Generation on the British Electricity Network. A report of the Technology and Policy Assessment Function of the UK Energy Research Centre, with financial support from the Carbon Trust. London: UK Energy Research Centre. Harvey, Scott. 2005. ICAP Systems in the Northeast: Trends and Lessons, California Independent System Operator, September 19. Hogan, William. 2005. On an Energy Only Electricity Market Design for Resource Adequacy. Cambridge, MA: John F. Kennedy School of Government, Harvard University. Holditch, Stephen. 2007. NPC Global Oil and Gas Study. Topic Paper 29. National Petroleum Council, Washington. www.npchardtruthsreport.org/ topic_papers.php (accessed February 26, 2010). IEA (International Energy Agency). 2009.World Energy Outlook 2009. Paris: IEA. Joskow, Paul. 2007. Competitive Electricity Markets and Investment in New Generating Capacity. In The New Energy Paradigm, edited by Dieter Helm. Oxford: Oxford University Press, 76122. Kranz, Bradley, Robert Pike and Eric Hirst. 2003. Intergrated Electricity Markets in NewYork. Electricity Journal 15 (2): 6465. Nordpool. 2008. Adjustment of Elspot Technical Price Ceiling in NOK and SEK. No. 98/2008. www.nordpoolspot.com/Market_Information/ Exchange-information/No9808-Adjustment-ofElspot-technical-price-ceiling-in-NOK-and-SEK/ (accessed February 19, 2010). Ofgem. 2006. Emergency ArrangementsOpen Letter. www.ofgem.gov.uk/Pages/ MoreInformation.aspx?docid=5&refer=
MARKETS/WHLMKTS/COMPANDEFF/ GASEMERG (accessed February 26, 2010). . 2010. Project Discovery: Options for Delivery Secure and Sustainable Energy Supplies. London: Ofgem. Perekhodtsev, Dmitri and Seth Blumsack. 2009. Wholesale Electricity Markets and Generators Incentives: An International Review. In International Handbook on the Economics of Energy, edited by Joanne Hunt and Lester Evans. Northampton, MA: Edward Elgar, 722768. Potential Gas Committee. 2009. Potential Gas Committee Reports Unprecedented Increase in Magnitude of U.S. Natural Gas Resource Base. Colorado School of Mines. www.mines.edu/Potential-GasCommittee-reports-unprecedented-increase-inmagnitude-of-U.S.-natural-gas-resource-base (accessed February 26, 2010). Sensfu, Frank, Mario Ragwitz, and Massimo Genoese. 2008. The Merit-Order Effect: A Detailed Analysis of the Price Effect of Renewable Electricity Generation on Spot Market Prices in Germany. Energy Policy 36: 308694. Stern, Jonathan. 2005. The Future of Russian Gas and Gazprom. Oxford: Oxford Institute of Energy Studies. Stoft, Steven. 2002. Power System Economics: Designing Markets for Electricity. New York: IEEE/Wiley. Trade and Industry Committee, House of Commons. 2005. Security of Gas Supply: First Report of Session 200506. Vol. 2, Written Evidence. London: TSO (The Stationery Office). Tversky, A. and D. Kahneman. 1992. Advance in Prospect Theory Cumulative Representations of Uncertainty, Journal of Risk and Uncertainty, 5: 297 323. World Audit. 2010. Freedom House Annual Survey Political Rights Checklist. www.worldaudit.org/ polrights.htm (accessed March 28, 2010). Yenikeyeff, Shamil Midkhatovich. 2006. The G8 and Russia: Security of Supply vs. Security of Demand? Oxford: Oxford Institute for Energy Studies.
5Market Failure and the Structure of ExternalitiesKenneth Gillingham and James Sweeney
olicy interest in renewable energy technologies has been gathering momentum for the past several decades, and increased incentives and funding for renewable energy are often described as the panacea for a variety of issues ranging from environmental quality to national security to green job creation. Sizable policies and programs have been implemented worldwide to encourage a transition from fossil-based electricity generation to renewable electricity generation, and in particular to fledgling green technologies such as wind, solar, and biofuels. The United States has a long history of policy activity in promoting renewables, including statelevel programs, such as the California Solar Initiative, which provides rebates for solar photovoltaic purchases, as well as federal programs, such as tax incentives for wind. Even in the recent stimulus package, the American Recovery and Reinvestment Act of 2009, $6 billion was allocated for renewable energy and electric transmission technology loan guarantees (U.S. Congress 2009). (See Chapter 11 for further discussion of the U.S. experience.) Moreover, such policies are not restricted to the developed world. For example, China promulgated a National Renewable Energy Law in 2005 that provides tax and other incentives for renewable energy and has succeeded in creating a burgeoning wind industry (Cherni and Kentish 2007).
Advocates of strong policy incentives for renewable energy in the United States use a variety of arguments to justify policy action, such as ending the addiction to foreign oil, addressing global climate change, or creating new technologies to increase U.S. competitiveness. However, articulation of these goals leaves open the question of whether renewable energy policy is the most sensible means to reach these goals, or even whether renewable energy policy helps meet these goals. Furthermore, many different policy instruments are possible, so one must evaluate what makes a particular policy preferable over others. Economic theory can provide guidance and more rigorous motivation for renewable energy policy, relying on analysis of the ways privately optimal choices deviate from economically efficient choices. These deviations are described as market failures and, in some cases, behavioral failures.1 Economic theory indicates that policy measures to mitigate these deviations can improve net social welfare, as long as the cost of implementing the policy is less than the gains if the deviations can be successfully mitigated. Under this perspective, policy analysis involves identifying market failures and choosing appropriate policy instruments for each. While an almost unlimited number of different possible
policy instruments can be envisioned, an analysis of relevant market failures allows us to identify which instruments are most likely to improve economic efficiency. This endeavor is complicated by the complexity of some market failures, which may vary intertemporally or geographically. This chapter explores these issues in the context of renewable energy, with a particular focus on renewable energy used for electricity generation. It first sets the stage with a brief background on the fundamental issues inherent in renewable energy. Next, it elaborates on the concepts of competitive markets and resource use, and how the deviations found in reality from the assumptions of perfect markets may result in market failures. This leads naturally to articulating the classes of possible deviations from perfect markets. A discussion follows of the use of policy instruments to help mitigate or correct for these market failures, with a particular focus on how the structure of the failure influences the appropriate policy approach.
either the availability of a substitute energy source or scarcity of the resource. The greater the rate of use relative to the size of the resource stock, the shorter the time until this ultimate depletion can be expected. These simple facts about the nature of depletable and renewable resources point to a seemingly obvious conclusion: the United States and the rest of the world will eventually have to make a transition to alternative or renewable sources of energy. However, the knowledge that the world will ultimately transition back to renewable resources is not sufficient reason for policies to promote those resources. Such transitions will happen regardless of policy, simply as a result of market incentives. The fundamental question is whether markets will lead the United States and the rest of the world to make these transitions at the appropriate speed and to the appropriate renewable resource conversions, when viewed from a social perspective. If not, then the question becomes, why not? And if markets will not motivate transitions at the appropriate speed or to the appropriate renewable supplies, the question becomes whether policy interventions can address these market failures so as to make the transitions closer to the socially optimal. The question of why not may seem clear to those who follow the policy debates. Environmental and national security concerns are foremost on the list of rationales for speeding up the transition from depletable fossil fuels to renewable energy. Recently there have also been claims that promoting new renewable technologies could allow the United States, or any country, to become more competitive on world markets or could create jobs. But much national debate often combines these rationales and fails to differentiate among the various policy options, renewable technologies, and time patterns of impacts. The rest of the chapter explores these issues in greater detail in order to disentangle and clarify the arguments for renewable energy policy.
tion renewable energy (e.g., residential solar photovoltaic investments) and energy efficiency decisions.5 These could imply an underuse of distributed generation renewable energyor an overuse of all energy sources (including renewables) if energy efficiency is underprovided. Both market failures and behavioral failures can be distinguished from market barriers, which can be defined as any disincentives to the use or adoption of a good (Jaffe et al. 2004). Market barriers include market failures and behavioral failures, but they also may include a variety of other disincentives. For example, high technology costs for renewable energy technologies can be described as a market barrier but may not be a market failure or behavioral failure. Importantly, only market barriers that are also market or behavioral failures provide a rationale based on economic efficiency for market interventions. Similarly, pecuniary externalities may occur in the renewable energy setting and also do not lead to economic inefficiency. A pecuniary externality is a cost or benefit imposed by one party on another party that operates through the changing of prices, rather than real resource effects. For instance, if food prices increase because of increased demand for biofuels, this could reduce the welfare of food purchasers. However, the food growers and processors may be better off. In this sense, pecuniary externalities may lead to wealth redistribution but do not affect economic efficiency.
externalities can be described statically. They may change over time, but the deviation has economic consequences that depend primarily on the amount of emissions released over a short time period (e.g., hours, days, weeks, or months). These may have consequences that are immediate or occur over very long time periods. Intertemporal deviations are those for which the externality consequences are based primarily on a stock that changes over time depending on the flow of the externality. The flows lead to a change in the stock over a relatively long period of time, typically measured in years, decades, or centuries. The stock can be of a pollutant (e.g., carbon dioxide) or of something economic (e.g., the stock of knowledge or of photovoltaics installed on buildings). If the flow of the externality is larger (smaller) than the natural decline rate of the stock, the stock increases (decreases) over time. Intertemporal externalities can best be described dynamically, for it is the stock (e.g., carbon dioxide), rather than the flow, that leads to the consequences (e.g., global climate change). For some environmental pollutants (e.g., smog), the natural decline of the stock is rapid perhaps over the course of hours, days, weeks, or months. For these pollutants, the stock leads to the damages, and the stock is entirely determined by the flow over this short time frame. These can be treated as atemporal deviations, as the dynamic nature of the externality is less important with such a rapid natural decline rate. For atemporal externalities, the appropriate magnitude of the intervention depends primarily on current conditions. Thus, because conditions can change over time, the appropriate magnitude could increase, decrease, or stay constant over time. For intertemporal externalities, the appropriate magnitude of the intervention depends more on the conditions prevailing over many future years than on current conditions or those at one time. As time passes, the appropriate magnitude of the intervention changes but, more predictably, based on the stock adjustment process. Therefore, the appropriate price or magnitude of the intervention will have a somewhat predictable time pattern.
cost might be virtually zero, significantly below the wage. Thus the potential net social gain from additional employment could range from nearly the entire wage to zero. Little evidence exists to suggest that additional employment in renewable energy can provide larger net social gains than any other industry, including the fossil-fuel industry. Moreover, such gains must be seen as transient possibilities in an economy such as that of the United States, which regularly is near full employment.
Environmental ExternalitiesEnvironmental externalities are the underlying motivation for much of the interest in renewable energy. The discussion here focuses on general issues in environmental externalities; specific issues inherent in intertemporal environmental externalities are addressed below in the section titled Stock-Based Environmental Externalities. Combustion of fossil fuels emits a variety of air pollutants, which are not priced without a policy intervention. Air pollutants from fossil-fuel combustion include nitrogen oxides, sulfur dioxide, particulates, and carbon dioxide. Some of these pollutants present a health hazard, either directly, as in the case of particulates, or indirectly, as in the case of ground-level ozone formed from high levels of nitrogen oxides and other chemicals. When harmful fossil-fuel emissions are not priced, the unregulated market will overuse fossil fuels and underuse substitutes, such as renewable energy resources. Similarly, if the emissions are not priced, firms will have no incentive to find technologies or processes to reduce the emissions or mitigate the external costs. The evidence for environmental externalities from fossil-fuel emissions is strong, even if estimating the precise magnitude of the externality for any given pollutant may not be trivial. In some cases, there may also be significant environmental externalities from renewable energy production, such as hydroelectric facilities that produce methane and carbon dioxide emissions from submerged vegetation, or greenhouse gas emissions and nitrogen fertilizer runoff from the production of ethanol biofuels. In many other
cases, these environmental externalities are relatively small. Whether renewable energy resources are underused or overused relative to economically efficient levels depends on which of the two environmental externalities is greater: those from fossil fuels or from the renewable energy resources. In most, but by no means all, cases, the externalities from the fossil fuels are greater, implying that the market will underprovide renewable energy. Unpriced environmental externalities from either fossil fuel or renewable energy use would imply either an overuse of energy in general or an underuse of potential energy efficiency improvements.
nalities associated with oil or natural gas consumption is more fraught with difficulties than doing so with environmental externalities, yet some analysts have suggested that the magnitude may be substantial (Bohi and Toman 1996). Others are more sanguine and believe that global energy markets can substantially buffer national security risks. In the U.S. context, natural gas and some renewable energy resources, such as biofuels, are substitutes for oil with few or no energy security externalities and thus would be underused relative to the economically efficient level. Improving the energy efficiency of vehicles and furnaces is also a substitute for oil and would also be underused. Most renewable energy resources produce electricity, so until electric vehicles are a viable largescale substitute for conventional vehicles fueled by refined oil products, national security externalities apply only indirectly to such renewable energy resources. However, these national security externalities, although indirect, can be important. For example, the production of electricity from renewables could lead to reductions in natural gas used for electricity production. This reduction would lead to more availability of natural gas for other purposes, such as heating, where it could substitute for oil in some locations. For biofuels, national security externalities are of foremost consideration. Moreover, in the European context, renewable energy directly substitutes for natural gas.
mation can play an important role (Young 2010). Information market failures are closely related to behavioral failures. Reducing information market failures would also be expected to reduce behavioral failures associated with heuristic decisionmaking. Imperfect foresight by either firms or consumers (or investors in the stock market who influence firms) suggests an inability to predict future conditions accurately, which may lead to an underestimate or overestimate of how energy prices may rise in the future. If firms systematically under- or overestimate future energy prices, then there may be an underinvestment or overinvestment in research and development (R&D) for renewable energy technologies relative to the economically efficient level. Although it certainly seems plausible that firms have imperfect foresight, it is less plausible to believe that this imperfect foresight will systematically lead to an underestimate of future energy prices, rather than random deviations that are sometimes underestimates and other times overestimates. Even if firms have imperfect foresight, as long as the firms estimates of future prices are not systematically biased, then on average investment in renewable energy technologies would still follow the economically efficient path. In this situation, errors leading to overinvestment would be balanced by those leading to underinvestment. At present, there is little evidence either for or against the hypothesis that firms systematically underestimate future price increases. Another information market failure is the classic principal-agent or split-incentive problem, which may influence renewable energy adoption in two ways. First, in many cases for rental properties, landlords make the decision about whether to invest in distributed generation renewable energy, while tenants pay the energy bills (Jaffe and Stavins 1994; Murtishaw and Sathaye 2006). Second, if landlords are not compensated for their investment decisions with higher rents, then they would tend to underinvest in distributed generation renewable energy. This market failure has been most carefully examined in the context of energy efficiency and heating (e.g., see Levinson and Niemann 2004), but the extent to which this
market failure is important for renewable energy has not yet been empirically examined. Finally, there may be a principal-agent problem relating to managerial incentives. In many cases, managers have their compensation tied to the current stock price, rather than the long-term performance of the company (Rappaport 1978). However, investors may have difficulty distinguishing between managerial decisions that boost short-term profits at the expense of long-term profits and those that boost both short- and longterm profits. In the context of renewable energy, the emphasis on short-term performance may lead to underinvestment in R&D for renewable energy technologies, for the benefits of developing such technologies are likely to be received over the long term, while the costs are borne in the short term. Of course, this issue may occur in any industry and is not unique to renewable energy resources.
Regulatory FailuresIn some cases, the regulatory structure can create perverse incentives. For example, average cost pricing of electricity implies that consumers often face a price of electricity that does not reflect the marginal cost of providing electricity at any given time. This may influence the adoption of distributed generation renewables, such as residential solar photovoltaic (PV) systems. In many locations, electricity output from a solar PV unit tends to be higher during the day, corresponding to times of high electricity demand. To the extent that the solar PV output is correlated with high wholesale electricity prices, consumers and firms deciding whether to install a new solar PV unit will undervalue solar PV absent tariffs that account for the time variation. Borenstein (2008) quantifies this effect in California, finding that solar is currently undervalued by 0% to 20% under the current regulatory framework, and that this could rise to 30% to 50% if the electricity system were managed with more reliance on price-responsive demand and peaking prices, because solar output would be concentrated at times with even higher value.
Economies of ScaleEconomies of scale, particularly increasing returns to scale, refers to a situation where the average cost of producing a unit decreases as the rate of output at any given time increases, resulting from a nonconvexity in the production function for any number of reasons, including fixed costs. This issue may inefficiently result in a zero-output equilibrium only when we have market-scale increasing returns, where the slope of the average cost function is more negative than the slope of the demand function, and the firm cannot overcome the nonconvexity on its own. Market-scale increasing returns refer to a nonconvex production function at output levels comparable with market demand. Figure 5.1 graphically illustrates the second condition. If the quantity produced is small (e.g., quantity a), then no profit-seeking firm would be willing to produce the product, but if production could be increased to some level above the crossing point (e.g., at the quantity b), then it would be profitable for the firm to produce: price would exceed average cost. Usually a firm could overcome the situation in Figure 5.1 on its own simply by selling at a low price. Even if this is a risky endeavor, it is not likely that all firms would ignore this opportunity. However, firms may not be able to take advantage
of the opportunity because of capital constraints or a simultaneous coordination problem. Capital constraints may be a problem only if the aggregate investment required is extremely large; otherwise, it is likely that some firm could be expected to raise the necessary capital. Capital constraints facing an economy, as occurred in the 20082009 recession, could limit such capital investments for an entire economy. Because such events tend to be transient, however, these constraints at most could be expected to delay the investments. Often economies of scale are accompanied by a chicken-and-egg problem, wherein multiple actors must simultaneously invest and ramp up production in order to commercialize a new technology. This may be most relevant in technologies that require a new infrastructure, such as hydrogen-fueled vehicles, which may or may not use renewable energy depending on the hydrogen generation source. Such possibilities require interindustry cooperation and thus may greatly delay investments. Similar chicken-and-egg problems have been overcome in the past, as with personal computers, operating systems, and application software or automobiles, gasoline, service stations, and roads, but these problems greatly complicate investments. It should be noted that the equilibrium that would occur with market-scale increasing returns would unlikely be a workable competitive equilibrium, but rather a single-firm monopolistic equilibrium. In fact, the situation of market-scale increasing returns is often referred to as a natural monopoly. This situation raises the possibility of market power.
Market PowerDemand
Average cost
Figure 5.1. Economies of scale: slope of average cost function is more negative than slope of demand function
Uncompetitive behavior may influence the adoption of renewable energy technologies in several ways. First, market power in substitutes for renewable energy can influence the provision of renewable energy through two channels. Firms effectively exercising market power in substitutes for renewable energy (e.g., at times the OPEC cartel) would raise the price of energy above the economically efficient level, making investment in
renewable energy more profitable and leading to an overinvestment in renewable energy. On the other hand, firms that have market power in substitutes for renewable energy may have an incentive to buy out fledgling renewable energy technologies to reduce competitive pressuresleading to a possible underprovision of renewable energy resources if that purchasing firm buries the renewable technology. However, the prospect of being bought by a competitor could provide a strong incentive for a new firm to be created with the explicit intention of selling itself to a larger company. Which effect dominates and whether there is market power in substitutes for renewable energy can be determined only empirically. Market power may also influence the adoption of renewable energy resources by influencing the rate and direction of technological change. If less competition exists in a market, firms are more likely to be able to fully capture the benefits of their innovations, so incentives to innovate are higher (e.g., see Blundell et al. 1999; and Nickell 1996). Conversely, if more competition exists, firms may have an incentive to try to escape competition by investing in innovations that allow them to differentiate their product or find a patentable product. Some evidence suggests that the relationship between competition and innovation may be an inverted U-shaped curve, with a positive relationship at low levels of competition and a negative relationship at higher levels of competition (Aghion et al. 2005; Scherer 1967). This relationship likely holds in all industries, not just the renewable energy industry. Finally, in some cases, vertically integrated utilities may effectively exercise market power by favoring their own electricity generation facilities over other small generation facilities, including renewable energy facilities. This was a concern for the implementation of renewables when utilities invested mostly in nonrenewable energy, but utilities now typically invest in renewable energy along with conventional generation plants.8
externalities. A second intertemporal deviation may occur if an imperfect capture of the stock of knowledge is created as a result of current actions, leading to underinvestment or underproduction of those activities that lead to growth of the knowledge stock. These can occur with knowledge generation processes, such as learning by doing or research and development; market diffusion of a new technology; or network externalities. Intuitively, when others can capture some of the benefits from the choice made by a firm or consumer, the uncaptured benefits will be socially valuable but will not be taken into account by the firm or consumer.
cost of renewable energy R&D may be quite high (Pizer and Popp 2008). Empirical work suggests that additional R&D investment in renewable energy will at least partly displace R&D in other sectors. Popp (2006) finds that approximately half of the energy R&D spending in the 1970s and 1980s displaced, or crowded out, R&D in other sectors. Part of the rationale for this may be that years of training are required to become a competent research scientist or engineer, and therefore the supply of research scientists and engineers is, at least in the short term, relatively inelastic. In the longer term, crowding out is less likely to be an issue, as universities train more scientists and engineers. Learning by doing A similar intertemporal market imperfection due to a knowledge stock spillover may also occur if there is a significant learning-by-doing (LBD) effect that cannot be captured by the firm. LBD has a long history in economics, dating back to Arrow (1962). The basic idea behind LBD is that the cost of producing a good declines with the cumulative production of the good, corresponding to the firm learning about how to produce the good better.10 One interpretation is that with LBD, the cost is dependent on the stock of knowledge, which is proxied by the stock of cumulative past production. In the standard model of LBD, the firm today bears the up-front cost of producing an additional unit and thereby also increasing the knowledge stock, while all firms in the industry benefit from the increased stock of knowledge, leading to reduced costs in the future for all firmsan intertemporal spillover. Importantly, LBD alone does not necessarily imply a market failure. In some situations, one could imagine that all knowledge leading to cost reductions could be used only by the single firm making the decision. In this special case, there are no spillovers, and the firm would have the incentive to produce optimally, weighing the up-front cost of learning against the benefits of the cost reductions in the future as it would any investment decision.11 Outside of this special case, the existence of LBD can represent an externality with the poten-
tial to be an important market imperfection in renewable energy markets. There is little or no empirical evidence on the degree of spillovers from LBD, but ample evidence exists that the cost of several important renewable energy technologies tends to decline as cumulative production increases (Jamasb 2007). This evidence alone does not prove the existence of a market failure, for other factors may also be able to explain the cost decreases (e.g., R&D or even time-dependent autonomous cost decreases). The magnitude of a LBD market failure is specific to each technology, and each has to be assessed on a case-by-case basis. Moreover, much like R&D spillovers, LBD spillovers are not unique to renewable energy technologies, but may also be present in any number of fledgling technologies as they diffuse into the market. Hence both R&D and LBD spillovers can be considered broader innovation market failures that lead to underinvestment in or underproduction of certain renewable energy resources. Network externalities Network externalities occur when the utility an individual user derives from a product increases with the number of other users of that product. The externality stems from the spillover one users consumption of the product has on others, so that the magnitude of the externality is a function of the total number of adoptions of the product. Often quoted examples of network externalities include the introduction of the QWERTY typewriter keyboard, telephone, and fax machine (David 1985). An important caveat about network externalities is that the externality may already be internalized. For example, the owner of the network may recognize the network effects and take them into account in his or her decisionmaking (Liebowitz and Margolis 1994). Alternatively, in some cases, the recipients of the network spillover may be able to compensate the provider. For example, for network effects in home computer adoption, the new adopter might take the previous adopter to lunch as thanks for teaching him or her how to use the computer (Goolsbee and Klenow 2002). When the externality is already internalized, net-
work externalities are more appropriately titled network effects or peer effects and do not lead to market failures (Liebowitz and Margolis 1994). In the context of renewable energy, network externalities may play a role in the adoption of distributed generation. This may come about if consumers believe that installing renewable energy systems on their homes sends a message to their neighbors that they are environmentally consciousand that more installations in the neighborhood will increase this image motivation or snob effect. Evidence for this effect has been shown in Sacramento for solar panels (Lessem and Vaughn 2009). Little evidence is available to indicate whether this is truly a network externality or just network effects in distributed generation renewable energy.
Policy InstrumentsEach of the failures described above can provide motivation for policy to correct the failure, but it is not always a simple task to appropriately match the policy to the failure. Table 5.1 lists some of the more common classes of policy instruments available to address failures relevant to renewable energy. This table is meant to be illustrative, as there exist an almost uncountable variety of different policy instruments. How do we choose among the policy instruments? Economic theory along with careful analysis can provide guidance. First, both theory and evidence indicate that multiple market failures will likely require multiple interventions, so a sensible policy goal involves matching the most appropriate intervention to the failure (Aldy et al. 2009; Goulder and Schneider 1999). In some cases, several policy instruments can address, or partly address, a given market failure. In these cases, if economic efficiency is the goal, the combination of policy instruments that provides the greatest net benefits should be chosen. In addition, many of the market failures relevant to renewable energy are broader market failures that may apply to a wide range of markets or technologies. Therefore, economic efficiency would
R&D tax incentives Instruments to correct market prices: excise taxes, cap-and-trade, subsidies Feed-in tariffs Information programs Product standards Marketable marketwide standards: renewable portfolio standards, low-carbon fuel standards, corporate average fuel, economy standards Transparency rules Macroeconomic policy Corporate taxation reform Competition policy/laws Restructured regulation Intellectual property laws
be further enhanced if the interventions to address these market failures were not focused solely on renewable energy. Several concerns warrant careful attention in this matching process. First, we care about how effective the intervention will be at actually correcting the market failure. Second, the benefits from the intervention must be weighed against the costs of implementing the policy, including both government administrative costs and individual compliance coststaking into account the risk of poor policy design or implementation. In addition, careful consideration of any equity or distributional consequences of the intervention is important, both for ethical reasons and for gaining the political support for passage of the policy. Uncertainty about the magnitude of the market failure and the effectiveness of the interventions is another important concern. In some cases,
potential damages from a market failure may be large enough that the most sensible intervention is direct regulation, so that we can be certain the risk is mitigated. For example, if a toxin is deemed to have sufficiently high damages with a high enough probability, it may be sensible for the government to simply ban it. A comprehensive analysis of the costs and benefits of different policy options that explicitly includes uncertainty, in this case could be expected to reveal the need for simply banning the toxin. Finally, the temporal structure of the market failures may have a profound influence on the temporal structure of optimal intervention. Economic theory suggests that not only should an intervention be matched to the failure, but also the temporal pattern of the intervention should be matched to the temporal pattern of the failure. For example, the optimal correction for failures
that decrease in magnitude and eventually vanish over time would be a transient intervention. Table 5.2 summarizes the matching, with the various market failures listed as rows and policy instruments from Table 5.1 as the columns. For those instruments that appear to be potentially well matched to the market failures, the letters P and T indicate whether the instrument could be expected to be permanent or transient. Of course, the particular circumstances of each market failure and the potential policy must be assessed. Some potential policies may be useful only under limited circumstances, and the evidence for some market failures in renewable energy is much weaker than others. Moreover, some of the policy
options listed may be reasonably well matched with a market failure but may be second best to other policy options.
Table 5.2. Sources of market failure and some illustrative potential policy instruments
ons, such as X pr ze
Labor supply/demand imbalances Environmental externali es Na onal security externali es Informa on market failures Regulatory failures Too high discount rates Imperfect foresight Economies of scale Market power R&D spillovers Learning by doing Network externali es
Restructured regu a on
Macroeconom c po cy
Produc on subs d es
Transparency ru es
Product standards
D rect regu a on
Feed- n tar s
Exc se taxes
nforma on programs
on po cy/ aws
fee, with the optimal tax set at the magnitude of the externality. Or a cap-and-trade system could impose a marketwide limit on the emissions, in which case trading of the allowances under a capand-trade system would lead to a market-clearing price for the allowances. The cap should be set so that the resulting permit price is equal to the magnitude of the externality.13 The magnitude of the externality can be estimated based on damage estimates from scientific and economic literature. In some cases, the risk from particularly severe pollutants (e.g., possibly some criterion air pollutants) may be sufficiently high that the marginal damage associated with the release of the pollutant would always exceed the economic costs of reducing that pollutionimplying that direct regulation could be an economically efficient policy. Direct regulation would entail the government setting strict limits on the amount of the severe pollutant emitted or, in some cases, possibly even banning emission of the pollutant entirely. Environmental externalities from renewable energy can be treated the same way as environmental externalities from fossil-fuel combustion. For most renewables, the environmental externalities are small, so the appropriate tax would be small. A few, such as corn-based ethanol and palm oil biodiesel, may have significant emissions of some pollutants, and the damages from these should be added to the price of the resource. A second tax or subsidy approach to addressing environmental externalities more closely follows the policies in many countries. Rather than putting a price on both fossil fuel and renewable energy generation corresponding to the magnitude of each externality, the same cost differential could be maintained by subsidizing low-emitting resources and not subsidizing (or taxing) highemitting resources. However, this approach would have the unintended consequence of making energy use less expensive than its actual social cost, because the external costs would remain unpriced. An additional subsidy on energy efficiency investment can correct for the overuse of energy, removing the primary distortion in energy markets. Unfortunately, this may still lead to a distortion through an overinvestment in the subsidized energy-efficient technologies, because
the optimal choice may have involved more energy conservation and less investment in energy-efficient technologies.14 In addition, such a combination of subsidies may lead to further distortions in factor markets, such as markets for the inputs in the production of energy efficiency equipment. Thus the economic theory suggests that the first-best approach to addressing environmental external damages is through taxes or permits, and the subsidy approach outlined above can be considered a second-best approach to be pursued if the first-best approach is not politically feasible. Other approaches rely on the idea that if firms must clearly disclose their environmental impacts, they will be motivated to reduce those impacts, and consumers will be motivated to shift their purchases away from damaging products and toward those that are environmentally benign. Information programs designed to publicize the environmentally damaging product or transparency rules designed to document and communicate the environmental damages are prompted by this idea. Enterprise software available from companies such as Hara Software15 have made it possible to document and broadly communicate carbon dioxide and other environmental impacts in a transparent manner.
Just as for environmental externalities, a second approach would be based on maintaining a price differential between fuels with high and low national security external costs. This approach would face the same issues: overuse of fuel with high external costs and overuse of energy in general. Policies to subsidize energy efficiency would help but may come at the cost of distortions through overinvestment in energy efficiency or overconsumption in some factor markets. Other policy instruments may also improve economic efficiency by reducing consumption of oil, such as product standards (e.g., for fuel economy), but these approaches inherently lead to additional economic distortions and thus are also not a first-best approach. For example, fuel economy standards lower the effective cost per mile of driving and thus induce more driving, a reaction known as the rebound effect. The additional driving may increase the use of oil, reducing the energy security (and environmental) benefits and at the same time increasing the external costs from accidents and congestion.
knowledge, such as of future probable energy prices, an obvious intervention is for the agency to share that information. The Energy Information Administration of the U.S. Department of Energy provides exactly such data and projections accessible to anyone. In fact, given the ability for a government agency to share information broadly,16 and at low cost, it is very unlikely that imperfect foresight about future energy conditions would provide a strong case for other governmental interventions. In cases when information is particularly difficult to process or a principal-agent issue exists, consumers may be unable to make informed decisions, suggesting that the government can improve economic efficiency by using its superior information-processing ability to make sensible choices.This reasoning underlies appliance energy efficiency standards and may perhaps pertain to distributed generation renewables in limited cases. If managerial incentives are misaligned because of the imperfect knowledge of stock market investors, accounting and information rules to promote transparency and a clearer flow of information may be warranted. These accounting and information rules are not specific to renewable energy and may also improve economic efficiency in general. In addition, if the managers of some firms take a short-term perspective and underinvest in renewable energy, then other firms with a longerterm perspective may invest more to take advantage of the long-term profit opportunities. If other firms with a longer-term perspective do not step in, then there may be motivation for public support for R&D, either through public R&D or subsidies for private R&D. This may not be a very likely outcome, but it could occur in the presence of behavioral failures on the part of stock market investors that lead to a systematic bias toward rewarding short-term performance.
real-time pricing (RTP) of electricity at the wholesale level could be expanded to the retail level.17 However, the benefits of RTP or time-ofuse (TOU) pricing would have to be weighed against the technology and implementation costs, however.
changes over time. Individuals influence these stocks only indirectly, by altering the flows into or out of the stock. But once the flow is determined, those individuals have no further control of the stock. For that reason, policy instruments cannot be directed toward the stock but must be directed toward influencing the flow. For economic efficiency, the strength of the incentives must be guided by the intertemporal nature of the stock externality and can be determined from the discounted net present value of the entire flow of future impacts.
atmosphere. This fact would slightly increase the rate of growth of the optimal carbon price. The magnitude of the damages from carbon dioxide is controversial, and estimates will improve with increased scientific knowledge. Different time patterns of damages would lead to different time patterns of the carbon price, based on the three points discussed above. For example, if the damages from an additional metric ton of carbon dioxide grow (in real terms) at the social discount rate, then the optimal carbon price will also grow (in real terms) at approximately the social discount rate. Under the unlikely assumption that the incremental damages are constant in real terms into the future, we could find a nearly constant (in real terms) economically efficient carbon price.
cases, little R&D would have been undertaken by firms relative to the economically efficient amount, so public R&D complements private R&D. On the other hand, public R&D can crowd out the private, depending on the nature of the R&D. For example, pure science public R&D would be much less likely to crowd out private R&D than would demonstration projects. The empirical evidence on public R&D is not clearcut. David et al. (2000) review the empirical evidence on whether public R&D complements or crowds out private R&D and find an ambiguous result, suggesting that the result is situationdependent and underscoring the importance of the nature of the R&D in the social rate of return of public investment. Intellectual property law plays a key role in how well firms can capture the rents from their innovative activity. Determining the direction in which to change intellectual property law is not simple. If intellectual property law is tightened (e.g., by increasing the length of time patents hold force), then two opposing effects would exist. Firms could capture more of the benefits of R&D and thus would have a greater incentive to invest in it. But fewer spillover benefits may result from the R&D activity, so the social rate of return from the activity would be lower. Little empirical evidence is available to suggest that either a tightening or loosening of intellectual property law would increase economic efficiency. LBD If learning by doing occurs in the production of a new technology (e.g., solar photovoltaic installations), then the act of producing increases the firms stock of cumulative experience and thus leads to declines in future costs. The stock of cumulative experience grows when insights from previous production by that or another firm allows it to improve its production techniques. The stock also may decline if some of these techniques are forgotten. Theories of LBD often proxy all of these complex dynamics by postulating that the cost of future production for all firms at any time will be a function of the cumulative stock of experience from production in the market. But the market failure can be thought of in a
more general sense as a spillover from the stock of any single firms cumulative experience from production to other firms. Once a firm chooses how much to produce at any given time (i.e., the flow into the stock of experience), it subsequently has no further control over the stock of experience and its impact on future costs. Thus economically efficient policies for LBD must focus on the quantity produced, while taking into account the fact that experience is a stock. The most straightforward policy instrument to address LBD spillovers is a subsidy. The economically efficient per-unit subsidy equals the discounted present value of all future cost reductions resulting from the additional production that cannot be captured by the individual firm. However, a second element to the economic theory behind LBD also may affect the economically efficient policy: the spillovers from LBD are postulated to decline along with the costs. For example, in the standard formulation, as illustrated in Figure 5.2, the percentage cost decrease depends on the percentage increase in the stock, so every additional unit has a progressively decreasing percentage impact on costs. Consequently, as cost decreases with a greater stock, a given percentage cost decrease leads to a smaller absolute cost decrease. These factors together imply that the LBD externalityand hence the
appropriate magnitude of the interventionwill be declining over time. Acting in the opposite direction, if the sales are growing rapidly, the cost reduction is applied to a larger amount of production, reducing the rate of decline of the intervention. Thus optimal subsidies for LBD will likely be transient and decline over time as LBD runs its course. The speed at which the subsidies are phased out will depend on the particular technology and may require adjustment if different conditions arise than were initially expected. In one example, the optimal solar PV subsidies for California calculated under the baseline assumptions in van Benthem et al. (2008) follow a declining path and are phased out over 15 years. Network externalities Network externalities may play a role in the adoption of distributed generation renewable energy. If it can be demonstrated that a network externality truly exists, rather than network effects, then one approach to correct for this externality would be a temporary production subsidy (Goolsbee and Klenow 2002). Once a product has taken over (nearly) the entire market, there would be no room for further spillovers and thus no need for the subsidy policy.
Figure 5.2. Illustrative incremental benefits from additional cumulative installations: LBD
ConclusionsRenewable energy has an immense potential to serve our energy needs, and in the long run, a transition from depletable fossil fuel resources to renewable energy is inevitable. This chapter has delved into reasons why policymakers should be interested in policies to promote renewable energy, pointing to a variety of market failures that may lead to a divergence between the optimal transition to renewables and the observed transition. Economic theory suggests that we can improve economic efficiency by matching the policy instrument to the market failure. The structure and nature of each market failure have important ramifications for the appropriate policy actions to correct for the market failure and move closer to an optimal transition to renewable energy. The discussion above distinguished between atemporal market failures and intertemporal (i.e., stock-based) market failures. In either case, the economically efficient policy action matches the temporal pattern of the market failure. This implies a temporary policy (e.g., LBD spillovers) in some cases and a permanent policy (e.g., R&D spillovers) in others. Renewable energy policy is likely to require several different policy instruments to address the various kinds of market failures. When the market failures are closely related, a single policy instrument can address, or partly address, more than one market failure. For instance, provision of information about low-cost or low-effort opportunities to save energy and help preserve the environment may reduce the informational market failure and also influence consumers to partly internalize the environmental externalities (Bennear and Stavins 2007). For renewable energy, the most important market failures, with the strongest empirical evidence, appear to be environmental externalities, innovation market failures, national security market failures, and regulatory failures. Only a few of the market failures identified in this chapter are unique to renewable energy. Environmental externalities due to fossil-fuel use are the most important of these, but if policy action is already under way to correct for externalities from fossil-
fuel emissions, then we must look to other market failures for motivation for renewable energy policy. As these other market failures often apply to other parts of the economy, addressing them may entail policy actions that extend much beyond renewable energy. Political feasibility is a final consideration with important ramifications for renewable energy policy. In some cases, the first-best policy approach may not be politically feasible. A second-best approach may involve multiple instruments, even in cases when the first-best approach involved only a single instrument. For example, rather than a single tax to internalize environmental externalities, the same price differential can be achieved by combining a smaller tax (or no tax) on fossil fuels with a subsidy for renewable energy. Similarly, a cap-and-trade system may not be politically feasible because of uncertainty about how high the costs of abatement might be, so a more viable option might be to use two instruments in a hybrid cap-and-trade and tax system, commonly known as a cap-andtrade with a safety valve (Jacoby and Ellerman 2004; McKibbin and Wilcoxen 2002; Pizer 2002). In other cases, the only politically feasible options for addressing the market failures relevant to renewable energy are not the first-choice instruments, but rather second-choice instruments that address the market failures indirectly. Renewable portfolio standards (RPSs) are one of the most prominent examples of a policy instrument that only indirectly addresses the market failures relevant to renewable energy. By setting a requirement on the amount of renewable energy in each utilitys electricity generation mix, an RPS adds an implicit subsidy on renewable energy, with the magnitude of the subsidy directly related to the stringency of the cap. If the RPS is carefully set, this implicit subsidy could act just like an appropriately set actual subsidyleading to the second-best outcome described above. However, finding this appropriate level for an RPS may be exceedingly difficult and subject to intense political disputes. A sensible set of policies to address the market failures relevant to renewable energy has the potential to greatly improve economic effi-
ciencyand at the same time would have other benefits, such as improving air quality and mitigating the risk of catastrophic global climate change. Much future work remains to better quantify the most relevant market failures and further improve our understanding of how to develop policies to best address these market failures.
AcknowledgmentsThe authors thank Richard Schmalensee, Boaz Moselle, Arthur van Benthem, Douglas Hannah, and Jonathan Leaver for very helpful comments. Any remaining errors are the sole responsibility of the authors.
Notes1. The concept of behavioral failures stems from behavioral economics and is quite new to environmental economics. See Shogren and Taylor (2008) and Gillingham et al. (2009) for recent reviews discussing the concept in the context of environmental economics. Economic theory defines economically efficient in technical terms as an allocation of resources where no potential Pareto improvement exists, which refers to a reallocation of resources that benefits at least one individual and imposes no costs on any others. Note that economic efficiency is a distinct concept from the equity or fairness of an allocation of resources. It is still theoretically unclear how to disentangle systematic biases in decisionmaking from inherent preferences, but behavioral welfare analysis is an area of active theoretical development and may eventually shed light on this issue. See, e.g., Bernheim and Rangel (2009). Important equity or fairness concerns may also be involved. This chapter focuses on economic efficiency as a policy goal, while noting that equity considerations, in theory, can often be dealt with through lump-sum transfers of wealth that do not distort incentives or through modifications of the income tax rates. If the policy goal is reducing
global inequity, other distributional policies are likely to be more effective than renewable energy policy. 5. It is important to note that unless a behavioral failure is a systematic, rather than random, departure of observed choice from a theoretical optimum, it may be very difficult to formulate policies. If the systematic departure is in a consistent direction, the intervention can work in the opposite direction to correct this deviation. But random deviations would require an intervention contingent on the deviation. For example, poor information about the operating characteristics of distributed photovoltaics could lead some people to install these devices even though they would ultimately come to regret the decision and other people not to install them even though the devices would have turned out to be beneficial. In such circumstances, development and dissemination of information about photovoltaic operating characteristics for alternative locations could improve such decisions. For the most part, however, policy options designed to compensate for random deviations would be difficult to formulate and effectively implement. 6. Full employment for a well-functioning developed economy refers here to at the natural rate of unemployment. Some unemployment will always exist as a result of transitions between jobs and mismatches of available and needed skills. 7. Personal income taxes or labor taxes, such as the U.S. Social Security tax, provide incentives to reduce the supply of labor, so that the marginal social value of labor exceeds the value of that labor to the worker. However, issues of the distortions associated with and the reform of such tax systems go well beyond the scope of this chapter. 8. This remains a concern for the overall economic efficiency of investment, even when it does not distort the mix of renewables versus nonrenewable technologies. 9. Almost indifferent because the cumulative impacts of emitting a ton now may be somewhat different from the impacts of emitting a ton in 20 years and because the regulatory environment could change in that period. 10. LBD is closely related to economies of scale, except that learning by doing has a distinctly different intertemporal relationship, where costs decline as a function of cumulative production, and increasing returns to scale implies that average costs decline with production at a given time.
11. If the knowledge leading to cost reductions by one firm could have been used by other firms, but that firm somehow manages to keep all the knowledge private, even if it does not use it (so it is effectively wasted knowledge), there would still be a market failure in that some of the potential benefits of the learning would not be captured by anyone. 12. As noted above, this does not deal with the labor market problems associated with income taxes or labor taxes that provide incentives to reduce the supply of labor. 13. A substantial literature addresses the trade-offs between a tax and a cap-and-trade system, particularly relating to policymaking under uncertainty. For a recent review discussing these issues, see Aldy et al. (2009). 14. If behavioral failures have caused an underinvestment in energy efficiency, then there may not be an overinvestment in energy efficiency from the subsidy. 15. Information available at www.hara.com. 16. In some cases, release of information is not possible or is undesirable, such as when it involves weapons programs or other programs closely related to national security. 17. In order to be effective, RTP would have to be complemented with real-time feedback on the electricity price at the current time. 18. Note that reducing taxes in some areas may require increasing taxes in others, so a full analysis should examine the relative distortions from each of the different taxes. 19. For example, in the United States, the Public Utility Regulatory Policies Act (PURPA) of 1978 required electric utilities to buy power from smallscale nonutility producers at the avoided cost rate, which is the cost the utility would incur were it to acquire the electricity from other sources. Choosing the appropriate price turned out to be remarkably problematic. 20. For instance, the 2020 efficient tax would be greater than the 2010 efficient tax by a factor of (1 + r)10, where r is the annual social discount rate. At a 5% discount rate, the 2020 carbon tax would be 63% greater than the 2010 carbon tax.
ReferencesAghion, P., N. Bloom, R. Blundell, R. Griffith, and P. Howitt. 2005. Competition and Innovation: An Inverted-U Relationship. Quarterly Journal of Economics 120: 701728.
Aldy, J., A. Krupnick, R. Newell, I. Parry, and W. Pizer. 2009. Designing Climate Mitigation Policy. Discussion paper 0816. Washington, DC: Resources for the Future. Archer, C., and M. Jacobson. 2005. Evaluation of Global Wind Power. Journal of Geophysical Research D 110: 120. Arrow, K. 1962. The Economic Implications of Learning by Doing. Review of Economic Studies 29: 155 173. Baumol, W. 1972. On Taxation and the Control of Externalities. American Economic Review 62: 307322. Bennear, L., and R. Stavins. 2007. Second-Best Theory and the Use of Multiple Policy Instruments. Environmental and Resource Economics 37: 111129. Bernheim, D., and A. Rangel. 2009. Beyond Revealed Preference: Choice-Theoretic Foundations for Behavioral Welfare Economics. Quarterly Journal of Economics 124: 51104. Blundell, R., R. Griffith, and J. Van Reenen. 1999. Market Share, Market Value and Innovation in a Panel of British Manufacturing Firms. Review of Economic Studies 66: 529554. Bohi, D., and M. Toman. 1996. Economics of Energy Security. Norwell, MA: Kluwer Academic Publishers. Borenstein, S. 2008. The Market Value and the Cost of Solar Photovoltaic Electricity Production. CSEM Working paper 176. Berkeley, CA: University of California Energy Institute. Cherni, J., and J. Kentish. 2007. Renewable Energy Policy and Electricity Market Reforms in China. Energy Policy 35: 36163629. Da Rosa, A. 2005. Fundamentals of Renewable Energy Processes. Amsterdam; Elsevier Academic Press. David, P. 1985. Clio and the Economics of QWERTY. American Economic Review 75: 332337. David, P., B. Hall, and A. Toole. 2000. Is Public R&D a Complement or Substitute for Private R&D? Research Policy 29: 497529. EIA (Energy Information Administration). 2008. International Energy Annual 2006. Washington, DC: Department of Energy, EIA. Gillingham, K., R. Newell, and K. Palmer. 2009. Energy Efficiency Economics and Policy. Annual Review of Resource Economics 1: 597619. Goolsbee, A., and P. Klenow. 2002. Evidence on Learning and Network Externalities in the Diffusion of Home Computers. Journal of Law and Economics 35: 317343.
Goulder, L., and S. Schneider. 1999. Induced Technological Change and the Attractiveness of CO2 Emissions Abatement Policies. Resource and Energy Economics 21: 211253. Heal, G. 1993. The Optimal Use of Exhaustible Resources. In Handbook of Natural Resource and Energy Economics, Vol. 3, edited by A. Kneese and J. Sweeney. Amsterdam: Elsevier. Hermann, W. 2006. Quantifying Global Exergy Resources. Energy 31: 16851702. Jacoby, H., and A. D. Ellerman. 2004. The Safety Valve and Climate Policy. Energy Policy 32: 481491. Jaffe, A., R. Newell, and R. Stavins. 2004. The Economics of Energy Efficiency. In Encyclopedia of Energy, edited by C. Cleveland. Amsterdam: Elsevier, 7990. Jaffe, A., and R. Stavins. 1994. The Energy Paradox and the Diffusion of Conservation Technology. Resource and Energy Economics 16: 91122. Jamasb, T. 2007. Technical Change Theory and Learning Curves: Patterns of Progress in Electricity Generation Technologies. Energy Journal 28: 5171. Lessem, N., and R. Vaughn. 2009. Image Motivation in Green Consumption. Working paper. Los Angeles: University of CaliforniaLos Angeles. Levinson, A., and S. Niemann. 2004. Energy Use by ApartmentTenants When Landlords Pay for Utilities. Resource and Energy Economics 26: 5175. Liebowitz, S., and S. Margolis. 1994. Network Externality: An Uncommon Tragedy. Journal of Economic Perspectives 8: 133150. McKibbin, W., and P. Wilcoxen. 2002. The Role of Economics in Climate Change Policy. Journal of Economic Perspectives 16: 107129. Murtishaw, S., and J. Sathaye. 2006. Quantifying the Effect of the Principal-Agent Problem on US Residential
Use. LBNL-59773. Berkeley, CA: Lawrence Berkeley National Laboratory. Nickell, S. 1996. Competition and Corporate Performance. Journal of Political Economy 104: 724746. Nordhaus, W. 2002. Modeling Induced Innovation in Climate Change Policy. In Technological Change and the Environment, edited by A. Grubler, N. Nakicenovic, and W. Nordhaus. Washington, DC: Resources for the Future Press. Nordhaus, W. 2009. Designing a Friendly Space for Technological Change to Slow Global Warming. Snowmass Conference on Technologies to Combat Global Warming. August 2009, Snowmass, CO. Pizer, W. 2002. Combining Price and Quantity Controls to Mitigate Global Climate Change. Journal of Public Economics 85: 409434. Pizer, W., and D. Popp. 2008. Endogenizing Technological Change: Matching Empirical Evidence to Modeling Needs. Energy Economics 30: 27542770. Popp, D. 2006. R&D Subsidies and Climate Policy: Is There a Free Lunch? Climatic Change 77: 311341. Rappaport, A. 1978. Executive Incentives vs. Corporate Growth. Harvard Business Review 56: 8188. Scherer, F. 1967. Market Structure and the Employment of Scientists and Engineers. American Economic Review 58: 524531. Shogren, J., and L. Taylor. 2008. On BehavioralEnvironmental Economics. Review of Environmental Economics and Policy 2: 2644. U.S. Congress. 2009. American Recovery and Reinvestment Act of 2009. 111th Cong., 1st sess. van Benthem, A., K. Gillingham, and J., Sweeney. 2008. Learning-by-Doing and the Optimal Solar Policy in California. Energy Journal 29: 131151. Young, P. 2010. Innovation Diffusion in Heterogeneous Populations: Contagion, Social Influence, and Social Learning. American Economic Review 99: 1899 1924.
6Renewable Energy, Energy Efficiency, and Emissions TradingJos Goldemberg
he present energy system is based primarily on the use of fossil fuels. In 2008, total primary energy consumption was 516 exajoules (EJ)corresponding to 12.32 billion tons equivalent of petroleumof which coal, oil and gas represented 81% (Figure 6.1). The remaining 19% originated in traditional biomass (8%), used mainly in developing countries; nuclear (6%); large hydropower (2%), which is a renewable
source of energy; and new renewables (3%), which include modern biomass, geothermal, solar, wind, small hydro, and marine energy.1 Large hydropower and new renewables together are referred to as modern renewables. The 20th century marked the emergence of fossil fuels, which represented less than 20% in the middle of the 19th century. Until then, renewablesmainly fuelwoodsupplied the bulkModern biomass EJ Bioethanol Biodiesel Bioelectricity Heat 1.7 0.5 3.6 3.7 % 0.32 0.09 0.70 0.71
Oil 33.34%
Gas 20.42%
Traditional biomass Modern biomass Geothermal Wind Solar Marine Small hydro 7.90% 1.83% 0.64% 0.16% 0.11% 0.00% 0.24% New renewables 2.96%
Sources: Authors elaboration on the basis of UNDP 2000, 2004; REN21 2009
Figure 6.1. World primary energy supply shares of 516 exajoules (EJ)
Jos Goldemberg
of the energy needed for all purposes ranging from home heating to cooking to manufacturing (Figure 6.2). These energy sources allowed the industrialized countries to achieve a high level of development benefiting at least one-third of the worlds population. Fossil fuels were cheap and abundant throughout most of the 20th century, which discouraged efforts to optimize energy systems and reduce energy losses. The continued heavy reliance on fossil fuels today as the main source of energy is creating serious problems, which are becoming increasingly evident as time goes by:
338 ppm, in 2008. Such increase is the main source of global warming and associated climate changes. Figure 6.3 shows the evolution of CO2 emissions resulting from the present energy system. Carbon dioxide emissions from Annex B countries (of the Climate Convention) have stabilized since 1990 but are growing rapidly in developing (nonAnnex B) countries (of the Climate Convention) at a rate of approximately 4% per year; this is reflected in world emissions, which are growing roughly 600 million tons of CO2 per year. Carbon dioxide emissions are the dominant component of greenhouse gas emissions, but in 2006, they represented only 69.6% of total emissions. The remaining 30.4% were methane (CH4), nitrous oxide (N2O), and fluorinated gases with high global warming potential (GWP), which are sulfur hexafluoride (SF6), hydrofluorocarbons (HFCs), and perfluorocarbons (PFCs) (Figure 6.4). GHG emissions are usually expressed in CO2 equivalent. Total emissions in 2005 were approximately 45 gigatons (Gt) of CO2 equivalent. To reduce CO2 and other GHG emissions thus became one of the most urgent tasks we face today. There are two approaches to this problem: use energy more efficiently, consequently emit-
Reserves of fossil fuels are finite and by the end of the 20th century were showing signs of exhaustion. Impurities of fossil fuels, such as sulfur oxides and particulate matter, became a main source of pollution at the local and regional levels. The combustion of fossil fuels has the unavoidable result of producing carbon dioxide (CO2), which is changing the composition of the atmosphere, as well as other greenhouse gases (GHGs). Before the industrial age, at the end of the 17th century, the amount of CO2 in the atmosphere was 0.027%, or 270 parts per million (ppm), but it has been growing steadily and reached 0.0338%, or
60 Oil 40
0 1850Source: UNDP 2000
1900 Year
ting less CO2 and extending the life of fossil-fuel reserves; and increase the contribution of renewable energy in the world energy matrix. National governments, as well as some sectors of the productive system, such as industry, transportation, and residential, can adopt these solutions to varying degrees. In industrialized countries, which have already reached a high level of energy consumption per capita, energy efficiency can be implemented more easily, but renewable energy can also play a significant role. In developing countries, where energy consumption per capita is low and growth in energy services is inevitable, clean and efficient technologies and renewable energy can be incorporated early in the process of development, following a different path than that taken in the past by todays industrialized countries. Against this background, this chapter discusses the potential of energy efficiency, renewable energy, and emissions trading schemes in achieving the objective of reducing greenhouse gas emissions.
Renewable EnergyTable 6.1 lists the energy from various types of renewable sources used in the world at the end of 2008, as well their yearly growth rates. Traditional biomass is left out of this table because it is used mainly in rural areas as cooking fuel or charcoal in ways that are frequently nonrenewable, leading to deforestation and soil degradation. Some of the renewable energy sources are developing rapidly, with impressive growth rates of 38% per year for photovoltaic (PV) and 25% per year for wind (see Figure 6.5). In 2008, renewables (including large hydro) represented approximately 5% of the worlds total primary energy consumption, but they are growing at a rate of 6.3% per year, whereas total primary energy supply is growing at a smaller rate of approximately 2% per year. Taking into account the appropriate efficiency and capacity factors,2 the numbers in Table 6.1 can be converted into the total primary energy contribution from renewables, as shown in Table 6.2 and Figure 6.6. An extrapolation of the contribution of renewables up to 2030 on the basis of the rates of growth in the last 10 years is shown in Figure 6.7. To give an idea of the effort that would be needed to curb CO2 emissions up to 2050, the
35.00 CO2 emissions (million tons CO2) 30.00 25.00 20.00 15.00 10.00 Non-Annex B countries (4.07% per year) 5.00 1988 Annex B countries (0.05% per year)
Figure 6.3. World fossil-fuel CO2 emissions and growth rate, 19902005
Table 6.1. Electricity, heat, and transport fuel from renewable sourcesYear Electricity (GW) end 1998 a Large hydropower Wind power Small hydropower Biomass power Solar PV Geothermal Solar thermal power Ocean (tidal) power 640 10 23 40 0.5 8 0.4 0.3 end 2001 b 690 23 25 40 11 8 0.4 0.3 end 2006 c 774 74 73 45 7.8 9.5 0.4 0.3 end 2008 d 860 121 85 52 13 10 0.5 0.3 3.00% 28.32% 13.96% 2.66% 38.52% 2.26% 2.26% 0.00% Growth rate per year 250 145 50 2.26% 23.20% 16.35% Growth rate per year 57 12 12.22% 38.95% Growth rate per year
Hot water/heating (GWth) Biomass heating Solar collectors Geothermal 200 18 11 210 57 11 235 105 33
Sources: aUNDP 2000; bUNDP 2004; cREN21 2007; dREN21 2009 Note: GW = gigawatts; GWth = gigawatts-thermal
International Energy Agency (IEA) recently produced two scenarios of what would be required in terms of renewables in the electricity sector. In one, called the ACT Scenario, global CO2 emissions would be returned to current levels by 2050; in the other, the BLUE Scenario, CO2 emissions would be reduced by 50% from current levels. The results are shown in Table 6.3. In the IEA scenarios, nuclear energy and coaland gas-fired thermal power plants with carbon capture and storage (CCS) are included. These numbers are very large but give an idea of the effort required to prevent catastrophic climate change. The main policy instruments used to accelerate the introduction of renewables in the energy systems of a number of countries are feed-in tariffs and renewable portfolio standards (RPSs). Feed-in tariffs are a policy adopted by governments to accelerate the introduction of renewable energy sources in their matrixes. They obligate utilities to purchase the output of renewable generators at a fixed price, which is set high enough to ensure that the renewable generation in ques-
tion is profitable. The costs of the feed-in tariff are generally passed on to consumers via regulated charges. Feed-in tariffs have been used in Germany and Spain (see Chapters 14 and 15), and in both countries, they have been associated with rapid growth in renewable generation. Under theHigh GWP gases N2O 7.1% CH4 22.9%
German system, the rates paid for new contracts decline annually, forcing the green energy sector to innovate.
Renewable portfolio standards (RPSs) require energy suppliers to purchase a proportion of their electricity (typically 10% to 20%) from renewable
Biodiesel PV grid Wind electricity Solar low-temperature heat Geothermal heat Small hydropower Bioethanol Geothermal electricity Biomass electricity Biomass heat PV off-grid Solar thermal electricity 0%Source: REN21 2009
Sources: Authors elaboration on the basis of UNDP 2000, 2004; REN 21 2007, 2009
70 60 Energy [EJ] 50 40 30 20 10 01998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 17.27 8.29 8.98 18.30 9.21 9.09 Total renewables 20.38 25.45 15.33 11.09 9.29 10.12 Large hydropower 11.68 13.16 43.72 32.04 59.25
New renewables
YearSources: UNDP 2000, 2004; REN 21 2007, 2009
energy sources. This enables renewable generators to charge higher prices for their electricity. In practice, the policy is implemented by having renewable energy generators earn certificates for every unit of electricity they produce. They then sell these along with their electricity to supply companies. As discussed in Chapters 13 and 11 of this book, RPS mechanisms have been used in the United Kingdom and in many U.S. states. Renewable energies are being introduced in a significant way in many countries, particularly in Europe in the form of distributed generation (Bayod-Rjula 2009), which is mostly renewable
and seems to be the approach that will be used on a large scale in the future (Figure 6.8).3
Energy EfficiencyThe amount of energy required to provide the energy services needed depends on the efficiency with which the energy is produced, delivered, and used. Gains in energy efficiency are usually measured by indicators, one of which is called energy intensity (I) and defined as the energy (E) neces-
Table 6.3. Additional rate of investment in power generation technologies needed in the electricity sector every year until 2050ACT scenario Coal-fired with CCS Gas-fired with CCS Nuclear Hydro Biomass plants Wind: inshore Wind: offshore Geothermal Solar PV Solar CSDSource: OECD/IEA 2008a, 2008b
Normalized plant size 500 MW plants 500 MW plants 1,000 MW 50 MW plants 4 MW wind turbines 4 MW wind turbines 100 MW plants Million solar panels 250 MW plants
sary per unit of gross domestic product (GDP): I = E/GDP. Reductions in the energy intensity of economies over time indicate that the same amount of GDP is obtained with a smaller energy input, as shown in Figure 6.9. Figure 6.10 shows the impressive improvements in energy efficiency made in the OECD countries in the period 19731998; without these gains, energy consumption would be 49% higher than it was in 1998, which means a decline in energy use of 2.3% per year. In terms of CO2
emissions for the OECD countries, this means a reduction of roughly 350 million metric tons of CO2 per year. Such decline can be attributed to a combination of the following structural factors:
improvements in efficiency of use of materials and manufactured goods in industrialized and transition countries, such as increased recycling, substitution away from energy-
Figure 6.8. Energy produced from distributed generation (DG) in European countries
Table 6.4. The carbon market: Volumes and values for emissions trading, 20062007Allowances Volume (MtCO2e) EU ETS New South Wales Chicago Climate Exchange UK ETS Total 1,104 20 10 N/A 1,134 N/A 24,699 2,109 50,393 2006 Value ($ million) 24,436 225 38 Volume (MtCO2e) 2,061 25 23 2007 Value ($ million) 50,097 224 72
Source: World Bank 2008 Notes: The New South Wales scheme and the Chicago Climate Exchange, for voluntary trading of emissions, were set before the EU scheme; MtCO2e = metric tons of carbon dioxide equivalent.
intensive materials, improved material efficiency, and intensified use of durable and investment goods; shifts in the mix of activities undertaken in the economy toward services and types of industrial production that are less energyintensive; and
saturation effects, whereby societies are approaching natural limits on the number of certain goods in the residential and transportation sectors (e.g., refrigerators, television sets, cars) that they can absorb, resulting in energy usage growing at a slower rate than GDP. (UNDP 2004)
Figure 6.9. Evolution of energy intensity in a number of countries and regions, 19902005
Because more than 80% of the energy used in the world today comes from fossil fuels, the reduction in energy intensity is reflected in a reduction in carbon intensity (I = CO2/GDP), defined as the amount of CO2 emitted per unit of GDP (in parity purchasing power) as shown in Figure 6.11. As can be seen, the OECD countries have experienced a steady decline in the carbon intensity. Non-OECD countries also had a decline, but carbon intensity stabilized after the year 2000. Figure 6.12 shows the energy intensities for different industrial sectors, indicating that some of them have been declining more rapidly than others. This clearly points out the most interesting sectors from the viewpoint of optimizing energy savings. Over the next 20 years, there is significant scope for improvements in energy efficiency, which may largely be attained by replacement of the existing capital stock. It is estimated that within the next two decades, energy efficiency in industrialized countries could be cost-effectively reduced by 25% to 35%, whereas in transitional economies, reductions of more than 40% could be achieved. Developing countries, which typically have high economic growth and dated capi-
160 140 Hypothetical energy use without savings 120 Energy (EJ) 100 80 Actual energy use 60 40 20 074 76 78 80 82 84 86 88 90 92 94 96 19 19 19 19 19 19 19 19 19 19 19 19
0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 1970 World Non-OECD total OECD total
1985 Year
Source: OECD/IEA 2007 Note: PPP is the purchasing power parity, adjusting nominal GDP figures according to the living conditions of each country
Figure 6.11. Carbon intensity: CO2 emissions per GDP (PPP), 19702005
tal and vehicle stocks, could achieve cost-effective improvement of 30% to more than 45% Global energy intensity could decline by as much as 2.5% per year as a result of a combination of structural changes and efficiency improvements. The extent to which this will be achieved depends on the effectiveness of government policy, adaptation of attitudes and behaviors, and the level of entrepreneurial activity in energy conservation and material efficiency. Policymakers play a central role in driving energy efficiency improvements by setting stand-
ards, such as building codes; market-based incentives, including certificate markets; and adequate payment systems (World Bank 2008) for energy that are applied by well-informed consumers, planners, and decisionmakers.
Emissions TradingThe discussions above have given brief overviews of two of the main classes of technology that are
100% Paper & pulp Nonmetallic minerals Chemicals Other manufacturing Primary metals74 76 78 80 82 84 86 88 90 92 94 96 19 19 19 19 19 19 19 19 19 19 19 19 98
available to reduce GHG emissions: improvements in energy efficiency and the increased use of renewable energy. Emissions trading, frequently known as cap-and-trade, is a technologyneutral tool to incentivize efficient emissions reduction by companies and countries. Several emissions trading schemes are currently in place and generally function as follows:
Setting of a cap. A government or international body sets a limit on the total amount of pollutants that can be emitted in a specific period. Allocation of allowances. Participants in the scheme are allocated allowances to pollute a certain quantity. Allowances can be allocated on a number of bases, such as historical emissions, or through an auction of rights. The total number of allowances allocated cannot exceed the cap set by the governing body. Emissions trading. Scheme participants that emit more than their allocations must purchase additional allowances, while those that have an excess number of allowances may sell these. Participants that can reduce their emissions at low cost have an incentive to do so, and sell allowances, whereas those that face high costs of abatement that exceed the costs of allowances must pay to pollute more.
One such scheme was implemented in the United States under the 1990 Clean Air Act to curb emissions of sulfur dioxide (SO2; a local pollutant, not a greenhouse gas). By 2007, the scheme succeeded in reducing annual SO2 emissions to approximately 50% of 1980 levels (EPA 2009). Another emissions trading scheme was implemented at the international level by the Kyoto Protocol, which came into force in 2005. This binds the participants to a cap-and-trade system covering the six major greenhouse gases. The scheme covers only developed nations, in recognition of the fact that they are principally responsible for the majority of the past global emissions. Notably, the United States, which is responsible for a significant proportion of world emissions, is the only developed nation that signed the UNFCCC that is not participating in the scheme. The participants agreed to an average overall reduction in emissions of approximately 5% relative to 1990 levels by the end of 2012 (UNFCCC 2009b). During the five-year compliance period between 2008 and 2012, participants can earn and trade credits through three market-based mechanisms: international emissions trading (IET), joint implementation (JI) projects, and Clean Development Mechanism (CDM). IET involves trading of allowances among countries as described above; JI and CDM are means for countries to earn allow-
ances by reducing emissions in other countries, as explained later in this chapter. The second commitment period of the Kyoto Protocol, together with a long-term cooperative action under the UNFCCC, were discussed at the end of 2009 in the 15th Conference of the Parties to the Climate Convention in Copenhagen but no general agreement of the type reached in Kyoto was achieved. It was decided instead that the industrialized countries, including the United States, commit to implement individually or jointly quantified economy wide emissions targets for 2020 to be submitted to Secretariat of the Convention by January 31, 2010. The developing countries would present voluntary non-mandatory pledges which will be subject to measurement, reporting and verification.
cost. The total number of allowances is reduced in each year of the scheme in order to achieve the desired overall reductions in emissions. The scheme, which initially covered the 15 then-existing member states, began its first trading period on January 1, 2005. The first trading period lasted until the end of 2007 and was primarily intended to ensure that the ETS was functional by the start of the first commitment period under the Kyoto Protocol in 2008. The second trading period commenced in January 2008 and will end in December 2012, coinciding with the end of the first commitment period of the Kyoto Protocol. Table 6.4 shows the volume of CO2 equivalent and volumes (in dollars) which were object of transaction in 2006 and 2007. As shown in Figure 6.13, the price of allowances fell dramatically twice during the first period. The first fall occurred following the peak in April 2006, when it was discovered that some countries had overallocated allowances to industry, creating a substantial excess supply of allowances. Subsequently, prices faced another dramatic decline to approximately 1.20 ($1.63) per ton in March 2007 and later fell to around 10 euro cents (14 U.S. cents) in September 2007, because existing allowances expired at the end of the first phase and could not be carried over into subsequent periods. The initial overallocation of allowances dampened the incentives to abate and created a windfall for many industrial companies. The subsequent collapse in the price of allowances reduced the incentives for abatement. The collapse of prices at the end of the first phase did not directly affect prices for contracts for 2008, the first year of the second phase. Market participants knew already in 2007 that this phase would be more stringent in regard to the cap and less lenient toward allowances, which explains the high prices for 2008 allowances. In April 2009, the European Commission announced a number of changes to the scheme that will be effective from the third trading period, commencing in January 2013 (European Council 2009):
Member states will introduce auctioning of allowances, which will be introduced gradu-
35.00 30.00 Price per ton CO2e ( ) 25.00 20.00 15.00 10.00 5.00 0.00 Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov 2006 2006 2006 2006 2006 2006 2007 2007 2007 2007 2007 2007
Figure 6.13. Carbon price, January 2006November 2007 (settlement price of December 2007 ECX futures)
ally into most sectors, with plans to auction all allowances by 2020. Following the primary auction, allowances can be traded among companies as before. Nitrous oxides and perfluorocarbons will be added to the list of GHGs covered by the scheme. The scheme will be extended to include the aviation industry. The number of credits that can be earned through CDM and JI activity will be limited in order to motivate member states to reduce emissions domestically.
process within the European Commission. Figure 6.14 shows the cap on emissions set by the EU as well as actual (and future) emissions. A robust secondary market for carbon certificates exists through which investors bank on the future value of the ETS certificates changing many times. The ETS does not include transport, however, and thus this action is limited to the industrial process and energy sectors.
Joint ImplementationJoint implementation (JI) is one of the flexibility mechanisms included in the Kyoto Protocol as a means for developed nations with emissions reduction commitments (Annex I countries) to meet their obligations at the lowest possible cost.5 JI allows an Annex I country to reduce its domestic emissions reduction requirements by undertaking projects to reduce emissions in another Annex I country. This allows Annex I countries to reduce their cost of emissions reduction. A key requirement for a JI project is that it must provide an emissions reduction, or a removal of carbon from the atmosphere that is demonstrably additional to what otherwise would have occurred. This is known as additionality. If a project can be shown to be additional and meet
The proposed caps for the third trading period target an overall reduction of greenhouse gases for the sector of 21% in 2020 relative to 2005 emissions (European Council 2009). In addition, the third trading period will be both more economically efficient and environmentally effective. It will be more efficient because trading periods will be longer (eight years instead of five) and have a substantial increase in the amount of auctioning (from less than 4% in the second phase to more than half in the third). Environmental effectiveness will be guaranteed by a robust and annually declining emissions cap (21% reduction in 2020 compared with 2005) and a centralized allocation
3,000 2,500 Emissions (tons) 2,000 1,500 1,000 500 0 2005 2006 HistoricSource: New Energy Finance 2009a
2008 Projected
2009 Cap
other requirements set out by the UNFCCC, the project owner can earn credits called emission reduction units (ERUs) from the host country. Each ERU represents an emissions reduction equivalent to 1 ton of CO2 and can be set against the domestic emissions reduction requirement. After a long preparatory process, JI projects began to take shape, and as of March 2010, 207 projects had been submitted. If all were implemented, they would lead to an emissions reduction of 363,355 million tons of CO2 equivalent in the period 20082012. The great majority of the projects are in the Russian Federation and eastern European countries. So far very few ERUs have been issued.
The CDM is administered by a designated national authority (DNA) in the country where the project takes place and is supervised by the CDM executive board at the international level. For projects to be approved, they must demonstrate additionality and meet the other requirements set forth by the UNFCCC. If a project is approved, the project owner is granted a certified emission reduction (CER), each of which represents an emissions reduction equivalent to 1 ton of CO2. This can be counted toward the domestic emissions reduction target. By June 1, 2009, 4,417 projects had been submitted; if all implemented, these would correspond to 2,931,813 million tons of CO2e, representing roughly 1% of the total necessary effort to curb GHG emissions until 2050. (Roughly 75% of the CDM projects are in China.) Table 6.6 shows the number of CDM projects of each type, along with the 2012 CERs granted. The distribution of CDM projects from different sectors are shown in Figure 6.15. In contrast to emissions trading schemes, which are actively traded in the stock market, JI and CDM are project-based transactions. Annual volumes for 20062007 are given in Table 6.7. Figure 6.16 gives estimates of the total amount of emissions transactions of the EU ETS, CDM, and JI for the years 2002 to 2008.
2012 ERUs (000) 96,223 166,302 32,058 71,053 10,896 410 362,355 27% 46% 9% 20% 3% 0% 100%
Number 106 2,959 989 540 111 190 52 21 4,968 2.1% 60% 20% 11% 2.2% 3.8% 1.0% 0.4% 100%
2012 CERs (000) 741,442 980,433 576,142 375,976 172,894 25,420 15,224 8,076 2,835,607 26% 35% 20% 11% 6.1% 0.9% 0.5% 0.3% 100%
Table 6.7. Volumes and values for CDM and JI, 200620072006 Volume (MtCO2e) Project-based transactions Primary CDM Secondary CDM JI Other compliance and voluntary transactions TotalSource: World Bank 2008
2007 Value ($ million) 5,804 445 141 146 6,536 Volume (MtCO2e) 551 240 41 42 874 Value ($ million) 7,426 5,451 499 265 13,641
537 25 16 33 611
Stimulus PackagesA significant amount of the stimulus packages adopted by a number of governments to face the financial crisis of 20072008 consists of investments in green activities. These amount to 6% of the total recovery packages announced by governments. Figure 6.17 shows how much 12 different countries invested in green stimuli. The estimated amounts announced by these economies total $184.9 billion invested in green stimuli. China
and the United States are the leaders, in nominal terms, of the green stimuli activities, earmarking $68.7 billion and $66.6 billion, respectively. The sector breakdown in Figure 6.18 shows that energy efficiency remains at the heart of the low-carbon fiscal stimuli. Accounting for as much as 36% of the total $184.9 billion, this sector will receive a boost of some $65.7 billion globally, mainly via building efficiency projects. In addition, $7.9 billion has been announced for research and development in energy efficiency. The second major winner is electricity grid infrastructure. More than $48.7 billion has been earmarked for grid development and upgrade, accounting for some 26% of the total funds. The U.S. Department of Energy has already disbursed $41.9 million in grants for fuel cell energy projects. Furthermore, $101.5 million has been directed to wind energy research, and detailed plans have been disclosed on $2.4 billion to be spent on carbon capture and storage and $4 billion for grid upgrades. Out of a total of $2 billion to support energy science research, the breakdown of almost $1.3 billion have also been confirmed, and only some $725 million remains to be allocated.
Primary CDM
Secondary CDM
Other $118bn 14 6
$ bn
China U.S. EU27 Japan S Korea Spain Germany Australia UK France Brazil CanadaSource: ECX 2009
68.7 66.6 11.3 8.0 7.7 7.6 3.7 3.4 2.7 2.5 1.9 0.8
Efficiency 35%
Unspecified 5%
Grid 26%Source: New Energy Finance 2009b
Notes1. Traditional biomass is used to denote locally collected and often unprocessed biomass-based fuels, such as crop residues, wood, and animal dung, most of which are used noncommercially. Sometimes these lead to deforestation and therefore are not renewable. Modern biomass refers to biomass produced in a sustainable way and used for electricity generation, heat production, and transportation (liquid fuels). International Energy Agency (IEA) statistics lump together traditional and modern biomass under the heading of combustible renewables and waste (CR W).
Average conversion efficiency: heat, 85%; biomass electricity, 22%; combined heat and power, 80%; geothermal electricity, 10%; all others, 100%. The main sources of distributed generation are reciprocating engines (internal combustion engines), gas turbines, microturbines, fuel cells, photovoltaic systems, thermoelectric solar plants, wind energy conversion systems (WECSs), biomass to energy power plants, and small hydroelectric power plants (SHPPs). Article 17 of the Kyoto Protocol. The Conference of the Parties shall define the relevant principles, modalities, rules and guidelines, in particular for verification, reporting and accountability for
emissions trading. The parties included in Annex B may participate in emissions trading for the purposes of fulfilling their commitments under Article 3. Any such trading shall be supplemental to domestic actions for the purpose of meeting quantified emissions limitation and reduction commitments under that Article. Article 6 of the Kyoto Protocol. For the purpose of meeting its commitments under Article 3, any Party included in Annex I may transfer to, or acquire from, any other such Party emissions reduction units resulting from projects aimed at reducing anthropogenic emissions by sources or enhancing anthropogenic removals by sinks of greenhouse gases in any sector of the economy, provided that: (a) any such project has the approval of the Parties involved; (b) any such project provides a reduction in emissions by sources, or an enhancement of removals by sinks, that is additional to any that would otherwise occur; (c) it does not acquire any emission reduction units if it is not in compliance with its obligations under Articles 5 and 7; and (d) the acquisition of emission reduction units shall be supplemental to domestic actions for the purposes of meeting commitments under Article 3. Article 12 of the Kyoto Protocol. (1) A Clean Development Mechanism (CDM) is hereby defined. (2) The purpose of the clean development mechanism shall be to assist Parties not included in Annex I in achieving sustainable development and in contributing to the ultimate objective of the Convention and to assist Parties included in Annex I in achieving compliance with their quantified emission limitation and reduction commitments under Article 3. (3) Under the clean development mechanism: (a) Parties not included in Annex I will benefit from project activities resulting in certified emission reductions; and (b) Parties included in Annex I may use the certified emission reductions accruing from such project activities to contribute to compliance with part of their quantified emission limitation and reduction commitments under Article 3, as determined by the Conference of the Parties to this Protocol.
ReferencesBayod-Rjula, Angel A. 2009. Future Development of the Electricity Systems with Distributed Generation. Energy 34: 377383.
CDIAC (Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory). 2009. Fossil-Fuel CO2 emissions, cdiac.ornl.gov (accessed January 10 2010). Cossent, Rafael, Toms Gmez, and Pablo Fras. 2009. Towards a Future with Large Penetration of Distributed Generation: Is the Current Regulation of Electricity Distribution Ready? Regulatory Recommendations under a European Perspective. Energy Policy 37: 11451155. ECX (European Climate Exchange). 2009. ECX EUA Futures Prices. www.ecx.eu/ (accessed December 21, 2009). EPA (U.S. Environmental Protection Agency). 2009. Acid Rain Program 2007 Progress Report. www.epa.gov/airmarkets/progress/arp07.html (accessed January 10, 2010). European Council. 2009. Directive 2009/29/EC of the European Council. Press release. ec.europa.eu/ environment/climat/emission/ ets_post2012_en.htm (accessed December 21, 2009). EU (European Union). 2003. Kyoto Protocol. europa.eu/rapid/pressReleasesAction.do?reference= MEMO/03/154&format=HTML&aged= 0&language=EN&guiLanguage=en (accessed December 15, 2009). New Energy Finance. 2009a. EU ETS Analyst Reaction (April 3, 2009). Available at carbon.newenergyfinance.com/ (accessed April 3, 2009). . 2009b. Carbon Industry Intelligence Research Note. October 2008. Available at carbon.newenergyfinance.com/ (accessed May 22, 2009). OECD/IEA (Organisation for Economic Cooperation and Development/International Energy Agency). 2004. Oil Crises and Climate Challenges: 30 Years of Energy Use in IEA Countries. www.iea.org/textbase/nppdf/free/2004/30years.pdf (accessed December 21, 2010). . 2007. CO2 Emissions from Fuel Combustion, 19712005. Paris: OECD Publishing. . 2008a. Energy Technology Perspectives. In Support of the G8 Plan of Action. Scenarios & Strategies to 2050. Paris: OECD/IEA Publishing. . 2008b. Worldwide Trends in Energy Use and Efficiency. Key Insights from IEA Indicator Analysis. In Support of the G8 Plan of Action. Paris: OECD Publishing. REN21 (Renewable Energy Policy Network for the 21st Century). 2007. Renewables 2007: Global Sta-
tus Report. www.ren21.net/pdf/ RE2007_Global_Status_Report.pdf (accessed December 10, 2010). . 2009. Renewables Global Status Report: 2009 Update. www.ren21.net/pdf/ RE_GSR_2009_Update.pdf (accessed December 10, 2010). UNDP (United Nations Development Programme). 2000. World Energy Assessment: Energy and the Challenge of Sustainability. New York: UNDP. . 2004. World Energy Assessment: Overview 2004 Update. New York: UNDP
UNEP RISO. (2010). CDM/JI Pipeline Analysis and Database http://cdmpipeline.org/cdm-projectstype.htm (accessed January 11, 2010). UNFCCC (United Nations Framework Convention on Climate Change). 2009a. Greenhouse Gas Inventory Data. unfccc.int/ghg_data/items/3800.php (accessed January 11, 2010). . 2009b. Kyoto Protocol. unfccc.int/ kyoto_protocol/items/2830.php (accessed January 11, 2010). World Bank. 2008. State and Trends of the Carbon Market 2008. siteresources.worldbank.org/NEWS/ Resources/State&Trendsformatted06May10pm.pdf (accessed January 11, 2010).
Part IIIRenewable Generation and Electric Power Markets
7Electricity Wholesale Market Design in a Low-Carbon FutureWilliam W. Hogan
olicy to convert the energy system to a lowcarbon configuration involves a central focus on the electricity sector. Electricity generation is a major use of fossil fuels and therefore accounts for a large fraction of the emissions of carbon dioxide; reducing carbon emissions in other sectors, such as transportation, may involve increased electrification; and electricity can be and has been generated without carbon emissions. In addition, electricity generating facilities burning fossil fuels are large and easy to identify and thus a natural focus for regulation and policy to reduce carbon emissions. Designing policy for a low-carbon electricity sector depends on the nature of the technology and institutions in that sector. In many countries, the electric power system is already involved in a major restructuring process. Critical technological and institutional features of that reform process center on wholesale market design elements that have important implications for expansion of reliance on low-carbon energy sources. The wholesale market covers bulk generation, dispatch, and transport of electric power for ultimate delivery to retail customers. Although the details and jurisdictional rules differ in the United States and the European Union (EU), a common distinctive feature of the wholesale market is the interaction through the interconnected transmission grids
that transcend states and countries. The complementary rules for retail supply or distribution of electricity are important, but the emphasis here is on electricity wholesale market design in a lowcarbon future. The main theme emphasizes the strong interaction between market design and policies for achieving a low-carbon future.
William W Hogan .
lower the voltage for retail distribution to factories, businesses, and homes. Typically the local retail distribution system is simpler in its design than the transmission system. In particular, the distribution network is more decentralized or radial in the sense that electrical power problems in one distribution network, the principal cause of power outages, can be isolated from the rest of the system. The interconnected transmission network is another story. The transmission network initially developed organically as individual companies sought to connect their generators to their loads. One of the features of the electric power system is the speed of adjustment when a change occurs in load, generation, or transmission connections. With present technology and little storage, to a first approximation, electricity is the limiting case of same-time production. The power produced and consumed must maintain instantaneous balance throughout the system, and any disruption of the transmission grid results in instantaneous redistribution of the flow of power in the system. The system is not controllable in the same ways as we imagine for a network of pipelines with valves and relatively slow-moving fluids. Power flows at the speed of light. Companies building bulk transmission facilities consequently relied on redundant pathways in the transmission grid to ensure reliability. In addition, the power system required standby generating capacity that could be called on immediately in order to replace a lost generating unit. As neighboring electricity companies would be unlikely to face disruptions at the same instant, redundant transmission and generation in one system could help other systems and lower the total cost of maintaining reliability. This confluence of technology and economics led to the gradual development of interconnected alternating current (AC) transmission grids linking loads to thousands of generators through tens of thousands of individual transmission links. Throughout such an interconnected grid, all of the generators and load must be synchronized. The resulting ensemble has been called the largest machine in the world (Amin 2000, 264). For example, the North American continent from
Mexico to Canada is dominated by only three synchronous grids in the Eastern, Western, and Texas Interconnections, operating with limited exchange through direct current (DC) interconnections between them. (Alaska and Quebec have their own separated grids as well.) Similarly, a large synchronized grid covers 24 European countries (ENTSO-E 2008). The complexity of a large synchronized grid is well known to electricity system operators but not most customers. Traditionally, the electricity company was vertically integrated from generation through transmission to distribution. Customers faced a monopoly that provided the power and charged a price only loosely coupled to actual contemporaneous costs. The company made its own investment plans for generation and wires, often through joint investments with other vertically integrated monopolies. Control of the interconnected transmission grid was handled through an array of bilateral and multilateral agreements among the members of the club, but with strict exclusion of potential new competitors. These club agreements worked reasonably well, although below the surface, difficult problems often occurred at the seams between regions or when one member of the group was leaning too much on the system. The club agreements involved coordinated planning and expansion of the transmission grid, always an opaque and arcane process. It was difficult to define the resulting rights to transmit power and determine how these rights would be allocated in the interconnected grid, and realtime dispatch of power plants had to be coordinated in order to maintain the grids reliability. The rules and incentives for operation of this system were complicated by the nature of electricity regulation and its focus on cost recovery. The emphasis of regulation or government ownership was on control of the average costs, with relatively little attention paid to the role of marginal costs and the connection to incentives that could or should govern operations and efficient investment. This history would be a major hurdle to overcome in the development of electricity market reforms.
Economic efficiency always played a secondary role to maintaining system reliability, keeping the lights on. Gold plating the system a little might produce some inconvenient questions on occasion, but major blackouts or even relatively minor but frequent local supply interruptions could limit a career and were deemed unacceptable. The common approach for the wholesale power system was, and continues to be, treating reliability as a constraint and economic efficiency as a goal to be sought subject to that constraint. (Because of the lower availability of redundancy and greater expense of reliability, a more nuanced trade-off is seen at the retail distribution level.) In real time, the variable cost of generating power ranges from virtually zero for run-of-theriver hydropower, to moderate for nuclear and coal plants, to expensive for natural gas and oil plants. With load changing rapidly over the day and total generation at the trough during the night at as little as 30% of the peak during the day, it makes sense to choose the cheapest generators available to produce at any time (Monitoring Analytics 2009). The basic idea is to operate as closely as possible to the security-constrained (respecting all the many reliability constraints on the grid) economic dispatch that provides the necessary ancillary services and minimizes the cost of meeting a given electric load at any moment and over the course of the day. Under the traditional electricity model, the aggregate cost of this economic dispatch would be recovered through preset customer charges that muted or completely ignored the dramatic changes in opportunity costs over the day and season. As a result, the traditional system often provided adequate cost recovery but did little to reveal or communicate incentives to guide either operations or investment. Rather, investment decisions relied on and defaulted to the expert assessments of the established electricity companies and left few opportunities for innovation by new entrants. Although the operating decisions were neither open nor transparent, a reasonable case can be made that traditional reliance on the framework of security-constrained economic dispatch produced efficient operating decisions and cer-
tainly contributed to the reliability of the system. The signals sent for investment and innovation were an entirely different matter, however. The system depended on the planning decisions of the protected monopolies, and the lack of transparency coupled with major construction delays and cost increases precipitated deep concern with investment choices.
Energy Policy Act (EPAct92), which included provisions for open access to the transmission grid. Authorities in Australia, Argentina, most of Europe, New Zealand, the United States, and elsewhere were experimenting with new structures for organizing the electricity system. Many recognized or assumed that economies of scale in generation had largely been exhausted, and new and more loosely regulated entrants were just as capable of building and operating successful power plants as had been the incumbent electric utilities. This precipitated the notion that vertical separation of generation, transmission, lowvoltage distribution, and supply would be at least possible and perhaps desirable. Existing generators, perhaps divided and divested from existing electric companies, could compete against each other and against new entrants to construct power plants and sell electricity to others, including final customers. Local distribution facilities would continue to operate more or less under the old regulated monopoly model, albeit often as a wires-only business with suppliers acquiring electricity from generators and selling on to final customers. A key requirement for this vertical separation and competition in generation would be open access to the common integrated transmission grid and associated unbundling of critical services. The best form of organization for the common interconnected grid with the unbundling of services was not immediately apparent. In the United States, the emphasis was soon on transmission open access and nondiscrimination in prices and services for all participants in the electricity market. As it turned out, these seemingly innocuous requirements had profound implications for the nature of electricity market design. The details of the story and the alternative market approaches are many, and an extensive literature exists on alternative market designs (Sioshansi 2008). The purpose here is not to recite these details, but to emphasize certain features that are salient for the discussion of a low-carbon electricity system that relies heavily on technologies such as solar, wind, and demand-side man-
agement, which differ substantially from the legacy portfolio of fossil-fuel generation.
necessary to undo what the nominal market has done. Countertrade, or some other system redispatch intervention like it, is necessary whenever the market schedules do not reflect system operation reality. Furthermore, workable countertrade market designs and interventions are necessarily discriminatory in selectively choosing and paying for redispatch of some generators but not others similarly situated, as shown by experience in the United States (Hogan 2002). This countertrade design has implications for the treatment of renewables and other low-carbon energy sources. In order to understand the implications, it is convenient first to sketch out the essential features of the RTOs and organized spot markets that integrate market design and system operations.
tions through the spot market can be gross, without separate bilateral transactions and schedules, or net, covering only imbalances relative to bilateral transactions and schedules. The differences are largely semantic, despite heated arguments in the past for the merits of gross or net market design. The principal market design problem that arises in all electricity systems follows from the nature of the interconnected transmission grid. In addition to being large, interconnected, fast responding, and contingency constrained, electricity transmission grids have an unusual feature that follows from the need for fast response. The redundancies and contingency constraints arise because it is not possible to use valves to control the flow of power. To a good first approximation, once the pattern of load and generation is set, the flow of power through the grid is determined by physical laws that distribute the power over the system to (more or less) minimize losses. It turns out that power flows to varying degrees on every available path between generation and load. This means that control of power plant dispatch and control of transmission flows are two sides of the same coin. Changing the dispatch changes the power flows, and changing the power flows requires changing the dispatch. This technological fact has profound implications. For example, it means that it is impossible to operate the system with only decentralized decisions about generation and load. There must be central coordination of everything and central control of enough of the dispatch to meet the requirements of system operations. The parallel flow of power means that any trading system must confront material externalities as the real flows for one transaction interfere with the real flows of other transactions. In the United States, the electric utilities struggled for decades to design a workable system of transmission flow accounting that would support physical transmissions rights, and never succeeded in working around the inconvenient fact that power flows everywhere (Lankford et al. 1996). The design solution to this otherwise intractable problem is to recognize that the framework of security-constrained economic dispatch, already familiar to system operators, provides the
foundation for a coordinated spot market (Schweppe et al. 1988). This spot market inherently respects the requirements of system operations and provides a convenient connection to electricity markets and forward trading. Recognizing that there must be a system operator, this design asks for bids and offers for power at the physical locations, schedules between locations, or schedules with bids and offers for deviations from the schedules. Thus the structure of the information provided to the system operator is the same as under a vertically integrated system, only now the engineering estimates of loads and costs are replaced by the bids and offers for power. As before, the system operator selects the changing pattern of economic dispatch to meet load and provide ancillary services subject to the (many) security constraints and the complex flows of power on the transmission grid. In economic terms, the independent system operator internalizes the major transmission externalities so that transmission and transaction schedules can be point-to-point and be silent on the path between the points. The quantity result of the securityconstrained economic dispatch for any interval is a schedule of generation and load at each electrical location. Hand in hand with the quantity dispatch is a set of market-clearing prices for settlement purposes that capture the system marginal cost of meeting increased load or decreased generation at each location. The term of art is locational marginal pricing (LMP). These locational marginal prices provide an immediate definition of the appropriate spot price of transmission between any two locations: a straightforward arbitrage argument dictates that the competitive spot price of transmission between any two locations is the difference in the energy spot prices at the locations. When there is no congestion in the system, the market clearing prices would be the same as the normal single market clearing price of simpler models, except for differences in marginal transmission losses. But when transmission constraints are binding, the locational prices differ according to the marginal impacts of activity at each location
on system congestion. These congestion-induced differences can be surprisingly large and sometimes counterintuitive. Simultaneous market clearing prices can often be well above the marginal cost of the most expensive generation running at some locations and well below marginal cost or even negative elsewhere (Hogan 1999). This is a material effect of the complicated electrical interactions made visible in the spot market but previously hidden in system operations. Once this security-constrained economic dispatch framework is adopted, with accompanying locational prices applied in market settlements, it is a simple matter for market participants to schedule or otherwise arrange transactions. For example, a bilateral transaction can be scheduled between any two locations, for a charge at the difference in locational prices. Any imbalances in actual delivery would be paid for at the relevant locational prices. With security-constrained economic dispatch and locational prices, the imbalance market merges with the spot market. All parties can participate, so there is open access. And all parties face the same scheduling rules and resulting prices, so there is nondiscrimination. No provision exists for countertrading through market positions taken by the system operator, because countertrades are not needed to bring the spot market into conformance with system operations. This results in less concern over conflicts of interest for the system operator. By construction, security-constrained economic dispatch is consistent with system operations and the spot market. In addition, the existence of the spot market and locational prices provides the foundation for the financial transmission right (FTR) (Hogan 1992). In the simplest form, an FTR calls for paying to the right holder the (congestion) difference between the corresponding locational prices. Absent grid outages, the FTR is a long-term instrument that provides a perfect hedge for the short-term congestion fluctuations in transmission spot prices. With a matching FTR between generation and load, a supplier can sign a fixed price contract for delivered power and be sure that the system will always allow fulfillment of the contract through either physical delivery or offset-
ting purchases and sales with no incremental charges for volatile transmission congestion. This workable financial transmission right provides the functionality sought without success in unworkable systems of physical transmission rights. Concerns early on were that this integrated locational model could not work well with very many locations. The United States went through the agony of forcing aggregations across zones to reduce the granularity of the market representation. Zonal aggregation subject to rules of open access and nondiscrimination was tried and abandoned in the PJM system, a large RTO that covers the Mid-Atlantic states in the United States, the corresponding New England RTO, the California RTO, and the Texas RTO (Hogan 2002; Potomac Economics 2009). This zonal aggregation inherently suppressed the detail of real system operations and thereby lost the efficient pricing signals, which in turn required complex rules to bridge the constructed gap between the spot market and system operations. Painful experience made it clear that the simplest system is to go as far as possible to match temporal and locational granularity with real operations and the associated system opportunity costs. The integrated locational design with very many locations works. For example, in the Mid-Atlantic states, PJM updates the coordinated dispatch and prices every five minutes for approximately 8,000 locations. This granularity means that geographically proximate locations that are electrically distant can have very different prices, and these prices reinforce rather than contravene the imperatives of efficient and reliable system operations. One of the regular reports from system operators newly adopting this model is that it greatly simplifies and improves dispatch operations, because market participants have strong economic incentives to follow dispatch instructions. Some contend that a more geographically aggregated system would be easier and cheaper to implement. This might be correct if an alternative were available that could avoid the need to attend to all the detail in real operations.2 No such alternative is available, however. If the system operator is applying the basics of security-constrained economic dispatch, then all the essential information
on transmission lines and multiple locations must already be available. The distinctive feature of the integrated locational market design is that it makes the locational marginal opportunity costs visible and applies these as the prices used in the settlements system. The incremental costs of using the full granularity are modest, or even negative when considering the costs of imposing rules and procedures to overcome the effects of artificial aggregation.
trading. The same conservative assumptions create opportunities for transmission owners to exploit transmission capacity that is in effect withheld from the rest of the market. Other forms of ex ante rules required by the disconnect between market design and system operation may include restrictions on the lead time for changing energy schedules. Physical operation of plants and control mechanisms often allow for changes up to minutes before real time, but formal market schedules may foreclose changes an hour or more ahead of operations. This scheduling lag could be particularly important for highly variable energy resources. Despite the conservative capacity restrictions and other ex ante rules, market schedules may still violate the physical limits of the system. Even though generation and load may balance within the boundaries of an aggregate zone used to define a common energy product, for instance, intrazonal power flows may violate transmission constraints. There is no way to set an ex ante rule that maintains the aggregation of the zone and reflects the impacts of the intrazonal limits on the transmission system.The trade-determined schedules cannot be honored without some redispatch of the system, and this provides a need for countertrade. Typically the initial schedules are honored, but the system operator arranges a generation redispatch that involves the equivalent of purchases and sales of energy at different locations in the system and creates counterflows that relieve the transmission constraints. In principle and in practice, this countertrade redispatch may involve the very trades and generators that created the potential overload. In effect, a generator may be paid in the first instance to provide energy as part of the nominal market equilibrium schedule, and then be paid not to generate the same energy in order to accommodate the counterflow and relieve the transmission constraints. As might be expected, the incentive effects of these aggregation and countertrade arrangements can be perverse in the extreme. The zonal aggregation models in the PJM system, New England, California, and Texas all faced these perverse incentives, which precipitated abandonment of the zonal aggregation and countertrading market design.
Other markets have implemented countertrade systems that work much better. For instance, the EU universally employs aggregate regions for defining energy products, and it uses pipeline models with conservative transmission capacity assumptions. All these workable systems find it necessary to dispense with principles of nondiscrimination. The transmission system operator is able to make onetime arrangements for countertrades that relieve transmission constraints and balance the system without having to offer the same deal to every other participant in the market. This reduces the ability of generators to extract payments for not running. Although necessary, this selective participation in the market by the transmission operator creates its own set of incentive problems. The cost allocation mechanisms for these countertrade systems are varied. In some cases, such as in the United Kingdom, the transmission system operator absorbs the costs under a price cap regime and therefore internalizes incentives to minimize the countertrade costs. However, this same system requires the operator to take on the sole responsibility for transmission expansion, as the aggregate price signals in the market do not provide sufficient incentive or information to guide detailed transmission investments.
the textbook ideal that should be the target for policy makers. A trading arrangement based on LMP takes all relevant generation and transmission costs appropriately into account and hence supports optimal investments (IEA 2007, 16).
Connection StandardsThe rules for generator grid interconnection play a major role in affecting the integration of new capacity. In the case of the integrated locational market design, there is fundamental simplicity in theory that can be reasonably approximated in reality. In the other market designs, system connection rules confront the challenge of designing protocols to overcome the perverse incentives created in the market. The simplicity of the integrated locational market design is illustrated in the rules for energy resource interconnection in the PJM system (PJM 2009). An investor that has a site with the required permits and wants to build a new facility to sell energy into the PJM market has to meet only a minimal set of requirements. Primarily these are technical constraints to ensure that the connection to the grid will satisfy electrical characteristics so that connection and charging of the line, with no material power generation, do not compromise the rest of the system. In effect, the generator interface is an extension cord to the rest of the grid that has to meet certain standards, and the generator has to pay for the equipment and extension line. This form of interconnection allows the generator to produce, buy, and sell energy in the spot market at the locational marginal price at its point of interconnection. There is no guarantee about the price or profitability of the energy produced, nor any guarantee that access to the grid at the point of connection provides transmission rights to hedge the cost of delivery to other locations in the grid. When the transmission system is constrained, the locational marginal price at the point of connection might be very low, and the generator may choose not to run. The generator can have the system operator make these decisions in real time by simply offering to produce with a
minimum acceptable price. There would be no need to arrange advance schedules or bilateral transactions. Although the generator could participate in forward markets, arrange long-term contracts, and purchase FTRs to hedge transmission costs, these are not necessary conditions of the connection rule, and immersion in trading would not change the incentives for the price-taking generator to make simple offers into the real-time market to allow the system operator to take the energy when available and when the locational price is at least equal to the minimum bid. As discussed below, in PJM, most generators choose a more complicated form of interconnection in order to participate in the capacity market that operates on top of the energy market. However, the simple ideal of the energy-only interconnection is available and is used by some generators. The countertrade markets face a quite different situation when considering the rules for connecting new generators. The generators do not see the costs of transmission constraints obscured by zonal aggregation of energy product definitions. Hence they may choose to locate where costs are actually higher but are imposed on someone else. Furthermore, the typical countertrade market design carries with it an explicit or implied obligation to accept the generators energy output and deliver it to some loads. In effect, in the terms of the integrated locational market design, new generators acquire a bundle of transmission rights by virtue of their interconnection. The connection of new generators may imply that the transmission owner must make sometimes substantial transmission investment to support these new rights. Similarly, for all generators, but particularly for highly variable wind and solar plants, there must be increased operating reserves or other controls needed to support energy delivery. The transmission system operator will have to make these investments or impose constraints on the interconnection. This gap between the incentives for the new entrant and the real costs of interconnection creates a need for rules and regulations to overcome the incentive effects. For example, new generators
may be required to make so-called deep transmission investments elsewhere in the network in order to accommodate their new production without undoing the implicit rights of other generators. Alternatively, the costs of reliability and transmission upgrades to support the expanded set of transmission rights are imposed on the transmission system operator or somehow collected in socialized charges applied to all customers.
market models can in principle provide an efficient dispatch for generators and respect the transmission constraints, but they do not by themselves align the incentives of customers. By contrast, the integrated locational market design provides a straightforward alignment of incentives. With a retail rate design that passes through the locational price of energy, the smart device would have smart prices that aligned with the incentives of the consumer and the systems costs. Some distortions would remain to the extent that distribution or other customer service fixed costs are collected in variable rates, but the distortions would be small given the large changes in energy prices that would or should be the norm during peak periods. The smart grid needs smart pricing. Market models that suppress price information, especially in prices of final delivery to the consumer, would require new and sometimes complex rules to undo the consequent perverse incentives. By contrast, the integrated locational market design intentionally links the market and operations in order to provide the right price signals.
Resource AdequacyA central function for vertically integrated electric utilities has always been to invest in anticipation of electricity demand. The club members of vertically integrated companies developed rules for investing while sharing the responsibility to maintain reliability. In practice, this amounts to a responsibility to build or contract for energy generation capacity adequate to provide a specified planning reserve margin (e.g., 15% of estimated peak load) that would ensure sufficient capacity in the event of normal outages for maintenance and some temporary equipment failures. This obligation continues for the vertically integrated utilities. However, the situation has become more complicated in the market models where generation has been separated from transmission, distribution, and final supply to the customers. The success of these models in achieving resource adequacy is neither certain nor resolved. The early days of restructuring, at least in the United States, saw a surge of new construction of
more efficient generating plants. This created excess capacity. When coupled with bad market design and rising oil and natural gas prices, this capacity was not economic, and the result was a major contraction in investment and significant financial losses for some generators. In one sense, this was the intended consequence of electricity restructuring in shifting the burden of risks to voluntary investors rather than imposing them on customers. The excess investment was not a good thing, even if a good deal of the problem was caused by the incentives of bad market design rather than the reliance on markets per se. The result has been a concern that in the future, investment in generating capacity in electricity markets would not be adequate. This concern has been strongly reinforced by the regular demonstration that the returns to investors in the energy and ancillary services prices have not been adequate to support new investment.3 Although it is the anticipated returns that should govern, not the realized returns in a market with excess capacity, the low realized returns are sobering. The consensus analysis is that energy prices in many electricity markets have been too low, and they are especially low during periods of capacity scarcity, such as during the peak hours of the day. The response to this concern has been varied. In some markets, notably New York, PJM, and New England, a capacity product and an associated capacity market have been created. The essence of the capacity market has been to construct a forecast of future loads and investments, and then conduct an auction for existing and new generators, or demand response, to offer capacity to meet those requirements. The full description of how to define capacity and ensure its future deliverability on the grid is another task. However, it is clear that this capacity construct is an attempt to re-create something like the responsibility of the vertically integrated utility, but now under the management of the independent system operator. The difficulties of defining and measuring capacity for new low-carbon technologies such as wind, solar, and load management are particularly relevant. As discussed below, the variable availability of renewable resources and the problem of
defining baseline load make it hard to map them into the equivalent savings in conventional fossilfuel generation capacity. In part, the problems are inherent in capacity markets where the product is not easy to define or measure, but the difficulties are exacerbated by the substantial differences in the technologies. At the other end of the spectrum has been the recognition that inadequate scarcity pricing is the root cause of the deficient incentive for investment.4 Markets such as in Australia and Texas have mechanisms to allow prices to rise during times of scarcity on the assumption that this will be sufficient to meet the needs of resource adequacy. Nothing fundamental requires a choice between improved scarcity pricing and adopting well-designed capacity markets. Better scarcity pricing would provide benefits in improved operations as well as market incentives for investment. Furthermore, better scarcity pricing in markets would complement the designs of capacity markets to account for actual or expected scarcity payments (Hogan 2006). The design of better scarcity pricing systems is an active agenda item in the RTO markets in the United States (ISO-NE 2006; MISO 2009; NYISO 2008). The concern with resource adequacy may be more acute in the United States than in the EU market design models. All of the U.S. models include procedures to monitor and mitigate the exercise of market power, primarily through the application of offer caps. These procedures, coupled with many other market design nuances, accumulate to depress energy market prices. By comparison, the EU market is more opaque in this dimension, more inclined to structural interventions such as divestiture, and less inclined to directly intervene in the bidding process. The incentive to exercise market power is material, and the opportunity may be greater in the EU context. The net result is a more mixed picture about the adequacy of incentives for new generation investment. Furthermore, at the moment, the more aggressive policies in some EU countries for introducing renewable energy sources, particularly wind, have produced excess capacity and raised total system costs, but the increase in capac-
ity depresses energy prices as they appear in the nominal market (Traber and Kemfert 2009).
Infrastructure InvestmentProviding generation resource adequacy is only part of the necessary development of the electricity system. Other necessary infrastructure development includes transmission grid expansion and deployment of system upgrades in the form of better information, smarter controls, and smarter metering. In the vertically integrated electric system, these investment decisions were and remain primarily the responsibility of the electricity monopoly, subject to regulatory approval. The restructured markets, however, present a different mix of choices, and in many respects responsibility for infrastructure investment is unresolved. A principal purpose of development of electricity markets was to provide better incentives and opportunities for decentralized decisions with a corresponding allocation of risk and reward for investments in generation plants and end-use efficiency. In an idealized electricity market structure, energy market prices would be sufficient drivers of generation and end-use investment. As discussed above in the section titled Resource Adequacy, the less-than-ideal reality has not yet achieved this goal for generation, and similar problems arise in providing the incentives for end-use investment. The case for transmission and related infrastructure investment is even more of a challenge. The transmission grid and associated dispatch functions of the system operator continue as natural monopolies. Even in the idealized version of electricity market design, it could be difficult to fashion a regime that relied on market-based transmission infrastructure investment (Hogan 2009). Accordingly, it is widely (but not universally) assumed that some residual monopolya transmission owner, system operator, or hybrid special-purpose organizationmust make infrastructure investment decisions, cause the expansion to be implemented, and then use the compulsion through regulation to make the erstwhile unwilling beneficiaries pay.
If the beneficiaries were willing to pay, they could in principle organize to make the transmission investment without the coercive power of regulation. In some cases, market participants do make their own decisions to invest and acquire incremental transmission rights (Hogan 2009). In other cases, however, it may well be that no coalition of beneficiaries could be assembled to undertake desirable transmission investment. In these cases, a transmission company, the system operator, and the regulator must cooperate to implement investment plans. Designing a hybrid system that could accommodate both market-based and regulatory-driven infrastructure investment is a major challenge for liberalized electricity markets. In some regions, the assumption is that such a hybrid system is impossible and only the transmission company can or will make investments. Other regions have seen a struggle to find a workable compromise. An interesting case is the hybrid transmission expansion framework adopted under the New York Independent System Operator (NYISO), modeled after the transmission investment experience in Argentina. In essence, the NYISO model allows for decentralized investment when the beneficiaries voluntarily assume the responsibility to pay. In cases where the dispersion of benefits makes it difficult or impossible to assemble a coalition of the beneficiaries, the system operator and regulators can go forward subject to a supermajority endorsement where the beneficiaries vote and pay for the investment in proportion to the estimated benefits (Budhraja et al. 2008; NYISO 2007). An important feature of both investment avenues is that beneficiaries pay and the costs are not socialized, which provides good incentives and is compatible with the framework for investment in generation and end use without creating conflicting subsidy regimes. The ability to develop an adequate hybrid infrastructure investment framework depends on the rest of the market design. In systems with broad energy regions with a common nominal energy product and a single price, the details of transmission congestion are hidden and market signals are weak (between regions) or absent (within regions). In these cases, market partici-
pants have no incentive to invest, and a transmission company monopoly may be the only workable system. For example, in England and Wales, the single transmission owner National Grid is responsible for designing, implementing, and charging for transmission investment under the supervision of the regulator. The United States, on the other hand, has a mixture of evolving infrastructure investment schemes. New England follows close to a central-planning and full-cost socialization system, whereas New York, PJM, Texas, the Midwest, and California have various hybrid models in place or in flux. The details of each of these systems can be complicated, but a general principle connects to the underlying market design. By construction, the more aggregated the market design, the less articulated the investment incentives for transmission. Within aggregated zones, market participants have no incentive to invest because transmission constraints are assumed away. Between aggregate zones, transmission investment incentives may exist, but the price information in the market would only dimly reflect the underlying reality of the transmission interactions. Only in the case of the fully locational integrated market designs could energy prices furnish accurate information about transmission congestion that provides a framework for awarding feasible transmission rights. But these market designs result in efficient infrastructure investment decisions only when the investment itself has relatively limited impact on market prices. Larger investments that could materially change expected market prices may then require a hybrid framework like the New York model.
and storage offer a variety of nontraditional operating characteristics that could affect electricity systems and markets. Energy efficiency and load management investments could materially alter the load profile and interact strongly with the portfolio of generation and transmission assets. Desirable policy for pursuing low-carbon technologies depends in large part on the associated choices in market design. In some cases, such as nuclear and hydropower, the technology is familiar and well understood. In others, such as carbon capture and storage (CCS) or expanded cofiring of biomass, although costs may be different, nothing is obviously fundamentally different from existing fossil-fuel generation that could alter market design. In still other cases, such as wind or solar, the operating characteristics are significantly different. At a small scale, almost any new generation technology can be absorbed, but large-scale integration of the newer low-carbon technologies is more complex. And some anticipated changes, such as the development of smart grids, smart appliances, and electrification of the transportation sector, could require or facilitate major changes in system operations and market design.
cation of risks. Part of the animating idea was to promote innovation, entry, and flexibility in the electricity system. New technologies and new operating procedures would arise as a result of market incentives and decentralized decisions. Incentives would be better aligned to support innovation in both end use and generation facilities, and innovation would increase. Rewards would go with the risks, and the market participants would do a better job of euthanizing bad investments as well as capitalizing on what works. The more innovative the idea, the less likely it would be adopted under the regulated vertically integrated organization. Regulation works better when the choices are highly standardized and easy to evaluate. But when the product includes many moving parts or involves a great deal of uncertainty, it can be much harder to write down the rules. In these cases, business judgments can be made and the uncertainty priced, with the associated risks and rewards. For example, consider the case of EnerNOC, an innovative aggregator of demand response services, and its demand product, which provides operating reserves in markets such as PJM (EnerNOC n.d.). The de facto product provided to PJM behaves essentially like reliable standby generating reserve that can be called on to meet gross load or reduce net load at a set of locations. The actual service, hidden from view of the system operators, is a complicated array of contracts and operating agreements with end users that allow a variety of interventions with various technologies to reduce or temporarily shift electricity load. These contracts are negotiated privately, and voluntarily, to share risks and rewards. There is no need for regulators to approve the complex and varied terms and conditions of the individual contracts. EnerNOC takes a reasonable business risk, constrained in its returns through the possibility of competition from others in providing similar services. PJM gets innovative access to cost-effective operating reserves that fit naturally under the market design. The market design stimulated a market response, but the rules did not prescribe the result. This broad motivation for electricity market reform is greatly reinforced by the challenges of developing a low-carbon electricity system. Car-
bon emissions have a long duration in the atmosphere and will affect the climate for centuries after their sources have been retired, and generating plants may operate for many decades. Hence we are interested in impacts over very long timescales, and we are hoping for and expecting dramatic innovations in technology and operating procedures. Over this timescale, enormous uncertainty exists about what will work and how the system will operate in practice. The biggest surprise would be if there were no major surprises. In this setting, it is even more important to engage the creativity and innovations of the many current and potential market participants rather than rely on the wisdom of a few central planners. If we knew what to do, the central planners could execute. But because we do not know exactly what to do, we need an electricity market design that facilitates learning. In the face of great uncertainty, the signals and incentives should be as technology-neutral as possible. Rather than prescribe or constrain the choices of technology, market operations and prices should reflect the real costs incurred. To the extent possible, externalities should be internalized. For example, rather than mandate technologies, we should put a price on carbon that should affect technology choices. Among the three market models, the vertically integrated model is most like central planning, and the integrated locational market design is most attuned to reflecting real costs associated with system operations. The aggregated countertrade models fall somewhere in between, with more opportunities for decentralized investment decisions but more muted incentives to guide those investments. The need to support innovation, to pursue technologies, investments, and consumption choices not yet envisioned by anyoneincluding by the central plannerprovides a powerful motivation for getting the signals and incentives aligned to support decentralized decisions. By contrast, the more the electricity market design relies on socialized costs, muted incentives, and regulatory mandates, the more the central planner assumes the burden for making investment decisions and applying regulatory compulsion to recover the costs.
higher costs, adoption will require some form of subsidy. If the higher cost is in the form of high investment costs, tax credits or other capital subsidies could be offered in many ways. Once the subsidy is provided, the new generation plant or end-use investment can participate in the ongoing electricity market on the same basis as other investments. If the source of the higher cost is a higher operating cost, the interaction with the market design may be more immediate. Consider, for example, an end-use technology such as load management to reduce consumption when the avoided cost of electricity is very high. If the market design does not provide the appropriate price signals that capture the avoided cost in the system, often over relative short intervals, enough incentive may not exist to pursue load management even if the up-front investment cost is low or subsidized. Here both the temporal and locational granularity of prices would be important. Similar challenges would arise for generation investments such as pump storage, flywheel storage, or pondage hydro, which could smooth out effective load curves but face high effective operating costs. If the market design does not offer sufficient granularity of price determination, then other cost-effective low-carbon technologies would be too expensive to operate or would require regulatory mandates for adoption.
impact on peak hours, and the random outages that cannot be controlled have a relatively low probability. By contrast, the implied effective capacity factor of 30% or below for individual renewable facilities is equivalent to random outage probabilities of 70% or more. The correlation of these outages with peak and near-peak loads would be important, but the basic intermittency is a hurdle for some low-carbon technologies. Another aspect of variable availability of lowcarbon technologies is the speed at which they come on or, more important, go off the system. This ramp rate can present material operational challenges for the system. For small-scale penetration of the low-carbon technologies, not much more need be done than include this variability as part of the natural variability of residual load. But on a large scale, the challenge would be different depending on the dispatchability of the plant. A common answer is that the individual variability of solar or wind facilities is less of a problem when regional diversification of sources is sufficient where winds speeds are not correlated. It is true that the correlation reduces across wider areas (Holttinen et al. 2009).6 However, exploiting this portfolio effect depends importantly on the configuration of the transmission grid as discussed below under Green Infrastructure Development. In principle, for the integrated locational market design, variability in production does not present much of a design problem. Securityconstrained economic dispatch would impose limits on the output of the plants and reduce prices for energy the more that capacity has to be reserved to meet the potential changes in production. For example, this might involve restricting output from wind facilities when the wind is blowing hard to keep the combined output of the wind farm below the level that the system could handle if the wind suddenly drops. The net effect would be to reduce the locational price of energy and increase the locational price of operating reserves. Most of the cost of variability would then fall on the wind supplier, with the associated incentives to invest in storage or other backup options.
This appeal to the theory of integrated locational market design contradicts the common assumption that windmills should generate whenever the wind is blowing. Although this is not correct as an economic matter, because at times free energy is too expensive, widespread concern exists that investment in low-carbon technologies should be combined with other investments to ensure that the power can flow when it is available. The market design implications of the intermittency of supply are familiar. The more granularity, over time and location, in the prices and dispatch, the easier it is to integrate low-carbon technologies. The better the prices reflect avoided costs, the easier it would be to integrate such technologies, especially those with high operating costs. The greater variability of supply for key lowcarbon technologies and increased end-use response become more and more important the greater the investment in smart grids. As discussed above, smart grids require smart prices. Smarter prices would better reflect scarcity conditions, locational differences, and dynamic response. In the absence of sufficiently granular prices and quantity definition, incentives for low-carbon investment diverge from the real costs of system operations. The details will be different in different markets, but the greater the gap, the more the reliance on regulation and central mandates to support low-carbon technologies.
tion are more problematic. In the case of New York, the large potential wind resources are in the northwestern region of the state, and the major load center is around NewYork City. Controversy has arisen about whether to build new transmission and who should pay. The integrated locational pricing design sends clear signals about the potential benefits of expanded transmission, but so far the benefits have not been seen as worth the costs. Under the innovative New York transmission investment rules, the beneficiaries must pay and must approve in advance according to supermajority voting. In the case of the proposed New York Regional Intertie, designed to exploit upstate wind, the developers withdrew the plan on the grounds that it would not receive sufficient support from the putative beneficiaries to obtain approval (Puga and Lesser 2009). One of the challenges for transmission investment is the intermittency of wind and solar energy production. A standard argument is that diversification of sources of supply across large regions where wind and cloud cover are not perfectly correlated would provide a sufficient portfolio effect to mitigate the intermittency. What this argument must confront is the level of transmission investment required. For the diversification argument to hold fully, transmission capacity must be sufficient to accommodate the maximum output of all of the sources. In the case of dedicated long-distance lines to bring power to load, each line must be sized to be fully utilized only a fraction of the time. Otherwise the full diversification benefit would not be realized. This surge capacity investment in transmission, and planned underutilization, could materially add to the costs of interconnection for remote low-carbon energy technologies (NAS Committee 2009). The more the market design adheres to the granularity of time and location, the greater will be the transparency of incentives for transmission investment. The more the market design aggregates across time and location, the more hidden will be the benefits of transmission. The higher the degree of aggregation, the greater will be the tendency to socialize the cost of transmission. Movement toward the integrated locational market design facilitates a beneficiary-pays system
of transmission expansion and supports other features of market investment decisions. Without a beneficiary-pays protocol, there is no principled answer to why the subsidies to transmission should not be extended to demand and production alternatives that are partial substitutes for transmission expansion. A beneficiary-pays protocol for transmission is an important long-term component of market design. As ready examples in Texas and New England demonstrate, however, the use of an integrated locational market design for the energy market is no guarantee that transmission investment will be market-driven and not socialized.
sure to future political risk (Neuhoff et al. 2009). Major investments in expensive low-carbon technologies would be profitable only if sustainable public policy ensures a significant implicit or explicit price on carbon emissions that is expected to remain high enough for long enough to recover the substantial investment. Sustainability of public policy will depend in part on compatibility with the basic market design. For example, an economywide cap on carbon emissions would be largely technologyneutral and would integrate well with efficient market operations. Investors in a particular technology would not need to worry as much that their specific venture would be subject to idiosyncratic subsidy changes. In order to change the economics of the individual investment, the carbon cap would need to be changed for all emissions, and this is less likely than a change in a targeted subsidy. By contrast, the boom-and-bust cycle of solar deployment in the United States, driven by the dependence on periodic reauthorization of targeted tax credits for solar investments, illustrates the risk of developing a sustained commitment to investment based on special-purpose support (NAS Committee 2009). Comprehensive carbon emission caps and good market design would reinforce the sustainability of the system.
may have little connection with the public benefits of learning. For instance, the case of the California solar rooftop initiative is instructive. Launched with a promise to install solar PV on a million rooftops by 2015, the idea was to both reduce carbon emissions and capture the benefits of learning by doing. However, whereas the assumed learning rate is an input, the necessary scale of the investment should be an output of the analysis and not a constraint on the policy. In the California case, further analysis revealed both the implied trajectory of the optimal subsidies and investment profiles as a function of the assumed learning rate. A central conclusion was that the optimal subsidies would be associated with investment at only about a quarter of the scale of the nominal million-rooftop target. Higher learning rates needed less initial subsidy, and lower learning rates might not produce enough benefit to justify any material subsidy (van Benthem et al. 2008). Proper design of support policies to capture the benefits of learning by doing interact with market design in straightforward ways. The more transparent the market design and the clearer the market signals, the easier to achieve the benefits of the learning and know whether the benefits are appearing. In the energy market, the better the price signals, to include scarcity costs, the better the opportunity for private investment to drive the benefits of lower costs.
presents a number of difficulties, some of which interact with the nature of the market design. The standards often lack a principled basis. A cap on carbon emissions, where the emissions of carbon can be measured and controlled, is technology-neutral. By contrast, renewable energy standards require selecting the acceptable technologies. The result has been a substantial degree of political bargaining, with variation across jurisdictions revealing the fundamental disagreements about goals and means. For instance, in the United States, state programs define a wide array of more than a dozen possible renewable technologies, yet states agree on only three technologies: biomass conversion, solar photovoltaic and wind (Michaels 2008, 10). The renewable energy standards also suffer from confusion about objectives and a poor mapping between policy and goals. Requiring minimum penetration standards to support early development of expensive or risky renewable technologies is akin to the quantity target and subject to the comments above about the mismatch between learning-by-doing justification and quantitative targets. Or consider the justification that the technology would never be competitive on its own, and the minimum standard is intended to reduce carbon emissions. Although this approach may have an appeal in the absence of a carbon cap, the argument no longer applies in the presence of a cap. As soon as there is a binding cap on carbon emissions, a renewable electricity standard (RES) produces no incremental carbon emission benefits. If the cap is binding, the effect of an RES is to require more expensive investments that reduce carbon emissions and therefore lower the cost for everyone else and every other technology in meeting the carbon cap. Hence the proposed 33% RES by 2020 in California should raise costs of control in the state, to the benefit of the rest of the country.9 But once a cap-and-trade system is in place, carbon emissions would be the same as without the RES. In effect, the RES lowers the market price for carbon emission permits and raises the social cost of reducing carbon emissions but does not lower carbon emissions.10
In market designs without vertical integration, an RES presents the challenge of how to implement and enforce the technology constraint. One approach is to create RES certificates and require all buyers or generators to redeem a certain percentage of RES generation credits or pay a tax (RECS n.d.). The price of the credits is then a factor in the price of electricity and alters the economic dispatch. However, the connection with carbon emissions is imperfect, as low-carbon technologies such as nuclear are often excluded from RES credits. Thus the preferred cap-andtrade system would have parallel trading systems to control emissions and promote renewability. There is nothing inherently infeasible about having multiple permits systems. For example, the carbon permit would be layered on top of tradable sulfur dioxide emission permits. But the added complexity does raise a question about the incremental benefits of RES mandates.
but the interaction with energy market design would be disruptive. In the case of the vertically integrated monopoly, there would be less of a problem. By assumption, the monopoly can internalize all the untoward effects in the production and delivery chain and ignore the internal transfer prices. By contrast, the market structures now based on bid and offers in a framework of securityconstrained economic dispatch would confront a substantial disconnect between the economic prices applied in the settlements system and the opportunity costs of changes in the dispatch. As much experience has shown, whenever market prices used in settlements systems diverge materially from the costs of real system operations, arbitrage opportunities and temptations are created that could undermine the dispatch and the market. It would be possible to impose a constraint on emissions in the dispatch (over very short intervals), and then apply the usual principles of economic dispatch subject to the emissions cap. This would produce consistent market prices with scarcity components reflecting the cost of carbon emissions implied by the constraint. In effect, this would replace an economywide cap on emissions, allowing implicit trading across time and space, with a new cap-and-tax system with short-term caps and implicit taxing at the implied variable carbon price. Absent a formal electricity-only cap-and-trade system that allowed intertemporal balancing, the system operator would have to decide on the limit for virtually every dispatch period. It would be reasonable to anticipate that the implicit (very) short-term carbon price would be highly volatile. A far simpler approach would be if the cap on aggregate emissions and the implied carbon price in the economywide cap-and-trade system were sufficient to internalize the cost of carbon. Then the carbon emission permit costs, along with all other costs for fuels and other emissions, would be treated the same in the security-constrained economic dispatch. In the integrated locational market design, the associated locational marginal prices would include the effects of carbon emissions. There would be no additional need for an
incremental green dispatch that could break the connection between market design and real operations.
Feed-In TariffsGreen energy mandates that require system operators to accept energy from approved technologies at approved fixed prices, known as feed-in tariffs, have been widely adopted in the EU. Not surprisingly, with high enough prices, such mandates have been highly successful at increasing the market share of approved sources such as wind and solar installations (Muoz et al. 2007). However, the structure of the feed-in tariff creates collateral damage similar to the effects of green uneconomic dispatch, and it is not clear whether the model is consistent with developing sustainable growth of low-carbon investments. Set aside the issue that the new technologies are expensive and the expense is becoming a matter of policy concern in the EU.11 The feed-in tariff interacts poorly with the rest of the market design in terms of system operations, price signals, and infrastructure investment. The problem with system operations appears because the system operator must take the energy input, even when the resulting power flow might foreclose other generation alternatives, except in emergency conditions. In the case of wind, for example, the variable cost of wind energy may be zero, but this does not mean that it is always costeffective to accept the wind energy. Conditions occur on the grid where the value of the wind energy is negative. In effect, the feed-in tariff forces wind energy onto the grid and drives the implicit (in the EU) or explicit (in the U.S. RTOs) locational price of energy substantially below zero. This is a regular event in PJM, for example. Because the mandate is to take the green energy, the burden of investing in transmission connections and upgrades falls on someone else, either on the transmission company or by some method of cost socialization through the system operator. Besides the obvious total cost inefficiencies, this cost socialization distorts the choices among renewable energy resources.
The simple alternative to the feed-in tariff would be some mix of investment tax credit or direct subsidies that reduce the cost of investment (but not operations). In order to provide incentives for operations, collection of the subsidy could be conditioned on offering the capacity in the real-time market, whereupon the usual principles of security-constrained economic dispatch would apply. Note that conditioning the subsidy on offering the energy to the market is not the same thing as conditioning the subsidy on the energy actually produced. System operators have good experience in discriminating good-faith offers of energy and need not actually dispatch the plant whenever it is offered to verify its bona fides. But if the subsidy is conditional on producing the energy, the effect would be to convert the fixed investment subsidy into a variable opportunity cost. In this case, the wind or solar plant could profitably enter a negative bid (bounded below by minus the variable subsidy value) to ensure it was dispatched. This would distort prices and exacerbate the problem of the technology subsidies sending the wrong signals to the market.
Transmission InvestmentA major challenge remains in defining the appropriate protocols for transmission investment. The general problems of constructing a satisfactory hybrid system, as outlined above, are magnified by the perception of many that a major expansion of transmission capacity is required to build the long-distance connectors between loads and the major potential sources of renewable energy (NAS Committee 2009). The principal implication for green energy mandates would be to decide on the degree to which transmission costs are considered in the competition among green energy resources. Which major expansions of transmission systems are necessary is far from obvious. Solar rooftop installations may require relatively little, and certainly different, transmission investments than distant wind or solar installations. Renewable energy sources are partial substitutes for each other, and there is no reason to believe that every renewable
option should be pursued. It follows that the model for transmission investment and cost allocation could have a major impact on the choice of renewable technologies. If transmission investment cost is socialized, for everything or even only for renewable sources, then distant wind will look cheaper and nearby rooftop solar more expensive, even when the opposite ranking may be true. An obvious principle to apply to green transmission investment, as for any transmission investment, would be to adhere to a beneficiary-pays system (Hogan 2009). If the requirement to pay for transmission makes a preferred renewable technology more expensive, then this can be addressed directly in the balance of subsidies. In the presence of a carbon cap, with the cap accepted as the proper balance of carbon costs and benefits, no good reason exists to treat green transmission investment any differently than other transmission investment.
ConclusionsPolicy supporting low-carbon technologies and end-use choices in the electricity sector interacts with electricity market design. Great uncertainty remains about the appropriate investments, and the consensus is that achieving a low-carbon future will require innovation and invention. Success is more likely if appropriate price signals and incentives appear to drive decentralized decisions of the many participants in electricity markets. Placing a price on carbon is a critical step. Getting the resulting incentives right will be easier the closer the electricity market design reflects the reality of electricity system operations. The experience with electricity market alternatives points to the integrated locational electricity market design as the only workable model that is compatible with open transmission access and nondiscrimination.
open-access rules lack the critical ingredient of nondiscriminatory access to economic dispatch. 2. Likewise, administrative aggregation without changing the underlying grid does not increase competition or reduce market power. 3. PJM reports that on average between 1999 and 2008, a combustion turbine would have earned 43% of its fixed charges in energy net revenue (Monitoring Analytics 2009). 4. The causes of inadequate scarcity pricing are many, including but not limited to regulatory offer caps on supply (Hogan 2005). 5. If you know what to do, do it! If you dont know what to do, decision analysis can help you decide. Aphorism from Professor Ron Howard of the Department of Management Science and Engineering at Stanford University. 6. This portfolio effect does not eliminate cases such as a night when the sun is down nor necessarily when even a large area can be becalmed and remove all the wind generation (NERC 2009). 7. Texas Utilities Code 36.053 (d), available at http://codes.lp.findlaw.com/txstatutes/UT/2/B/ 36/B/36.053 (accessed February 20, 2010). 8. Initial designs in the EU included allocations for new power plants as a set-aside under the total cap. This creates the incentive to build new plants and raises the cost for existing plants by reducing the net cap for the existing fleet. 9. California Executive Order S-1408, November 17, 2008, available at www.gov.ca.gov/executiveorder/11072/ (accessed February 20, 2010). 10. The argument is slightly different for a carbon tax, where the price of carbon is fixed. Then an RES adds to costs by requiring use of more expensive low-carbon technologies, but it does reduce carbon emissions. 11. According to ENDS Europe (2009): The new German coalition government will cut feed in tariffs for solar power by more than the planned 8% reduction for 2010, according to a 128-page policy document published on Friday [October 23, 2009]. The tariffs will now be reviewed every three years.
Notes1. In the U.S. case, vertically integrated utilities are subject to certain open-access provisions, but these
ReferencesAmin, M. 2000. National Infrastructures as Complex Interactive Networks. In Automation, Control, and
Complexity: An Integrated Approach, edited by Tariq Samad and John Weyrauch. New York, John Wiley and Sons, 263286. Anadon, Laura Diaz, and John P. Holdren. 2009. Policy for Energy Technology Innovation. In Acting in Time on Energy Policy, edited by Kelly Sims Gallagher. Washington, DC: Brookings Institution Press, 89127. Boucher, Jacqueline, and Yves Smeers. 2002. Towards a Common European Electricity Market Paths in the Right Direction Still Far from an Effective Design. Journal of Network Industries 3 (4): 375424. Budhraja, Vikram, John Ballance, James Dyer, Fred Mobasheri, Alison Silverstein, and Joseph H. Eto. 2008. Transmission Benefit Quantification, Cost Allocation and Cost Recovery. Sacramento, CA: California Energy Commission, Energy Research and Development Division. Dubash, Navroz K. 2002. The Changing Global Context for Electricity Reform. In Power Politics: Equity and Environment in Electricity Reform, edited by N.K. Dubash. Washington, DC: World Resources Institute, 1130. ENDS Europe (Environmental Data Services Europe). 2009. New German Coalition Confirms Solar Subsidy Cuts. www.endseurope.com/22460 (accessed February 20, 2010). EnerNOC (Energy Network Operations Center). No date. EnerNOC: Get More from Energy. www.enernoc.com/ (accessed February 20, 2010). ENTSO-E (European Network ofTransmission System Operators for Electricity). 2008. Regional Group Continental Europe, StatisticalYearbook. Brussels, European Network of Transmission System Operators for Electricity. EPSA (Electric Power Supply Association). 2008. EPSA Statement on Renewable Energy in Organized Electricity Markets. Press release. Washington, DC: EPSA. Grubb, Michael, Tooraj Jamasb, and Michael G. Pollitt. 2008. A Low-Carbon Electricity Sector for the UK: What Can Be Done and How Much Will It Cost? In Delivering a Low-Carbon Electricity System, edited by Michael Grubb, Tooraj Jamasb, and Michael G. Pollitt. Cambridge, UK: Cambridge University Press, 462497. Helman, Udi, Harry Singh, and Paul Sotkeiwicz. 2010. RTOs, Regional Electricity Markets, and Climate Policy. In Generating Electricity in a Carbon-Constrained World, edited by Fereidoon P. Sioshansi. Burlington, MA: Academic Press, 527563.
HEPG (Harvard Electricity Policy Group). Various dates. RTO annual reports. www.hks.harvard.edu/ hepg/rlib_rp_RTO_ISO_reports.html (accessed February 20, 2010). Hogan, William W. 1992. Contract Networks for Electric Power Transmission. Journal of Regulatory Economics 4: 211242. . 1999. Getting the Prices Right in PJM: Analysis and Summary: April 1998 through March 1999, the First Anniversary of Full Locational Pricing. Cambridge, MA: Harvard University. www.hks.harvard.edu/fs/whogan/pjm0999.pdf (accessed April 16, 2010). . 2002. Electricity Market Restructuring: Reforms of Reforms. Journal of Regulatory Economics 21 (1): 103132. . 2005. On an Energy Only Electricity Market Design for Resource Adequacy. Cambridge, MA: Center for Business and Government, John F. Kennedy School of Government, Harvard University. www.hks.harvard.edu/fs/whogan/ Hogan_Energy_Only_092305.pdf (accessed April 16, 2010). . 2006. Resource Adequacy Mandates and Scarcity Pricing (Belts and Suspenders). Comments submitted to the Federal Energy Regulatory Commission. Docket Nos. ER05-1410-000 and EL05148-000. www.hks.harvard.edu/fs/whogan/ Hogan_PJM_Energy_Market_022306.pdf (accessed April 16, 2010). . 2009. Electricity Market Structure and Infrastructure. In Acting inTime on Energy Policy, edited by Kelly Sims Gallagher. Washington, DC: Brookings Institution Press, 128161. Holttinen, Hannele, Peter Meibom, Antje Orths, Bernhard Lange, Mark OMalley, John Olav Tande, Ana Estanqueiro, Emilio Gomez, Lennart Sder, Goran Strbac, J. Charles Smith, and Frans van Hulle. 2009. Impacts of Large Amounts of Wind Power on Design and Operation of Power Systems, Results of IEA Collaboration. 8th International Workshop on Large Scale Integration of Wind Power into Power Systems as Well as on Transmission Networks of Offshore Wind Farms. October 2009, Bremen, Germany. IEA (International Energy Agency). 2007. Tackling Investment Challenges in Power Generation in IEA Countries: Energy Market Experience. Paris: OECD Publishing. ISO-NE (ISO New England). 2006. FERC Electric Tariff No. 3, Market Rule I, Section III.2.7.
Joskow, Paul. 2008. Challenges for Creating a Comprehensive National Electricity Policy. Speech given to the National Press Club. September 2008, Washington, DC. www.hks.harvard.edu/hepg/Papers/ Joskow_Natl_Energy_Policy.pdf (accessed February 20, 2010). Lankford, Craig B., James D. McCalley, and Narinder K. Saini. 1996. Bibliography on Transmission Access Issues. IEEE Transactions on Power Systems 11 (1): 3040. Michaels, Robert J. 2008. A Federal Renewable Electricity Requirement, Whats Not to Like? Policy Analysis No. 627. Washington, DC: Cato Institute. MISO (Midwest ISO). 2009. FERC Electric Tariff, Vol. No. 1, Schedule 28. Monitoring Analytics. 2009. 2008 State of the Market Report for PJM. Vol. 2, Detailed Analysis. www.monitoringanalytics.com. Muoz, Miquel, Volker Oschmann, and J. David Tbara. 2007. Harmonization of Renewable Electricity Feed-In Laws in the European Union. Energy Policy 35: 31043114. NAS Committee (Committee on Americas Energy Future). 2009. National Americas Energy Future:Technology and Transformation. Washington, DC: National Academy of Science. NERC (North American Electric Reliability Corporation). 2009. Special Report:Accommodating High Levels of Variable Generation. Princeton: NERC. Neuhoff, Karsten, Sam Fankhauser, Emmanuel Guerin, Jean Charles Hourcade, Helen Jackson, Ranjita Rajan, and John Ward. 2009. Structuring International Financial Support to Support Domestic Climate Change Mitigation in Developing Countries. Cambridge, UK: Climate Strategies, www.climatestrategies.org. NYISO (New York Independent System Operator). 2007. Order No. 890 Transmission Planning Compliance Filing. Docket No. OA08-13-000. Cover Letter Submitted to Federal Energy Regulatory Commission, December 7, 1415.
. 2008. Ancillary Service Manual, Vol. 3.11, Draft, 6-196-22. PJM 2009. PJM Open Access Transmission Tariff, Subpart A: Interconnection Procedures. 6th Rev. Vol. No. 1, Para. 36.1. Potomac Economics. 2009. 2008 State of the Market Report for the ERCOT Wholesale Electricity Markets. www.hks.harvard.edu/hepg/Papers/RTOISO_reports/ 2008_ERCOT_SOM_REPORT_Final.pdf (accessed April 16, 2010). Puga, J. Nicolas, and Jonathan Lesser. 2009. Public Policy and Private Interests: Why Transmission Planning and Cost-Allocation Methods Continue to Stifle Renewable Energy Policy Goals. Electricity Journal, 22 (10) (December): 719. RECS (RECS International Association). No date. Renewable Energy Certificate System. www.recs.org (accessed February 20, 2010). Rudkevich, Aleksandr. 2009. Economics of CO2 Emissions in Power Systems. Boston: CRA International. www.hks.harvard.edu/hepg/ (accessed February 20, 2010). Schweppe, F. C., M. C. Caramanis, R. D. Tabors, and R. E. Bohn. 1988. Spot Pricing of Electricity. Norwell, MA: Kluwer Academic Publishers. Sioshansi, Fereidoon P. 2008. Competitive Electricity Markets: Design, Implementation, Performance. Oxford: Elsevier. Stavins, Robert N. 2007. A U.S. Cap-and-Trade System to Address Global Climate Change. Hamilton Project discussion paper 200713. Washington, DC: Brookings Institution. Traber, Thure, and Claudia Kemfert. 2009. Gone with the Wind? Electricity Prices and Incentives to Invest in Complementary Thermal Power Plants under Increasing Wind Energy Supply. Berlin: German Institute for Economic Research. van Benthem, Arthur, Kenneth Gillingham, and James Sweeney 2008. Learning-by-Doing and the Optimal Solar Policy in California. Energy Journal 29 (3): 131 152.
8Energy Regulation in a Low-Carbon WorldRichard Green
ncreasing the use of renewable energy will have significant impacts on the operations of energy utilities, the companies that deliver electricity and gas to consumers using networks of wires and pipes. As discussed in Chapter 2, many renewable energy technologies are very different from the traditional large-scale, centralized technologies that utilities have relied on for more than 100 years. Electricity transmission and distribution system operators will have to become used to taking power from many smaller generators, distributed around the system, while larger-scale renewable generators are often sited far away from the major loads. Other renewable electricity may be self-generated by consumers and firms, and electric utilities would then provide a residual service or perhaps install and maintain the self-generators. Gas utilities may find that the demand for their product falls significantly as consumers switch to renewable sources of heat. If the utilities operations are changing, should this affect their regulation? Some principles of regulation, such as the need for transparency and stability, will surely remain unchanged. What about the way in which these principles are put into practice? Should the scope of regulation, the activities subject to an economic regulator, be broadened (or narrowed) as the amount of renewable generation grows? Should the regulators
objectives, or the factors it takes into account when deciding how to achieve those objectives, change? How are its decisions likely to change? This chapter begins with a discussion of the scope of economic regulation and its interaction with energy policy. It then presents four models of economic regulation for energy utilities: a retail competition model, used in the Nordic countries, the United Kingdom, and some U.S. states (such as Texas), in which only the natural monopoly in delivery (transmission and distribution) is regulated; a wholesale competition model, in which unregulated generators provide power, but the prices paid by (at least some) retail customers are regulated (as in France, Spain, and some other U.S. states); a single buyer model, in which a regulated entity is responsible for procuring power through competitive tenders (as in some developing countries); and an integrated firms model, in which regulators have to oversee most of the industrys value chain (as in those parts of the United States that have not adopted liberalization policies). The chapter subsequently goes into more detail about how the increase in renewable energy will affect the electricity industry, then draws out some general implications of these changes for economic regulation, which will apply regardless of the specific model chosen. This is followed by a
Richard Green
discussion of lessons that are specific to the particular tasks facing regulators, which vary according to the regulatory model chosen. Although the electricity industry will see the biggest changes, the chapter ends with a look at possible impacts on the gas industry and lessons for its regulation.
scope of regulation, as for example the security of supply of imported energy is a foreign policy concern for many countries. Nonetheless, if the industrys capital stock suffers because the utilities lack resources, the countrys security of energy supply will quickly fall. An emphasis on security of supply may be an unusual way of framing a regulatory issue, but this is a key objective of energy policy for governments. The U.S. Department of Energy has five strategic themes, the first of which is energy security, defined as promoting Americas energy security through reliable, clean and affordable energy (DoE, 2006, 7). The European Unions 2006 Green Paper on Energy set three core objectives: sustainability, competitiveness, and security of supply. The United Kingdom has four goals for energy policy, including goals related to the environment (to put the UK on a path to cut our carbon dioxide emissions by some 60% by about 2050, with real progress by 2020), security of supply (to maintain reliable energy supplies), and the cost of energy (to ensure that every home is adequately and affordably heated) (DTI 2003).1 Energy policy thus commonly adds a third goalachieving a low environmental impactto the two that have traditionally concerned regulators: achieving low prices while ensuring sufficient investment to maintain security of supply. These three goals may often appear to conflict and are frequently portrayed as the corners of a triangle. Diversifying energy sources can increase security of supply, but if it involves raising the share of higher-cost resources, it will act against the objective of keeping prices down. Adding pollution control equipment will reduce environmental impacts and raise prices. In some countries, coal is seen as a more secure primary fuel than oil and gas, but the carbon dioxide (and local pollutant) emissions from burning coal, and hence its environmental impact, are higher. At other times, however, a new development, and particularly a new technology, may further all three goals at once. The introduction of combined cycle gas turbine generators in the United Kingdom in the 1990s raised security of supply by diversifying away from coal (which then dominated the indus-
trys fuel mix),2 reduced the environmental impact from sulfur emissions (and also from carbon dioxide, which was not then a priority for environmental policy), and was also cheaper than building coal-fired stations when new investment was required (Newbery 1999).3 Increasing the efficiency with which energy is used will tend to reduce the cost and environmental impact of a given level of energy servicesthat is, heat and light, rather than gas or electricity. This generally leads to lower overall levels of energy consumption, which will increase security of supply by cutting the amount of primary energy required.4
Models of RegulationThe way an industry is regulated has to reflect its structure. In the past, this did not in itself cause any variation in practice, because practically all of the energy utilities were either vertically integrated monopolies or part of a chain of monopolies. Several stages can be distinguished: electricity generation (or the production of gas) is the first, followed by high-voltage or high-pressure transmission over potentially long distances. Distribution at lower voltages or pressures takes the electricity or gas to the consumer and was traditionally defined to include the sales transaction as well. (Full energy liberalization, however, is based on the idea that retailing, called supply in the United Kingdom, can be separated from distribution.) Gas utilities might buy their product from oil companies, and some industrial self-generators would sell surplus power to the electricity grid, but the rest of the industry was monopolized. Regulators accordingly concerned themselves with the final prices charged to end consumers and the intermediate prices used when a regional or national utility sold gas or power to a smaller firm for local distribution. This started to change when competition was introduced to the energy utilities. Once it was realized that the natural monopoly at the heart of each industry covers only its network, then other companies could be allowed to use that network, whether they were producing energy or selling it to consumers. The first experiments were for lim-
ited competition in electricity generation in the United States, where the Public Utilities Regulatory Policies Act of 1978 required utilities to buy power from certain kinds of rival generators at their avoided cost of generation, and in Chile, where the 1982 Electricity Act led to the creation of several regional spot markets (Pollitt 2004).5 The privatization of the gas industry in Great Britain went further, as independent companies were meant to compete to sell gas to large consumers, and the privatization of electricity included a timetable that would culminate in every consumer in the country having a choice of electricity supplier (retailer). Many other countries followed suit, and the European Union (EU) adopted a proliberalization policy in 1996 and committed itself in 2003 to introducing full retail competition by 2007 at the latest. Despite this commitment, not all member states have achieved, or perhaps do not wish to achieve, the same level of competition in practice. Similarly, in the United States, 13 states and the District of Columbia have implemented retail competition, and others participate in regional transmission organizations that run organized wholesale markets, but nearly half do not (EIA 2009).6 This means that moves to increase the share of renewable energy need to be studied in the context of a range of regulatory systems. These systems differ in terms of the activities that the regulators have to carry out. Do they need to regulate the prices paid by consumers? Are separate prices incurred for using the network that need to be regulated, or are the costs of the networks simply bundled into the consumers prices? Are the costs of generation subject to regulation? Figure 8.1 shows four different models of regulation defined below for the purposes of discussion in this chapter: retail competition, wholesale competition, single buyer, and integrated firms. The first, retail competition, is the minimalist model of regulation, in which the regulators activities are largely confined to the natural monopoly of the network of pipes and wires and the system operation. For this model to work
Generation Retail competition Wholesale competition Single buyer Integrated firms Competitive Competitive Tendered
well, effective competition must exist both in electricity generation or gas production and in retailing. If retail competition is effective, then prices to end consumers do not need to be regulated, as retailers will be forced to set prices close to their costs of buying energy, paying to use the network and dealing with their customers. Similarly, prices in the wholesale market can generally be set by the normal processes of competition. The caveat is needed because of the special features of electricity, a nonstorable product that must be delivered through a transmission network subject to complicated and interacting physical limitations. The demand for electricity is very inelastic in the short term, not just because it cannot be stored, but also because most customers do not see the real-time price of power. These features make it possible for even companies with low market shares to exercise market power at particular times or in particular parts of the country, and thus a system of wholesale market monitoring is often needed to ensure that this is not abused. Even so, this market monitoring should be seen as a special form of competition policy, rather than of economic regulation. The regulators role under the retail competition model is to ensure that the prices for using the network are appropriate, and that all companies have equal access to it. If the network owner is still integrated into the other stages of the industry, this can be hard to ensure, as an unscrupulous operator can limit its competitors access to the network in many ways.7 The best solution to this problem is to separate the role of system operator from any commercial user of the network, either
by banning network owners from the other stages of the industry or by creating an independent system operator (ISO) to control the network assets owned by companies active in production, generation, or retailing. An ISO has the advantage that it can coordinate operations over a wider area than a single company, which will be useful if the historic pattern of asset ownership has created a patchwork of systems that would be inefficiently small if operated separately. At the same time, an ISO may not be as effective in pushing through a program of capital investment in the networks of independent firms as a single network company would be. It is worth noting that the problem of discrimination among network users has historically been much less important at the distribution level than in transmission. Energy sources (power stations or gas fields) are usually connected to the transmission system rather than to distribution. Even when a large power station was connected to the distribution system, it was usually the transmission operators that controlled its ability to operate. At the retail level, it was practically impossible for a distribution system operator to discriminate among customers of different retailers in terms of the quality of energy supply, as all the customers connected to the same segment of the network would receive their supply under the same physical conditions. The regulatory requirements for keeping distribution networks separate have therefore typically been weaker than with transmission, particularly for smaller utilities.8 The second model, wholesale competition, has effective competition in generation, based around an active wholesale market, but competition in retailing is limited or nonexistent, so final prices to consumers are regulated, as well as the charges for using the networks. This might be a transitional phase; if competition has recently been introduced and is becoming stronger, but most customers are still with the original incumbent retailers, then some protection can be appropriate.9 Some EU countries have so little competition to serve domestic customers that this transitional phase may appear permanent, even though the market is officially open. It is also possible as a permanent policy to restrict competition to the
wholesale market only, if policymakers are uncertain of the benefits of retail competition and do not wish to incur the transaction costs of implementing it. The wholesale market needs to have enough separate buyers to avoid a monopsony, but a market covering several regional or subregional retail monopolies will meet this condition. In the United States, Vermont and West Virginia participate in regional wholesale markets (ISO New England and PJM, respectively) without opening their retail markets to competition. A third model, which has been applied in a number of developing countries, is to have a single buyer. This is a regulated entity that is responsible for procuring electricity from generators, but through contracts rather than a wholesale market. The industry may be too small for adequate competition among generators, or policymakers may distrust the markets ability to deliver enough capacity, particularly in a system facing high demand growth. The use of long-term contracts is important, as electricity generation has very high sunk costs, and a producer selling to a single organization without the protection of a contract risks being held up for a lower price once its investment is complete. When the European Commission designed a single buyer model, it made it equivalent in its effects to limited retail competition, but most countries that actually use this model combine it with monopolies in retailing. This means that prices to end consumers must be regulated, as with the wholesale competition model. It is likely, however, that prices no longer need to be regulated for transmission and distribution, for these prices may not exist. Generators will have agreed to the terms for connecting to the network as part of their long-term contracts, and as this would be done before an investment decision is made, the contracts should provide sufficient protection to both parties. If the generators know that they will have to make payments to use the network, they will seek higher prices from the single buyer. Although there may not be formal prices for using the network, the regulator will still have to oversee its revenue requirements, ensuring that the amount recovered from consumers is reason-
able. Figure 8.1 thus describes this part of the industry as regulated without specifying that it has regulated prices. The cost of generation should be kept down by competitive tendering, and then passed on to consumers (just as adequate competition in a wholesale market should keep down the amount that regulated retailers could charge in the second model). The fourth and final model of regulation is one of integrated firms. This does not mean that a vertically integrated monopoly is responsible for every stage of gas or electricity supply. It may well be that vertical separation exists between the utilities responsible for energy production (or import) and transmission, and those responsible for distribution and retailing. The German electricity industry has a three-layer system of interconnected, regional, and municipal utilities, with the interconnected utilities owning almost all of the transmission system and much of the generation, but regional and municipal utilities delivering energy to about two-thirds of customers (Schulz 1995). The model is also compatible with a utility that buys some of its gas or power from third parties, but without the separation implied by a formal single buyer. Independent power producers that sell power as their main businesses would need the protection of long-term contracts, but industrial self-generators with occasional surpluses of power could sell these under short-term deals. In this model, generation is now regulated, as are the networks. The regulator must oversee final prices for all consumers, and there may be no intermediate prices to regulate, although if a large utility sells gas or electricity to a smaller one, the regulator may also need to oversee this transaction to ensure that excessive costs are not passed on. The main challenge for regulatorsunder any modelis to ensure that prices are high enough for the utility to recover efficiently incurred costs, but not so high that consumers are exploited. The regulator can generally measure what a utilitys costs actually are (assuming that competent accountants are available, and with a caveat concerning the cost of capital), but it is a different matter to measure what the costs could be. When just one firm is being regulated, the regulator can
only extrapolate from past trends, adjust for predicted changes in the future, commission efficiency studies from consultants, and perhaps make comparisons with firms in other jurisdictions, hoping that their environments are sufficiently similar to make the comparisons valid. When the regulator must deal with several firms, it is possible to make comparisons among them on the principle of yardstick regulation (Shleifer 1985). If the firms face sufficiently similar operating environments, then there should be no reason why all firms could not reach the level of the best performers, and their prices should be set to reflect this. Good performers could expect to receive more than the cost of capital, whereas firms with higher costs would not be able to do so until they cut those costs. In practice, some of the differences in operating environments will be significant, and regulators will have to adjust for these (Jamasb and Pollitt 2007; Pollitt 2005). The difficulty of doing so is why competition, where possible, generally gives better incentives to firms than does even the most enlightened regulation.
developments for environmental reasons, though opportunities still exist to develop small-scale schemes. Further increases in renewable energy therefore are likely to come largely from burning biomass (including waste) and from wind power. In some countries, solar photovoltaic power may be a sensible option, though not in northerly, frequently cloudy regions like the UK; other countries are surrounded by seas containing significant energy in their currents, tides, and waves. Devices to harvest this energy are being developed but are still mostly in the prototype stage.10 Most renewable generation will cost more than conventional alternatives, excluding externalities, unless the price of fossil fuels rises dramatically. In most cases, this is because of high capital costsseveral renewable technologies get their fuel for free. Biomass generation is the main exception for which fuel costs are a significant part of the whole. Large-scale hydroelectric generation was generally a low-cost energy source, although few sites remain available, and wind turbines at particularly good, windy locations onshore appeared quite competitive with fossil-fueled power stations at the high energy and carbon prices of early 2008. These high costs mean that companies will not be willing to build most types of renewable generators unless required or they are offered financial support. Chapter 6 and the various country studies discuss the mechanisms that can be used to achieve this. The EU target for renewable energy leads to a wide range of national targets, related to each countrys income and existing level of renewable energy, and the mix of renewable sources used will vary among countries. Table 8.1 gives a set of estimates prepared for the European Commission, giving a mix of sources predicted to minimize the cost of meeting the 20% target for renewable energy. The use of biomass and hydroelectricity will increase, but their percentage share of renewable generation would fall. Solar and marine energy would increase from very low levels at present. By far the greatest increase would come from wind generation, which might provide more than onesixth of Europes electricity in 2020.11
TWh Biomass Geothermal Hydro Solar Marine Wind Total 90 6 344 2 1 82 525
This level of wind generation will lead to significant challenges in dealing with intermittency. Large-scale wind generators reach full output only at wind speeds between around 12 and 25 meters per second (m/s), shutting down at higher speeds and losing power rapidly when the wind is low.12 A well-sited turbine might have an average output equal to 30% of its rated capacity over the year, but the actual output will vary sharply over time, and meaningful predictions are impossible more than a few days in advance. In regions where clouds are rare, the main factor affecting the output from solar power will be the earths predictable rotation (although in regions with variable cloud cover, this will have a significant effect on solar output), and tidal energy can be predicted years in advance but will sometimes be highest at off-peak times. Biomass generators can run when the user chooses, but if these users are households or small businesses with combined heat and power units, they may choose a heat-led operating pattern that does not necessarily produce power at the times when it would be most useful for the electricity industry (Hawkes and Leach 2007). Intermittent outputs from renewable generators cause two main problems. First, the system operator will need to keep more plants in reserve for times when the level of renewable output suddenly falls. This particularly applies to wind out-
put, as most other renewables are more predictable over the relevant timescales (of a few hours).13 Gross et al. (2006) have estimated that these reserves would cost 2 to 3 ($3.05 to $4.58) per megawatt-hour (MWh) of intermittent renewable energy if the United Kingdom were to get 20% of its electricity from wind. Note that the system operators always have to keep some plants in reserve in order to respond instantly if a power station fails, but it has traditionally been sufficient to set this reserve equal to the size of the largest unit running on the systemthe worst case for a single failure. If the wind drops over a large area, many separate wind turbines, with a greater collective capacity, could be affected. The level of reserve generation (or demand reduction potential) needed to cover this risk naturally grows with the amount of wind generation, and the system reserve requirement is the greater of that required by wind stations and by conventional plants. The rules of the systemeither those of the wholesale market or the regulations of the system operator, depending on where the responsibility for providing these reserves lieswill have to ensure that these can be paid for. The second problem is that the wind or tide may not be available at the time of peak electricity demand, which is what determines the total amount of capacity needed on the system. Not all of the capacity of a conventional plant can be counted against this need, as it may be unavailable at the critical time, but it is generally sensible to assume that more than 80% will be. The risk that many wind generators will be becalmed at the same time is greater, however, and the effective contribution of a portfolio of wind power stations to ensuring ability to meet peak demand has been estimated at only 20% (or less) of their total capacity.14 This means that 10 gigawatts (GW) of additional wind power could replace only 1 to 2 GW of conventional stations, in terms of planning to meet the peak demand. The energy produced by the wind stations could actually replace the output of around 3.5 GW of conventional plants, if the time of delivery were irrelevant.15 In other words, between 1.5 and 2.5 GW of conventional plants would be needed per 10 GW of wind, because we cannot know whether wind stations
will produce when we most want them to. Gross et al. (2006) predict that this would cost 3 to 5 ($4.58 to $7.63) per MWh of wind energy in UK conditions. The wider the geographic dispersion of the wind stations, the lower this penalty will beas long as this dispersion reduces the correlation among their outputs and enough transmission is available. Note that both of these problems are far less severe in a system with, or connected to, large amounts of hydro generation with reservoir storage. The output from hydro generators can respond inversely to the output from wind, storing energy in the form of water until it is needed. The cost of short-term reserve to cover variations in wind output is very low, as it costs almost nothing to keep a hydro station ready to increase output at short notice. At most hydro stations, the reservoir is not large enough to keep the generator running throughout the year, so the system can provide enough energy over the year as a whole only if it has more than enough capacity to meet the peak demand. In this context, the risk that a wind generator will not be available at the peak time is less important. In a system with little hydro generation, the inevitable consequence of increasing the variable output from wind generators is that the output from conventional generators will also tend to be more volatile, except to the extent that demandside response takes up the slack. Sometimes the output from wind stations will rise at the same time as demand, reducing the increase required from conventional stations; at other times, demand and renewable outputs will move in opposite directions, increasing the strain on the system. There will also be some stations that are needed for the times when high demand levels coincide with low wind speeds but do not run very often. It will be important to ensure that these generators can cover their costs. Demand response can reduce the need for peak capacity, cutting the industrys costs (Borenstein 2005). Some response can be made without consumers noticing, such as delaying the start of water heaters or refrigerator motors when prices are particularly high, or responding to some other signal such as system frequency. Some
industrial customers have long been used to scheduling energy-intensive processes away from the times when power is most expensive, although this rescheduling is likely to involve some costs. The likely growth of electric vehicles offers a good opportunity to increase the unnoticed element of demand response, simply by ensuring that the vehicles mainly charge themselves during low-demand periods overnight, although it will be important to ensure that the vehicle is sufficiently charged by the time it is needed. Electric vehicles have a significant advantage over most other forms of energy storage, in that the battery is needed in any case, and this kind of use by the electricity industry is essentially a free good. Car batteries could also be discharged when the industry is particularly short of power, but imposing additional cycles of deep discharging and recharging would shorten the batterys life. Given the cost of batteries, this would be an expensive source of electricity. Moving on to network issues, in many countries significant investments will be needed to accommodate an increase in renewable generation (Pollitt and Bialek 2008). Many of the best sites for renewable generators are remote from the centers of demand, and transmission lines will be needed to move the electricity to where it is needed. Most renewable generators are on a much smaller scale than conventional centralized power stations, so many separate schemes, each with its own grid connection, may be needed to replace a single large conventional station. Because the renewable schemes are generally smaller, their connections will often be to the distribution networks rather than the transmission system. This will certainly be the case where self-generation or community-owned small-scale generation is concerned. Connecting large amounts of generation to the distribution networks will require significant changes in their operating practices. Broadly speaking, transmission networks have always been operated to accept two-way flows across every line in the system, but distribution systems have generally been designed to take power in one direction onlyfrom the connection with the transmission system to the customer.16 If power
always flows in one direction, the design of the system is simpler, and protection equipment against power surges is needed on only one side of the substation it is intended to protect. If power could suddenly flow from a generator located on the customer side of the substation, then extra protection equipment is needed, and it will be necessary to manage the flows on the distribution network in a much more active manner. This requires additional equipment to monitor conditions in real time and operating practices that respond to them. Local generators and loads may have to change behavior in order to keep power flows within acceptable limits. Greater monitoring, however, can also change those limits. For example, windy conditions allow the heat from power flowing through the network to dissipate more easily, increasing the safe operating limit at the very times when the output from nearby wind farms will be at its greatest (Douglas 2005). Finally, it is worth mentioning another change that is not directly connected to the growth in renewable energy but is clearly linked to the move toward a low-carbon energy system. This is the adoption of carbon trading, first in the European Union, and now in parts of the United States.17 The need to buy permits raises the marginal cost of generation from fossil fuels, even if the generator receives those permits for free.18 This will tend to raise the wholesale price of power produced by a given capital stock; in a fully competitive market, the price would rise by the price of emissions multiplied by the carbon content of the marginal plant. In the longer term, the level and composition of the industrys capital stock may change because of carbon trading. First, if generators receive some permits free of charge, contingent on the existence of a power plant, then the value of those permits can be offset against the cost of keeping the station open. This may allow the station to remain open when its revenues from the electricity market alone would not justify this. This could lead to a market equilibrium with a greater amount of capacity, which would tend to depress the price of power, partly offsetting the impact of the carbon price (Green 2005).
A more significant change to the capital stock should be that compared with high-carbon power stations, low-carbon generators become relatively more attractive. Natural gas will gain relative to coal, and there is already talk of nuclear power enjoying a renaissance in the United States and United Kingdom. Many countries are promoting demonstration schemes for carbon capture and sequestration and, once the technology is proven, could compel generators to fit this to new plants and perhaps even retrofit it to existing ones. If the carbon price rose to a sufficiently high level and was expected to remain there, carbon capture would be economic without government compulsion. The price within the ETS, however, has never reached this level, let alone stayed there long enough to make such investments attractive. This also applies to renewable generators. If they receive a wholesale price that has been increased by the cost of carbon emissions, this will reduce their need for additional support. Likely levels of carbon prices, however, are unlikely to make most renewable generators competitive with gas-fired or nuclear generators, at least given the costs expected in the United Kingdom (DECC 2009a).
oil price of $48 per barrel; if the oil price rises to $78 per barrel, energy costs will be much higher, but the additional cost of renewable energy will fall to at most 11 billion ($15 billion) and would be zero in one scenario. Some of these costs may be borne by taxpayers, but the remainder will have to come from energy consumers, and regulators will need to allow the utilities to collect theseas with any other efficiently incurred cost increase. Where parts of the industry have been liberalized, the regulators will not determine prices directly, but regulators that have oversight of these competitive areas must not intervene to suppress necessary increases. Second, the electricity industry will need a lot of capital investment, both in renewable generation (many technologies are capital-intensive) and in changes to the network to accommodate it. This will affect the weight that regulators should place on the different cost components when setting incentives for network companies and for generators, if they are regulated. A regulated firms costs can be broken down into its cash operating costs (labor, materials, and bought-in services), the return on its new investments, and the return on its existing assets. The firm is assumed able to control both its operating costs and its investment in new assets, but its existing capital stock is, naturally, fixed. The required return on both new and existing assets depends on the cost of capital, which is affected by the perceived risk of the business. High-powered incentives to reduce costs will increase the companys risk, as success or failure to do so will feed through to the companys profits. Standard models of financial economies, such as the capital asset pricing model, suggest that diversifiable risks should not affect a companys cost of capital. Nonetheless, the level of incentives might have an impact through two routes. First, if the companys profits become more sensitive to the overall state of the economy, and are positively correlated with it, then its beta will rise, increasing the cost of its equity. Second, if favorable tax treatment reduces the cost of debt and a company with risky cash flows has to reduce its gearing, this will increase its weighted average cost of capital.
An optimal regulatory scheme would balance the expected savings from strengthening the incentive on the volume of operating costs, or of investment, against the cost of having to pay a higher expected rate of return. In other words, although it is possible to give the firm very highpowered incentives to reduce both its operating costs and its investment (such as a pure price cap regime does), this would be undesirable if it caused too great an increase in the cost of capital. If the industrys cost structure shifts away from operating costs, then optimal regulation would imply paying more attention to the rate of return on capital and less to the level of operating costs. An increase in the volume of investment will mean that more weight should be placed on incentives for efficient investment than on the impact of a higher cost of capital on payments for existing assets. In other words, incentives for efficient operation might be relaxed, and those for efficient investment should be strengthened. One way of doing this in practice is to offer the firm a menu of regulatory contracts (Laffont and Tirole 1993; Sappington 1982). In the United Kingdom, the regulator has given distribution companies such menus for their investments over the next price control period (Pollitt and Bialek 2008). The regulator uses consultants to assess the necessary level of investment, but the firm can propose a higher base figure. The firms allowed revenues are then linked to this proposal and its outturn expenditure. Whatever the outturn level of expenditure, the firms revenues are highest if this was the level that it actually proposed, providing an incentive for truthful cost revelation. The allowed revenues increase by less than a unit for each unit increase in the firms actual expenditure, however, so the firm has to bear a share of any cost overrun and keeps a share of any savings. This provides an incentive to keep actual costs down, and one that is greater the tougher the target that the firm accepts. Accepting a tough target thus allows the firm the chance to benefit from highpowered incentives. A firm that accepts and meets the regulators target will be allowed revenues of more than the amount it spends, which gives a margin of error in case the regulators target is too tough. Under Ofgems scheme, which differs
from some versions of the menu of contracts approach, the allowed revenues also increase by less than a pound for each pound that the firm proposes to spend above the regulators target. The greater the firms proposal, the less revenue the firm will be allowed for any outturn spending below this proposal.19 Overall, the scheme gives the firm an incentive to reveal the truth about its expected capital needs and to keep its costs down, while allowing it to pass part of any genuine overrun on to consumers. A final general implication is that the number of small electricity generators that wish to inject energy into the network will increase. Any energy producer connected to the network must be subject to the network operators technical standards and the industrys commercial arrangements purely bilateral transactions are impossible in an interconnected system. Energy regulators are often involved in overseeing the details of such standards and arrangements to ensure that these do not discriminate against particular network users. If we wish to further the development of microgeneration (and the high costs of many microgenerators should be taken into account in that decision), then the regulators will have to ensure that the technical standards are not too onerous and the commercial arrangements are suitable for nonspecialists. Accommodating these generators will be helped by a variety of so-called smart grid technologies. Traditionally, system operators have had little real-time information on the actual state of the distribution network, which forces them to run it in a relatively conservative manner. More information can be used to increase the allowable power flows. Furthermore, some small-scale generators and loads can be dispatched, responding to instructions based on the state of the system both the overall balance between generation and demand and conditions on the local distribution network. Regulators will have to ensure that companies are rewarded for innovations such as these, and that the boundaries among generation, retailing, and networks do not act as barriers. This is potentially a disadvantage of the retail competition model, which sometimes severs the relationship between a customer and its network operator
(although in some countries, the customer continues to receive a bill for network services). The need to give incentives for innovation has emerged as a key theme of the British regulator Ofgems 20082010 review of network regulation, known as email@example.com Spending on research and development by the distribution network operators in Great Britain has risen dramatically since a separate revenue stream to fund it was created in 2005 (Ofgem 2009). One key issue is that spending may have to be incurred well before customers see any benefit from it, and regulators must allow companies to fund this.
the average level of prices is linked to the industrys expected costs, and a key aspect of these is the level of investment. Investment in the electricity networks will need to rise, increasing costs and hence prices. Strong incentives to minimize investment costs might keep prices down but could jeopardize investment in renewable energy if insufficient grid capacity is available. For an extreme example, new generators inquiring about connecting to the transmission system in Scotland were told that they might have to wait more than 10 years for capacity to become available (House of Lords 2008a, para. 133). The primary cause of such delays is the long lead time for transmission investment, typically greater than that for generation. In part, this time depends on the process of obtaining planning permission for new transmission lines, which has become a lengthy one in many countries. Sometimes it will not be possible to develop a line in time to meet an unanticipated need for capacity, and the regulator (and would-be generators) will have to accept thisalthough reforms to speed the planning process would make this less common. The impact of this lead time is worsened because it will not always be clear in advance exactly where capacity will be needed, or how much, even if the general requirement is clear. If the network owner is worried that it will be penalized for spending that turns out to be unnecessary, it will not wish to start the detailed investment process until the needs are clear and, preferably, contracts are signed with users. This is likely to impose significant delays, which would be unnecessary in cases where it had earlier become clear that some investment was likely to be required. Regulators should allow companies to recover the cost of making plans, and even seeking planning permission, for new lines that appear likely to be needed but are later abandoned as circumstances change. To reduce the risk that too many unwanted lines are planned, wider consultation should be done on network investment plans. Pollitt (2008) calls for a process of constructive engagement already used abroad and in the regulation of UK airports, where users and the airport owner could jointly agree on a pro-
gram of capital investment. Littlechild (2008) describes the negotiations used to decide on transmission investments in Chile, where transmission users were able to insist on a cheaper alternative to the line originally proposed by the transmission utility. A balance is needed between making a timely start to investment and not authorizing projects with insufficient demand. The California Independent System Operators Location Constrained Resource Interconnection Policy requires financial commitments from renewable generators to use at least 60% of a new lines capacity, but then incorporates the lines costs in its general transmission tariff.22 A more prescriptive approach would imply the creation (or empowerment) of some kind of national planning agency to coordinate generation and network investment and act as a guiding mind, as Ofgem puts it in its RPI-X@20 review. It is also important to ensure that the investment needs in the grid are not increased by inflexible procedures. Traditionally, transmission companies have been reluctant to connect generators unless they are certain they can almost always accept their output. This is a sensible policy for conventional generators, as, if the investment is desirable, they can expect to be needed to meet peak demand, and preventing them from generating could risk power cuts. Wind generators, in contrast, may not generate at the time of the peak; if they do, some other stations, which need to be available, will not be running. The transmission system does not need to accept every generators output simultaneously, and investment rules should be altered accordingly. It is important, however, to allocate access to the grid efficientlysomething achieved automatically in a wholesale market based on locational marginal prices. The second aspect of price regulation is the distribution of payments among network users. In particular, how should they vary over time and space? A partial answer is that if the wholesale market adopts nodal marginal pricing, then costreflective variations will occur over time and space, and these will be passed on to network users. The prices at nodes with net generation are, on average, below those with net demand,
because the costs of transmission are incurred as power flows from generation to demand nodes. The transmission operator can therefore buy low and sell high, retaining the surplus. The problem is that other charges will have to be added, for these net revenues from nodal marginal prices generally recover just a small part of the total cost of transmissionoften only around 20% (PrezArriaga et al. 1995). It is possible to derive an optimality condition for transmission investment, under which the marginal cost of expanding transmission capacity should just equal the marginal benefit of doing so, which is given by the difference in prices at each end of the line. With constant returns to scale and perfectly divisible capital, this suggests that nodal pricing would completely recover the costs of an optimal system. Perez-Arriaga et al. suggest that this result does not hold because of economies of scale and indivisibilities, because it is often sensible to build a line that is currently too large, in order to accommodate future growth, and because changing patterns of generation and demand mean that the inherited transmission system will inevitably be suboptimal. This suggests that the missing revenue should not be seen as related to identifiable costs of transmission, but as a lump sum that has to be recovered with the minimum of distortion. Given this, how should the remaining network costs be recovered across time? Should generators (and customers) pay whenever they are sending power to (or taking power from) the grid? Should they pay based on the maximum cost that they could impose on the grid or based on the cost they impose when the load on the grid is heaviest? Given that capital costs form a high proportion of the total, this would argue for some form of peak load pricing, rather than recovering costs evenly over time. Peak load pricing is based on charging users at the time when the load on the grid is heaviest, as capacity built to cope with these times is effectively free at times with lower loads. The issue is that with this kind of peak load pricing, renewable generators would tend to face much lower transmission payments per kW of capacity than would conventional generators, but
those payments would be variable over time. In some years, a renewable generator would be able to generate at the time of the system peak and would be assessed for transmission charges on the basis of this. In other years, the system peak would come when the local wind was low, and the generator would have little output and would pay little for transmission. This is unlikely to be politically acceptable and could also produce perverse incentives not to generate at the system peak transmission charges per MW can be an order of magnitude above all but the very highest electricity prices, and if just one or two hours of generation creates a liability to those charges, it will not be worthwhile.23 This suggests that transmission charges should be based on either the capacity of a power station or the energy it generates over the year, perhaps weighted according to the time of production. If the charges are capacity-related, intermittent renewable generators will pay much more per MWh actually generated than conventional stations able to run at base load. Conventional generators that are needed to meet peak demands, but rarely run at other times, will be in a similar situation. Both may claim this to be discrimination. Furthermore, they will need some revenue source to cover these costs if they are to remain available in a long-term equilibrium. For peaking generators, this is likely to be reflected either in the prices for peak power or in a capacity market, if one exists. For renewable generators, less able to rely on peak prices or capacity markets, the remainder will have to come from their support scheme. Charging intermittent renewable generators on the basis of their capacity, but adjusted for their relatively low expected output, might be a politically acceptable way of reducing the cost of thiseffectively, using a hybrid scheme. If transmission charges are energy-related, the impact on peak prices and the cost of supporting renewables will be lower, though electricity prices will be slightly higher throughout the year as generators pass through this additional marginal cost. Furthermore, wholesale prices may not accurately reflect the marginal cost of expanding transmission to accommodate more generation (even with nodal marginal prices, indivisibilities might mean
that the system has to be expanded to the point where the price differences at each end of the line are quite low), or wholesale prices may not include any transmission component. In this case, the transmission charges should signal the cost of expanding the system, and capacity-related charges will send a more accurate signal than energy-based ones. Similar considerations apply to the decision about how network charges should vary over space. The overall system of transmission charges (including transmission-related elements in any wholesale market) should signal the costs of using the transmission system and ensure that the network companies recover their efficiently incurred costs (Green 1997). If other parts of the pricing system send adequate locational signals, so that network charges are purely to recover a lump sum of missing revenue, then a uniform national or systemwide charge is appropriate. If generators and customers do not otherwise face charges that reflect the spatial variation in transmission costs, however, then transmission charges should vary over space. The implications of this may depend on factors specific to each market. In Great Britain, for example, transmission charges are highest in Scotland (which is bad news for wind farms sited there) and lowest in southwest England (which will be good news for marine energy schemes located off the coast of that region). In general, however, it seems likely that renewable generators located in rural areas will face higher transmission charges than conventional generators closer to the loads. Once again, this will affect the cost of supporting renewable energy. The higher the transmission charges paid by the marginal generator (the one whose needs just determine the level of support required), the greater the support it will need. Unless the support scheme separates out and pays each generators individual transmission charge, this will raise the rents obtained by generators in more favorable locations. Transmission charges may not, in fact, have much impact on the locational choices made by renewable generators, which need to choose sites that maximize their output. A 1% difference in the load factor achieved by a British wind farm,
for example, would largely offset the difference in transmission charges between the north and south of Scotland or central and southwest England.24 In this case, minimizing the rents obtained by well-sited generators might be more important than sending precise signals. However, the impact on conventional generators must also be taken into account. These have more flexibility to choose their sites and should be sent signals of transmission costseither through spatially varying wholesale prices or through transmission charges. The final issue involved in regulating the wires is how to regulate the transmission system operator, as distinct from the transmission owner. The transmission owner receives payments for having built the transmission wires and keeping them in good repair; the transmission operator is responsible for managing their daily use to move power. Sometimes both functions are combined in a single company, but an independent system operator can be used when it is most effective to run a single market over several transmission owners grids or when regulators want to ensure that the grid is operated independently of generation interests, without ordering an integrated company to divest any assets. The system operator will need to buy operating reserves and, in markets without nodal pricing, will resolve congestion by buying and selling power on either side of a constraint. As the amount of renewable energy increases, so will these costs. This implies that the benefits from giving the system operator strong incentives to control them are likely to rise. It may be difficult to give strong financial incentives, relative to the costs involved, to an independent system operator, for organizations like these have few assets to cushion them against unforeseen events that would lead to a bad outturn, and hence performance penalties, even in a single year. If significant transmission assets are owned by an independent company that is also the system operator, however, then the revenue stream for these assets provides a suitable cushion against which to strengthen the system operators incentives.25 The increase in renewable energy therefore provides an additional argument for establishing independ-
industries has been to allow utilities to recover the cost of such stranded assets, in the interest of longterm investment incentives. This would also be appropriate in this case but will increase the apparent cost of renewable energy. With a liberalized wholesale market, the cost of stranded assets is absorbed by their owners, but the risk that something like this would happen should have been taken into account when those owners calculated their cost of capital, which would normally be higher than in a regulated system. The cost of generation will also be affected by the cost of carbon in countries with a system of carbon trading. If generators have to buy all their permits, then this will be a cost of business that the regulator should include in their allowable revenues. If generators receive some permits for free, however, then the regulated firms revenue requirements should take this into account, muting the increase in price. Politically, this may be an advantage, but it implies that the impact of carbon pricing will have to come entirely from the supply side, rather than from both supply- and demandside responses. It is worth pointing out that where generation is traded in a wholesale market, regulators are often responsible for market monitoring, even though this is really an aspect of competition policy rather than of economic regulation. Market monitors need to know when to intervene to stop the abuse of market power, which typically manifests itself as high prices. The challenge in electricity markets is that some periods of high prices may be essential if generators are to recover their fixed costs,26 and market monitors must not intervene to suppress these necessary peaks. A system with a high proportion of intermittent renewable generators may find that the residual load on thermal generators becomes much more uneven, with periods of zero (or negative) marginal costs. If the market is so competitive that prices can rise above marginal cost and pay for capacity only in the few hours when demand is high enough to create scarcity rents, prices at these times will have to be extremely high. An oligopoly with moderate price-cost margins for a larger number of hours might produce a more stable outcome. In either case, the need to differ-
entiate between high and excessive prices, and perhaps respond to political pressure to keep prices down, will make the task of the market monitor more demanding.
geting should allow plenty of room for entry in generation to relax this constraint. The other standard mechanism is to pay renewable generators a feed-in tariff that offers them a premium price for their power. Some organization has to make these payments and recoup the cost of doing so. A distribution company might appear to be well placed to do this. First, most renewable generators will be connected to its system, which gives it the knowledge of which generators are eligible for support and what they produce. Second, it already bills either consumers or their retailers for distribution services in a nondiscriminatory manner and can add the cost of the feed-in tariff to these bills. In a system with more than one distribution utility, however, there will almost certainly be a mismatch between each utilitys share of renewable energy sources (and hence payments) and energy consumption (and hence receipts). On the basis that the cost of supporting renewable energy should be shared over a wider area, side payments among distributors would be needed. This means that the scheme would have to be overseen by a government agency or the regulators office to ensure that the correct payments were made. It might be best for this agency to actually calculate those payments, using information provided by the distribution companies. These points also apply in the retail competition model, because that depends on retailers facing similar obligations. They would not apply if no retail competition existed, because then no distortion could arise. In the absence of retail competition, the regulator has more ability to influence which customers actually have to pay for renewable energy. If regulators have a particular duty to protect selected groups of customers, this raises the issue of whether the price rises should be skewed toward others. The problem with this approach is that it can be difficult to ensure that such help is actually given to those who need it most. Keeping down tariffs for those who use very little electricity may benefit the owners of second homes, for example. Many fuel-poor households have relatively high energy consumption, often because their homes are poorly insulated.28
The distribution of price increases among customer classes is also relevant here: commercial and (particularly) industrial customers facing international competition might claim that they should not be exposed to unnecessary price rises, suggesting that domestic customers should face a high proportion of the support costs. For most industrial and commercial customers, energy costs are not significant; Yago et al. (2008) found that a 30% increase in the price of electricity would raise costs by less than 1% in sectors making up 90% of the UK economy. The remaining sectors will be more affected, however, and some of those are exposed to international competition particularly aluminum smelting. Should regulators try to design tariffs that will shield such energy-intensive industries from price increases? The general thrust of liberalization policy has been to reduce such political interference in markets, as benefits often go to the best lobbyists rather than the most deserving causes, and align prices to costs.
When it comes to the growth of renewable energy, however, this could lead to a reduction in end users demand for natural gas. The use of renewable heat sourcesbiomass and heat pumps29could double as part of the EUs drive to obtain 20% of its energy from renewable sources, displacing natural gas and other fossil fuels (European Commission 2007). In the long term, the need to reduce CO2 emissions by 80% or more is likely to be incompatible with burning natural gas without carbon capture, which will probably be impractical for small-scale heating systems. Gas industry regulators may thus face the challenging task of managing both growth and decline. If the demand for gas for electricity generation continues to grow, then the issues involved will be similar to those facing electricity networks where more investment is needed. However, the networks involved will mainly be the highpressure pipelines, delivering large amounts of gas to large generators. If renewable heat and renewable sources of electricity reduce the demand for gas by end users, then the utilities will have to face this, even though the capital (and some operating) costs of their distribution networks will not be declining. This would imply a rising per-unit price, just as the price of natural gas may also be increasing. The process would not be sudden, and a switch to biogas might boost demand, but the potential exists for a vicious circle of rising prices and falling demand. In extremis, regulators or governments might need to consider giving gas utilities additional support to ensure that they can still manage their distribution networks safely while reducing sales.
ConclusionsThis chapter has suggested specific ways regulators will need to respond if the energy utilities are to absorb large amounts of renewable energy. What about the broader questions posed at the outset? Should regulators objectives change, or the factors they take into account when deciding how to achieve those objectives? Regulators doubtless will have to consider their impact on the environ-
ment when making decisions. If they did not, they would find it hard to justify the investments needed to accommodate renewable electricity generation, for example. Should protecting the environment be seen as an objective to place alongside consumer and producer protection or a constraint on how the regulator goes about its other duties? A mathematical problem can be set up to give the same results whether environmental protection is included as something to maximize or a constraint that must be met, if the constraint is set at the right level. In the real world, however, regulators would probably have to act in different ways if they were to give environmental protection the same priority as their other duties. We do not want them to damage the environment, but their relative expertise is not in this area, and it would be preferable if economic regulators were not designing environmental policy. It would seem best for regulation to continue to prioritize consumer protection and ensuring that firms can finance their activities, but to do so in light of government environmental policies, and promoting their achievement.30 Should the scope of regulation be broadened or narrowed? Regulators will need to make different decisions on specific issues, but there will be no step changes in the kinds of questions they must address. The bulk of the evidence so far is that well-designed liberalization programs offer superior performance to the traditional utility model (Joskow 2008). Would the growth of renewable energy overturn this result? Integrated firms can coordinate the development of the network to accommodate renewable generation. The single buyer model allows the policymaker to choose a mix of lowcarbon generators in a coordinated manner to meet its targets. Wholesale market monitoring to guard against the exploitation of market power will be more challenging in an industry with a high proportion of intermittent generation with very low marginal costs. Retail competition may act as a barrier to the development of smart grid technologies that coordinate devices for consumers and on the network. Competitive models that raise the cost of capital will be more costly for an industry that is becoming more capital-intensive.
These are significant arguments, all suggesting that liberalization will be less successful at dealing with the challenges of renewable energy. They are not likely conclusive, however. The great advantage of competition is that it can allow new entrants to spot opportunities that incumbents have not exploited. If we want many consumers to generate part of their own electricity, then a model with free entry for competing retailers that could provide and maintain generating equipment on a customers premises could further this. Even though the retail competition model often severs the direct link between the customer and distribution network operator, it should be possible to find a business model through which retailers or others that install a smart grid application on a customers premises can share in the resulting benefits. Regulators may have to help the industry develop the essential information protocols and compatibility standards to make this possible, but competitive environments are generally more innovative than monopolies. As the level of renewable generation increases, less of the market may be truly competitive. Governments may reserve part of the market for renewable generators, either implicitly or explicitly. Other support schemes may be needed to persuade companies to invest in carbon capture and storage or nuclear power. The danger would be that if the market for unsupported generation became a small rump, competition within it would no longer act as sufficient discipline on the participating firms, and the prices set there would lose their ability to send meaningful signals. Helm (2008) has suggested that a utility should be created to tender for these stations, financing them in a way that minimizes the cost of capital. This is effectively a variant of the single buyer model. If sufficient competition exists among developers to win the tenders and build the stations, it might achieve the best of both worlds. The danger, however, is that the single buyer would end up favoring particular technologies that turned out to be unsuccessful, and that there would be no checks and balances to prevent this. It would be better to establish a long-term carbon price and use this to provide incentives for investment in low-carbon generation on the level
playing field of a competitive wholesale market. Similarly, efforts to provide a guiding mind to coordinate investment in generation and the networks should be based on constructive engagement among the relevant parties, sharing information on their plans and needs, rather than on prescriptive central planning. At the same time, competitive models do involve greater risks, and thus a higher cost of capital, than the alternatives. In a more capitalintensive industry, the cost of capital will make a greater contribution to consumer prices, but the importance of choosing an efficient amount of capital will also riseparticularly during a period of high investment, such as most countries are now facing. Again, competitive mechanisms generally lead to better investment decisions than regulatory ones; even when investors have indulged in excessive plant building, this has not been at the cost of electricity consumers. Therefore, although the detailed decisions economic regulators need to make will change, and they will have to take more account of the environment as a constraint on what they expect companies to do, the shift to renewable energy will not change the fundamental tasks or nature of economic regulation. We may need to adapt our existing tools but can continue to use them to build a low-carbon energy system.
AcknowledgmentsThanks go to the editors, Chris Hemsley and Andrew Quinn, and participants at the Centre for Competition and Regulation Policy Workshop held at Aston Business School in July 2009, for helpful comments. The author also thanks Boaz Moselle for directing him to the relevant part of the California ISOs website.7.
Notes1. The fourth goal, to promote competitive markets in the UK and beyond, helping to raise the rate of sustainable economic growth and to improve our
productivity, is also related to the price of energy, although one might argue that a competitive market is a means to an end rather than an end in itself. Although an indigenous source of supply would normally be seen as secure, miners strikes caused major power cuts in the 1970s and threatened them in 19841985, thus diversification away from coal reduced the risk of a recurrence. In practice, much of the new investment was made before additional capacity was actually needed, because market power raised prices and made entry attractive, and it also may have raised the industrys costs in the short term. The demand for energy services is likely to increase because of their lower effective price (the so-called direct rebound effectas comfort becomes cheaper, people like to be more comfortable) and because the money saved in one area typically will be spent on other goods and services that also consume energy (an indirect effect). Sorrell (2007) reviews the evidence for this and concludes that in developed countries, the direct rebound effect is between 10% and 30% of the energy saving per unit of energy services. The evidence on indirect effects is inconclusive, but they may be quite large (more than 50% of the initially predicted energy saving). The prices in these spot markets, and for longerterm sales, were calculated based on estimated marginal costs, rather than freely submitted price offers. The map of regional transmission organizations, which do not correspond neatly with state boundaries, shows 20 states with little or no coverage. Even in those states, however, some bilateral trading may occur. For example, the network operator might deny access on spurious grounds of system security (i.e., claiming that the network would be vulnerable to failure if power flows from the entrants area increased), fail to invest in new infrastructure that would facilitate access, or institute a margin squeeze through setting excessive prices for ancillary services. The EUs second electricity and gas directives, for instance, allow member states to choose whether to exempt utilities serving fewer than 100,000 customers from the requirements for legal separation between distribution and retailing that are otherwise compulsory (European Commission 2003a, b).
In Great Britain, the incumbent retailers faced price controls when selling to domestic customers until 2002, even though all customers had a choice of retailers by mid-1999. In contrast, price controls for medium-size commercial and industrial electricity customers were lifted as soon as this market was opened to competition in 1994, in the belief that these buyers were more sophisticated and could protect themselves by shopping around. An exception is the tidal barrage at La Rance, France, which is effectively a hydroelectric schemea very mature technologybuilt across an estuary rather than an inland river. A similar scheme across the Severn Estuary in the United Kingdom could generate 5% of the countrys electricity (DECC 2009b) at the cost of significant changes to the local ecosystem and 21 billion ($32 billion). While wind energy would be 41% of renewable generation in this scenario, 43% of electricity generation would be renewable, giving an overall penetration of more than 17%. The range can vary among turbine designs. For example, GEs 3.6-megawatt (MW) offshore wind turbine does not need to cut out until the wind reaches 27 m/s but needs a speed of 14 m/s to reach full output, whereas its 1.5 MW 1.5xle onshore design can reach full power at a speed of 11.5 m/s but cuts out after 10 minutes averaging 20 m/s. Individual photovoltaic units could have volatile outputs on a partly cloudy day, producing much less when a cloud moves overhead. Gross et al. (2006) give a range from 19.1% to 26% for Great Britain, if it were to get 20% of its energy from windsomewhat less than is now planned. The UK government uses a range of 10% to 20% for the larger quantities expected (House of Lords 2008b, 484), and E.ON UK, an integrated power and gas company, has suggested that the figure should be less than 10% (ibid., 107). This assumes that the wind generators have a load factor of 28% and the conventional generators one of 80%, which is rather low but produces round numbers in the example: both sets of stations would produce an average output of 2.8 GW. Some power stations have been connected at distribution voltages, particularly at the higher voltages close to the transmission system, but on a limited scale compared with the likely growth of distribution-connected renewable generation.
17. Federal legislation for a nationwide scheme is being debated at the time of writing (November 2009). 18. A generator with a surplus of permits, albeit free, still faces the opportunity cost of not selling the spare ones. 19. If the firm spends more than it proposed, however, it would have been better off had it proposed a higher level of spending. 20. Because Great Britain follows the model of retail competition, formal regulation covers only the transmission and distribution networks, and this review does not deal with issues related to generation or retailing, even though many network innovations will also affect these sectors. For documents related to the review, see www.ofgem.gov.uk/Networks/rpix20/Pages/ RPIX20.aspx. 21. This is because many renewable generators will be located far from demand, and their low load factors mean that it would not be economic to build enough transmission capacity to accommodate all their output at the rare times when they are producing at close to full output, leading to an increase in transmission constraints. 22. For more information, see the California ISOs website at www.caiso.com/1816/ 1816d22953ec0.html. 23. If this behavior allowed a generator to avoid building the extra transmission, and the generator could still meet the peak demand, it would be efficient. 24. These transmission charges vary by around 7.00 ($10.68) per kW per year, whereas a forward wholesale price for 2010 (at the end of June 2009) was 51.25 ($78.17) per MWh, and a renewable generator would also receive Renewables Obligation Certificates that were selling for around 52.00 ($79.32) per MWh (UK Powerfocus 2009). A 1% change in load factor leads to an increase in output of 88 MWh (for a 1 MW unit), and hence an extra 4.70 ($7.17) per kW per year in income. 25. A state-owned transmission company potentially has the best financial cushion of all, but it may well be less receptive to financial incentives than a private-sector organization. 26. The alternative paradigm uses a capacity market to recover generators fixed costs, sometimes coupled with payments linked to spot prices that should act to restrain those prices (Cramton and Stoft 2008). 27. If a high level of integration between retailing and renewable generation extended to the generation
sector overall, then the barrier to entry in retailing would be severe. If several regional incumbents exist within a larger market, however, effective competition among them is possible, though not inevitable. 28. In the United Kingdom, fuel-poor households are defined as those that need to spend more than 10% of their income to achieve acceptable standards of heat and light. 29. Heat pumps basically use refrigerator technology in reverse to extract heat from the environment, chilling a fluid so that it is colder than conditions outside (or underground) and can absorb heat from its surroundings. Compressing the fluid then raises its temperature to a point where the heat can be extracted and used. The gross energy delivered by a heat pump can be four times that used for pumping, in which case 75% of the energy delivered would be counted as renewable by the EU. 30. In countries where the regulator is constrained to closely follow specific laws, this implies that those laws should give the regulator tasks that achieve these same aims.
ReferencesBorenstein, S. 2005. The Long-Run Efficiency of Real-Time Electricity Pricing. Energy Journal 26 (3): 93116. Cramton, P., and S. Stoft. 2008. Forward Reliability Markets: Less Risk, Less Market Power, More Efficiency. Utilities Policy 16 (3): 194201. DECC (Department of Energy and Climate Change). 2009a. Consultation on Renewable Electricity Financial Incentives 2009. London: DECC. . 2009b. The UK Renewable Energy Strategy. Cm 7686. London: Stationery Office. DoE (Department of Energy). 2006. U.S. Department of Energy Strategic Plan. Washington, DC: Department of Energy. Douglas, J. 2005. In the Zone. Power Engineering International 13 (12): 4547 DTI (Department of Trade and Industry). 2003. Our Energy Future: Creating a Low Carbon Economy. Cm 5761. London: Stationery Office. EIA (Energy Information Administration). 2009. US Independent System Operators. www.eia.doe.gov/ cneaf/electricity/page/channel/fig8.html (accessed March 22, 2010). European Commission. 2003a. Directive 2003/54/EC of the European Parliament and of the Council of 26 June
2003 concerning Common Rules for the Internal Market in Electricity and Repealing Directive 96/92/EC. Brussels: Commission of the European Communities. . 2003b. Directive 2003/55/EC of the European Parliament and of the Council of 26 June 2003 concerning Common Rules for the Internal Market in Natural Gas and Repealing Directive 98/30/EC. Brussels: Commission of the European Communities. . 2007. Renewable Energy Road Map: Renewable Energies in the 21st Century: Building a More Sustainable Future: Impact Assessment. Accompanying Document to the Communication from the Commission to the Council and the European Parliament. SEC (2006) 1719/3. Brussels: Commission of the European Communities. Green, R. J. 1997. Electricity Transmission Pricing: An International Comparison Utilities Policy 6 (3): 177 184. . 2005. Electricity and Markets Oxford Review of Economic Policy 21 (1): 6787. Gross, R., P. Heptonstall, D. Anderson, T. C. Green, M. Leach, and J. Skea (2006) The Costs and Impacts of Intermittency: An Assessment of the Evidence on the Costs and Impacts of Intermittent Generation on the British Electricity Network. London: Imperial College. Hannah, L. 1979. Electricity before Nationalisation. London: MacMillan. Hawkes, A., and M. Leach. 2007. Cost-Effective Operating Strategy for Residential Micro-combined Heat and Power. Energy 32 (5): 711723. Helm, D. R. 2008. RenewablesTime for a Rethink? www.dieterhelm.co.uk/node/488 (accessed March 22, 2010). House of Lords. 2008a. The Economics of Renewable Energy, Economic Affairs Select Committee Fourth Report of Session 20078. HL195-I of 20078. London: Stationery Office. . 2008b. The Economics of Renewable Energy, Economic Affairs Select Committee Fourth Report of Session 20078. Vol. 2, Evidence. HL195-II of 20078. London: Stationery Office. Jamasb, T., and M. G. Pollitt. 2007. Incentive Regulation of Electricity Distribution Networks: Lessons of Experience from Britain. Energy Policy 35 (12): 61636187. Joskow, P. L. 2008. Lessons Learned from Electricity Market Liberalization. Energy Journal 29, Special Issue in Honor of David Newbery, 942. Laffont, J.-J., and J. Tirole. 1993. ATheory of Incentives in Procurement and Regulation. Cambridge: MIT Press.
Littlechild, S. C. (2008) Some Applied Economics of Utility Regulation. Energy Journal 29, Special Issue in Honor of David Newbery, 4362. Newbery, D. M. (1999) Privatization, Restructuring and Regulation of Network Utilities. Cambridge, MA: MIT Press. Ofgem. 2009. Innovation in Energy Networks: Is More Needed and How Can This Be Stimulated? Regulating Energy Networks for the Future: RPI-X@20. Working paper 2. London: Office of Gas and Electricity Markets. Prez-Arriaga, I. J., F. J. Rubio, J. F. Puerta, J. Arceluz, and J. Marin. 1995. Marginal Pricing of Transmission Services: An Analysis of Cost Recovery. IEEE Transactions on Power Systems 10 (1): 546553. Pollitt, M. G. 2004. Electricity Reform in Chile: Lessons for Developing Countries. Journal of Network Industries 5 (34): 221262. . 2005. The Role of Efficiency Estimates in Regulatory Price Reviews: Ofgems Approach to Benchmarking Electricity Networks. Utilities Policy 13 (4): 279288. . 2008. The Future of Electricity (and Gas) Regulation in a Low-Carbon Policy World. Energy Journal 29, Special Issue in Honor of David Newbery, 6394. Pollitt, M. G., and J. Bialek. 2008. Electricity Network Investment and Regulation for a Low-Carbon future. In Delivering a Low Carbon Electricity System: Technologies, Economics and Policy, edited by M.
Grubb, T. Jamasb and M. Pollitt. Cambridge: Cambridge University Press, 183206. Sappington, D. 1982. Optimal Regulation of Research and Development under Imperfect Information. Bell Journal of Economics 13 (2): 354368. Schulz, W. 1995. Restructuring the Electricity Market: A German View. In Competition in the Electricity Supply Industry: Experience from Europe and the United States, edited by O. J. Olsen. Copenhagen: DJF Publishing. Shleifer, A. 1985. A Theory of Yardstick Competition. RAND Journal of Economics 16: 319327. Sorrell, S. 2007. The Rebound Effect:An Assessment of the Evidence for Economy-wide Energy Savings from Improved Energy Efficiency. London: UK Energy Research Centre. Spiller, P. T., and L. Viana Martorell. 1996. How Should It Be Done? Electricity Regulation in Argentina, Brazil, Uruguay and Chile. In International Comparisons of Electricity Regulation, edited by R. J. Gilbert and E. P. Kahn. Cambridge: Cambridge University Press: 82125. UK Powerfocus. 2009. Month at a Glance. UK Powerfocus 109: 1. Yago, M., J. P. Atkins, K. R. Bhattarai, R. J. Green, and S. D. Trotter. 2008. Modelling the Economic Impact of Low-Carbon Electricity. In Delivering a Low Carbon Electricity System:Technologies, Economics and Policy, edited by M. Grubb, T. Jamasb, and M. Pollitt. Cambridge: Cambridge University Press, 394413.
9Building Blocks: Investment in Renewable and Nonrenewable TechnologiesJames Bushnellithin a span of 20 years, the electric power industry has become the central focus of two extraordinary policy trends, each one significant enough to fundamentally reshape the industry. One of these trends is liberalization, a term that has come to encompass both privatization and regulatory restructuring. Beginning with the visions articulated in such works as Joskow and Schmalansee (1985) and Schweppe et al. (1988), the restructuring movement in electricity can be viewed as an extension of the trend toward market liberalization that had previously transformed the airline, communications, and natural gas industries. The generation sector of the industry has undergone a sporadic but inexorable transition from economic regulation under cost-of-service principles to an environment in which markets heavily influence, if not dominate, the remuneration and investment decisions of firms. The second trend to engulf the electricity industry has been the growth of the environmental movement. More specifically, the growing alarm over the threat of global climate change, and the more recent engagement of policymakers in combating it, is likely to dominate decisionmaking in the power industry over the next several decades. Electricity and heat production are responsible for 40% of CO2 emissions in the United States and about 31% worldwide (Stern 2006).
Although not obvious at first glance, these two trends, restructuring and environmental regulation, share many common ideological roots. In the United States, the growing stringency in air quality regulation was accompanied by an increased acceptance of market-based environmental regulations. These include cap-and-trade mechanisms, such as the program put in place to limit SO2 emissions under the 1990 amendments to the Clean Air Act (Ellerman et al. 2000). Regulators were also interested in experimenting with market-based incentives to promote alternative energy sources. Many trace the birth of the U.S. independent power industry to the passage of the Public Utilities Regulatory Policies Act (PURPA) in 1978. The PURPA legislation established mandates for the purchase of energy produced by qualifying small and renewable sources of generation (Joskow 1997; Kahn 1988). Although inspired largely by environmental and energy security goals, the largest impact of PURPA is arguably in the resulting demonstration of the viability of smaller-capacity generation technologies and the nonutility generator business model. One important aspect of the independent power producer business model was the relative freedomand riskallowed in investment of new facilities. Investments are based on market-
James Bushnell
based long-term contracts and projections of market revenues, rather than regulatory findings of need and guaranteed cost recovery. The restructuring movement in the United States was led by states with the worst track records in utility investment (Ando and Palmer 1998). Although evidence suggests that operations have become more efficient in these states (Wolfram 2005), restructuring was primarily intended to improve the incentives of firms to make prudent investments (Borenstein and Bushnell 2000). In some parts of the world, this general approach to investment has come to dominate the industry. In many others, policymakers continue to search for the proper tools for balancing market incentives with concerns over reliability and adequacy of investment (Joskow 2005; Oren 2005). One central aspect of this search concerns the design of wholesale electricity markets and the payment streams they provide to suppliers. Markets can differ greatly on the primary sources of remuneration for generators, with some relying on energy and ancillary services markets, while others have established mechanisms for compensating suppliers for their installed or available capacity (Bushnell 2005; Cramton and Stoft 2005). This chapter studies the intersection of these two trends as they come to dominate the economics of the industry. In particular, it examines how the increasing penetration of intermittent renewable generation can change the economic landscape for merchant power investment in conventional thermal generation. Currently, renewable generation earns revenues from a wide range of sources, from energy markets to government tax credits. The impact of renewable generation on the electricity markets in which they participate has to date been relatively modest outside of regions of high concentration such as west Texas. That will almost certainly change, however, as state and federal policies considerably ramp up the amount of renewable generation throughout the country. This can have a profound impact on prices and the economics of supply for both renewable and nonrenewable generation. An equilibrium model of generation investment is developed, based on the long-standing principles of finding the optimal mix of capital-
intensive and higher-marginal-cost resources to serve a market with fluctuating demand. This model is then applied to data on electricity markets from several regions of the western United States to examine how the interaction of increasing wind capacity and electricity market design affects the equilibrium mix of thermal capacity and the revenues earned by renewable suppliers. The chapter first provides a brief background on this question, then describes the equilibrium conditions that form the stylized investment model. Next, it details the data and assumptions used in the study. The final section contains the bulk of the results and analysis.
the growth of both renewable and nonutility generation. To this day, the renewable industry is dominated by nonutility producers.1 The subsidization of renewable generation is expanding in parallel with efforts to create capand-trade programs for CO2. This can be seen as antithetical to the spirit of a cap-and-trade program, where promoting flexibility in compliance options is a central ideal. Unlike the SO2 program, cap-and-trade is but one of a broad set of policy tools being brought to bear against greenhouse gas (GHG) emissions. Some view this as undermining the strength of cap-and-trade regulation. The cap has less incremental impact if much of the GHG reductions are already accounted for under various more directed measures and regulations. In regions such as California, cap-and-trade is viewed more as a backstop than as a bulwark in combating climate change. Although policies that promote renewable generation sources are extremely popular with regulators, politicians, and the general public, their continued expansion to unprecedented levels does raise some concerns. One source of concern is cost. Although the technological frontier continues to advance, much controversy exists over the appropriate timing and form of policy intervention to promote renewable generation. Most accept that renewable generation would not be a significant source of supply today if not for some form of public support. The fact that the external cost of GHG emissions have not yet been priced into the investment decisions of fossilbased generation firms certainly provides justification for support of renewable power, but the prospect of regional and possibly national caps on CO2 emissions undermines that justification. A common argument for support of renewable generation is the hope that expansion of supply will yield learning benefits, thereby lowering costs of future supply. However, a market failure exists only if that learning cannot be appropriated for private gain. Although the bulk of public support for renewable generation has taken the form of production mandates or credits, it is not clear whether commercialization is the point in the supply chain where the problem of intellectual property is most acute. Further, the evidence to
date indicates that cost reductions in alternative energy sources can be driven as much by exogenous technology developments as by the expansion of installed capacity (Nemet 2006). The most commonly heard concern over the rapid expansion of renewable electricity supply is over the fact that this supply is available only intermittently (NERC 2009). With the prospect of one-fifth or more of electrical energy coming from intermittent sources, many in the industry are confronting the fact that the traditional tools for planning for and providing reliable electric service may prove inadequate. In fact, as discussed below, the traditional utility planning paradigm has been disrupted by market liberalization over the last 10 years. The industry has yet to settle on a single framework to replace utility planning. The large-scale addition of intermittent resources is therefore happening against a backdrop in which the mechanisms through which generators are compensated are very much in flux.
would guarantee the recovery of costs undertaken to meet that need. In the liberalized market, private firms no longer receive a guaranteed recovery of their investments. One of the hopes for liberalization was that this market-based risk would lead to more prudent and cost-effective investment decisions. At the very least, it was observed, the costs of overinvestment would be borne by investors rather than ratepayers under the new market regime. In many markets, this latter belief has been supported by the fact that many of the firms that procured or expanded capacity in liberalized markets experienced severe financial difficulties during the early part of this decade. Whereas a transition away from payments based on a cost-of-service framework is shared by all liberalized markets, the revenue streams that replaced these payments differ greatly. Many markets focus the remuneration of generators on the provision of energy and related services. In the jargon of the U.S. electricity industry, this conceptual framework has been referred to as an energy-only framework. The name, which is somewhat inaccurate, refers to the fact that contributions against fixed and sunk costs arise only from payments for the provision of either energy or associated operating reserve services. Although no market is fully unconstrained in this way, markets such as those found in the United Kingdom, Australia, Texas, New Zealand, and Norway operate under general energy-only principles to the extent that they have no or relatively high price caps and provide no other specific payments for the supply of capacity. In many markets, however, the revenues provided from the provision of energy and ancillary services appear to be insufficient to cover the fixed cost of new entry (Joskow 2005). Myriad reasons can be given for this, including the existence of price caps, the subtle but significant impact of the decisions of system operators on market prices, and simply the overinvestment of capacity. This and other factors have led to a level of discomfort among many policymakers over leaving investment decisions entirely up to the market. Therefore, many electricity markets, including several in the United States, provide payments for capacity availability that supplement revenues received
for the provision of energy and ancillary services. This feature is not unique to the United States, as capacity payments played a significant role in the early years of market liberalization in the United Kingdom and continue to be a significant factor in Spain and Colombia. The details of these capacity payments vary, but the general common features that are represented in the stylized model used here are a formal or informal constraint on energy prices combined with a fixed payment (here assumed to be in dollars per megawatt-year) based on installed capacity. The fixed payments can be scaled according to the expected or historic availability of generation, a fact most significant for wind generation sources. In many restructured markets, some form of payment for installed or available capacity is made to producers as a supplement to the revenues they earn through the sale of energy and ancillary services. These payments are not without controversy, however, as debate continues over how exactly to measure and remunerate the provision of reliable capacity (Cramton 2003; Hogan 2005; Oren 2005). One aspect of this debate is how to deal with unconventional sources of generation. Resources, such as hydroelectric facilities, that are energylimited cannot produce at their full capacity all the time. Many renewable resources can supply power only intermittently, and their supply is dependent on ambient conditions rather than under the control of the operator. In general, the capacity payments made to resources such as these are scaled downward according to rough probabilistic measures of their potential availability. As explored below, the specification of such rules will interact with the level of penetration of renewable generation to shift the relative value of different types of payment streams for intermittent producers. The power industry today therefore features two contrasting models for financing new investment: the energy-only model, which relies on periodic, extremely high prices for energy and ancillary services to provide the scarcity rents that are applied to the recovery of capital costs; and the capacity payment model, in which a large portion of the capital costs are recovered through capacity
payments. Under energy-only markets, the choice and profitability of specific generation sources will depend on the degree and timing of high prices. Under capacity markets, the spot energy prices are somewhat less critical, but the specific implementation of capacity payments is very important to the relative profitability of technologies. The large-scale deployment of intermittent resources can imply a major paradigm shift for both investment models. Electric systems will likely experience a massive addition of renewable generation capacity that is largely motivated by nonmarket considerations such as climate change. This will result in an influx of energy with extremely low marginal cost, but only during some time periods. As a result, the remaining need for thermal generation capacity could look very different than it would in the absence of the renewable capacity. In market terms, the levels and patterns of energy prices could be quite different with the addition of renewables. The months and hours that experience peak prices will be driven as much by the availability of intermittent resources as by the fluctuations in end-use demand. These questions are explored empirically in the following section.
that achieve the break-even point where annual energy revenues for each technology equal their annualized cost of capacity. As in Borenstein (2005), these values depend on prices rising above the marginal cost of the highest-cost technologies where, in effect, demand sets the market price. This process has come to be called scarcity pricing in wholesale electricity markets. Similar to Lamont (2008), the model also incorporates intermittent resources. As described below, the wind production profiles used here are based on specific projections of wind production profiles, rather than the stylized correlation coefficients used by Lamont. The model here assumes perfect competition, essentially free entry into any generation technology in the markets, and also disregards concerns of lumpiness of capacity. Firms are free to install any combination of capacity sizes that satisfy differentiable equilibrium conditions. This greatly simplifies computational concerns and, in light of the size of the markets being examined here, is not an unreasonable assumption. As this is a longterm model, it also ignores operational constraints such as minimum run times, start costs, and ramping rates. These are obviously important considerations of operating an electricity system that will be affected by the expansion of intermittent technologies, but they are beyond the capabilities of the model used here. The approach of the model is to examine the actual load profiles or hourly distributions of demand of certain markets, and then impose varying levels of intermittent wind production on those demand distributions. In other words, the wind investment is considered exogenous to the equilibrium investment model, having been implemented through nonmarket constraints such as a renewable portfolio standard. The model then derives the mix of thermal technologies that would be constructed to serve the resulting residual demand that is left over after accounting for wind production. The equilibrium resulting from an assumption of competitive entry and no lumpiness is equivalent to the optimal, or leastcost, set of technologies. The intuition behind the equilibrium constraints described in the appendix is straightforward. Firms will continue to con-
struct additional capacity in a given thermal technology as long as the revenues implied by the residual demand are sufficient to cover a levelized cost of investment, as well as operating costs. The empirical calculations are based on data taken from the Western Electricity Coordinating Council (WECC) for the reference year 2007. These data are in turn subdivided into the four WECC subregions: the California (CA) region; the Northwest Power Pool (NWPP) region; the southwest (AZNM) region, made up mostly of Arizona and New Mexico; and the Rocky Mountain Power Pool (RMPP) region (see Figure 9.1). The general approach is to ask how electricity load would have looked during 2007 under various levels of wind penetration. The model then solves for the equilibrium investment mix of conventional technologies that optimally serves the resulting load shape. This section includes descriptions of the data sources and assumptions used in implementing this calculation. It is important to keep in mind that this is not a simulation of the incremental investment required going forward in these markets, but rather an exercise that examines how the long-run equilibrium mix of generation and costs would change. Thus it is not meant to be predictive of these actual markets, but uses these market data to
develop calculations for a range of possible representative markets. The market-based model assumes that all regions are restructured (when in fact, only California is currently even partially restructured) and that the investment choices are starting from a clean slate of no existing capacity. One difficulty with simulating electricity markets in a high level of detail is that, although data on most fossil-fuel-based generation units are quite extensive and reliable, far less data exist on the activities of hydroelectric plants, renewable generation, and the substantial amount of power generated from combined heat and power (CHP) or cogeneration plants. When building a counterfactual re-creation of an electricity market, these data gaps make assumptions about the missing production necessary. This chapter takes the approach of restricting the construction of a counterfactual market outcome to the portion of resources for which detailed data are available. In effect, it assumes that, under the counterfactual assumptions of wind penetration, the operations of nonmodeled generation plants would not have changed. The total production from clean sources is unlikely to change in the short run. The production of electricity missing from the data is driven by natural resource availability (rain, wind, sun) or, in the case of CHP, to nonelectricity production decisions. The economics of production are such that these sources are essentially producing all the power they can. However, it is important to recognize that this modeling approach assumes that existing unconventional sources will not change not only how much they produce, but also when they produce it. This is a problematic assumption in regions with substantial hydro resources, such as the Pacific Northwest. Ideally, an investment analysis would involve a co-optimization of hydro, wind, and thermal electric production. This is beyond the scope of this chapter. For this reason, the results pertaining to the Pacific Northwest region should be interpreted with this shortcoming in mind. In any event, the goal here is not to reproduce the electric system as it actually operated in 2007, but rather to assess how investment decisions would play out if the industry were starting from a
completely clean slate and faced the residual (after existing unconventional generation) load shapes of 2007. The data used here are meant to convey conditions present in representative electricity systems, rather than completely reproduce a specific system.
Demand DataThe primary data source for this discussion is the BASECASE dataset from Platts, which is in turn derived primarily from the continuous emissions monitoring system (CEMS) used by the U.S. Environmental Protection Agency (EPA) to monitor the emissions of large stationary sources.2 Almost all large fossil-fired electricity generation sources are included in this dataset, although hydroelectric, renewable, and some small fossil generation sources are missing. The CEMS reports hourly data on several aspects of production and emissions. Hourly data on nuclear generation plants are included with fossil generation data in the BASECASE dataset. The model here uses the hourly generation output and carbon emissions for available facilities. These hourly output data are aggregated by firm and region to develop the demand in the simulation model. As described above, this is in fact a residual demand: the demand that is left after applying the output from non-CEMS plants. Plant cost, capacity, availability characteristics, and regional fuel prices are then taken from the Platts POWERDAT dataset. These data are in turn derived from mandatory industry reporting to the Energy Information Administration (EIA) and North American Electric Reliability Council (NERC). These data are then combined to create a demand profile and supply functions for periods in the simulation. Although hourly data are available, for computational reasons these are aggregated into representative time periods. Each of the four seasons has 50 such periods, yielding 200 explicitly modeled time periods. The aggregation of hourly data was based on a sorting of the California residual demand. California aggregate production was sorted into 50 bins based on equal MW spreads between the minimum and maxi-
mum production levels observed in the 2007 sample year. A time period in the simulation therefore is based on the mean of the relevant market data for all actual 2007 data that fall within the bounds of each bin. For example, every actual hour (of which there were 14) during spring 2007 in which California CEMS production fell between 7,040 and 7,243 MW were combined into a single representative hour for simulation purposes. The number of season-hour observations in each bin is therefore unbalanced; there are relatively few observations in the highest and lowest production levels, and more observations closer to the median levels. The demand levels used in the simulation are then based on the mean production levels observed in each bin. In order to calculate aggregate production and revenues, the resulting outputs for each simulated demand level was multiplied by the number of actual market hours used to produce the input for that simulated demand level. Table 9.1 presents summary statistics of CEMS load levels for each of the four WECC subregions.Table 9.1. Summary statistics of demandCEMS load (MW) Region CA NWPP AZNM RMPP Mean 13,216 15,334 17,942 6,986 Min. 6,022 9,670 13,626 5,531 Max. 29,985 18,884 25,586 9,141 S.D. 3,626 2,400 2,706 723
potential data with other local sources of information to construct projections of new wind development, as well as of hourly wind production from those potential new sources. This chapter draws on the hourly load profiles of the projected wind facilities from the WECC study and aggregates these profiles according to the four WECC subregions described above. Because of the focus here on the investment impacts of wind penetration, this section looks at the baseline level of estimated production used in the WECC study and also a level that is double that used in the WECC study. The aggregate generation levels are summarized in Table 9.2. As a portion of CEMS load, the new wind sources would account for about 15% of 2007 CEMS energy, although these resources are not evenly distributed across the WECC.3 The RMPP area, which includes the wind-rich areas of Wyoming, has a great deal of wind potential, whereas the desert Southwest has much less. When the projected additional wind production is combined with and assumed to displace CEMS production, the result is a sharply shifted residual load profile that must be served by conventional generation sources. Figures 9.2 and 9.3 illustrate the hourly CEMS load, both before and
after accounting for the additional wind resources for the months of August and December. The aggregate effects are well summarized by load duration curves presenting the cumulative distribution of CEMS load and residual demand after new wind sources. Figure 9.4 presents these load duration curves for the four subregions. The CEMS load profile in California is much more variable than in other regions, while CEMS load levels in the Pacific Northwest are relatively constant because of the abundance of hydro energy in that region. In all cases, the increasing penetration of wind resources makes the load profiles steeper. This reflects the fact that wind pro-
duction is not correlated with CEMS load. To the extent that more wind production is generated in low CEMS load hours, the residual load becomes more variable and the load duration curve steeper. This effect is most pronounced in the RMPP region, where CEMS load was relatively constant but which experiences the highest degree of wind penetration.
The market implications of Figure 9.4 are central to the results of this chapter, so they deserve a little further discussion. The increasing penetration of wind resources in the WECC will create a surge of energy supply, much of which will be uncorrelated with end-use demand. The net result is a residual load shape that is more peaky. As will be demonstrated in the results of the
simulations, the optimal mix of resources to serve this profile of residual demand will be composed of a far greater share of low-capacity-cost, highmarginal-cost peaking resources.
region is treated as isolated from the others. This is equivalent to assuming that transmission flows among the regions do not change from their 2007 levels. As described above, the four regions represent a wide spectrum in terms of current demand for generation and future wind potential.
Energy-Only MarketThe results are first examined under an assumption that each region operates under an energyonly market paradigm, with no price cap and no capacity payments. The analysis begins with the equilibrium energy prices in each market. Figures 9.5 and 9.6 illustrate the hourly market-clearing energy prices in each market for the final week of August and first week of December. Note that these prices are plotted on a logarithmic scale, reflecting the highly volatile nature of equilibrium electricity prices in an energy-only market. Although significant differences in energy prices are difficult to detect in CA, the impact of wind penetration is clear on the pricing patterns in regions such as the NWPP and RMPP. The changes in the residual demand profiles and the resulting equilibrium prices do have a significant effect. Figure 9.7 summarizes the equilibrium investment levels under three wind scenarios: wind at 2007 levels, wind at 14% of CEMS load, and wind at 28% of CEMS load. Several aspects of the results are reflected in Figure 9.7. First, the already volatile CA load profile implies an optimal mix of relatively little base load generation compared with the other regions, whereas the very consistent load of the NWPP implies an optimal mix that is heavily base load,
Source: EIA 2007 Notes: O&M = operating and maintenance costs; HR = Heat Rate
Price ($/MWh)
8,800 8,500
with no peaking resources at all under the baseline scenario. Second, the increasing penetration of wind resources produces a clear shift of investment toward less capital-intensive peaking resources in every market. This shift is most pronounced in the RMPP region, where wind penetration is the greatest as a percentage of baseline
CEMS load. Third, less thermal capacity is needed in every market as a reflection of the fact that wind generation has lowered the residual demand required to be served by thermal sources. However, the equilibrium thermal capacity requirement is reduced only modestly by the entry of new wind capacity.
NWPPLevels of 2007 Wind at 14% Wind at 28% 4,000 12,000 20,000 Levels of 2007 Wind at 14% Wind at 28% 4,000
Coal CTGraphs by region
These factors are summarized in Table 9.5. For each region, the aggregate equilibrium thermal capacity and assumed wind capacity are given in the first two columns of figures. The assumed average capacity factor, taken from the wind profiles from the WECC study is given in the next column, and the shares of thermal capacity that are base load and peaking are given in the last two columns. Note that the large levels of new windTable 9.5. Equilibrium results for energy-only marketThermal Capacity (MW) CA 23,308 22,753 22,442 NWPP 14,472 13,188 12,237 AZNM 20,276 19,691 19,141 RMPP 6,751 6,000 5,374 New wind Capacity (MW) NA 5,670 11,340 NA 7,890 15,780 NA 3,840 7,680 NA 4,650 9,300
capacity, those of more than 10 gigawatts (GW), result in reductions of equilibrium thermal capacity of only 1 to 2 GW. Across the regions, the reduction in thermal capacity averages about 15% of the new installed wind capacity, with relatively little variation across regions. It is important to mention again, however, the strong assumption made here that hydro output, particularly in the NWPP region, would
Wind capacity factor NA 33% 33% NA 28% 28% NA 39% 39% NA 41% 41%
Share Coal 43% 36% 28% 93% 81% 64% 73% 68% 62% 86% 61% 26%
Share CT 44% 50% 55% 0% 4% 10% 11% 14% 17% 7% 20% 37%
not adjust to the new intermittent capacity. By taking advantage of the implicit storage potential of the hydro resources, one would expect the equilibrium capacity needs in this region to be reduced quite a bit more than implied by this calculation.
It is worth noting that these values are quite a bit lower than those currently seen in U.S. electricity markets. One reason for this is that the investment numbers from the EIA represent generic investment costs for the country, while capacity markets tend to operate in regions of the United States, such as California and New York, where investment can be much more costly. Another more important driver is that these equilibrium simulations are allowing the price to rise above the marginal cost of a peaking plant more frequently than has been historically seen in these markets. This is a reflection of the fact that the model determines the equilibrium, break-even level of capacity, whereas todays markets tend to feature more capacity than this level. In practice, todays capacity markets do not attempt to differentiate among causes of revenue shortfalls; they usually calculate net costs of entry based on historic energy prices.5 Therefore, the revenues lost to the price cap in this simulation produce less missing money than has been estimated from current capacity market proceedings. Next, the above simulations are repeated, with two adjustments to the original model summarized by equations (9.4) and (9.5) in the appendix at the end of this chapter. The most striking results are naturally found in the hours that were formerly those with prices significantly above $1,000. Figures 9.8 and 9.9 illustrate the changes to the peak hour price duration curves for the CA and RMPP regions because of both wind penetration and the capacity market policies. These figures summarize the 150 highest price hours in each market, in order from highest to lowest. Note that prices in these figures are on a logarithmic scale because of the high volatility. For the wind scenarios, the same hours are plotted. As is clear from these figures, the highest price hours in the baseline simulations are not those producing the highest prices as wind investment increases. This reflects that fact that as wind investment increases, prices are increasingly driven by wind availability as well as total end-use demand. This is particularly true for the RMPP region, where the highest price hours under high levels of wind investment rank below the 100th highest price hours without the wind investment.
Energy only
These figures also illustrate the impact that the price cap has on these scarcity price hours. In general, the highest price hours are reduced to the cap levels, and price levels in most other hours remain unchanged. The resulting impact of these capacity market elements on investment levels are summarized in Table 9.7.
New wind Capacity (MW) N/A 5,670 11,340 N/A 7,890 15,780 N/A 3,840 7,680 N/A 4,650 9,300
Wind capacity factor N/A 33% 33% N/A 28% 28% N/A 39% 39% NA 41% 41%
Share coal 43% 36% 28% 93% 81% 64% 73% 68% 62% 86% 61% 26%
folio of plants that features the same production profile as the regional aggregate profile used to construct the residual demand. In order to evaluate the revenues of intermittent resources under a capacity market paradigm, further assumptions are needed about how the reliable capacity of those resources, on which the capacity payment is based, is measured. The most basic, and clearly overgenerous, method would be to assume that 100% of the installed capacity was eligible for capacity payments. Given the intermittent availability of wind resources, this is not the usual approach. A more conventional approach is to discount the installed capacity according to a historical measure of the capacity factor (average energy output divided by capacity) of either the specific unit or the class of technologies from which the unit is drawn. Even this approach can overstate the value of capacity if the production profile of a generation unit is negatively correlated with total system load. A third approach, similar to one recently adopted for the purposes of measuring wind and solar capacity in California, is to measure the production of resources only during high demand hours, discarding production statistics for other hours.6 For purposes of comparison, revenues have been calculated under a capacity market in two
ways, roughly following the options outlined above. The first approach discounts the capacity payment according to the annual capacity factor, derived from the wind profile data. The second approach calculates a capacity factor only for hours 14 through 17 of each day. The results for the two alternative calculations of capacity factor (using annual average and peak hour average) were very similar, so only the revenues are reported, assuming capacity payments are based on the peak hour average capacity factor. Table 9.8 summarizes the revenues of this hypothetical average wind turbine for each region. These values are given in terms of $/kWy. By comparison, the peaking units are earning $95.82/kWy, while coal plants are earning $282.17/kWy.7 To the extent that actual costs of new wind facilities would exceed these equilibrium investment revenue levels, the difference would have to be captured in subsidieseither through the production tax credit or through price premiums paid by utilities in order to comply with their renewable portfolio standards. As further reference, using the same assumptions and cost estimates from the EIA as were used to calculate thermal annual fixed costs, wind costs would be roughly $231/kWy.
Table 9.8. Summary of the revenues of the hypothetical average wind turbine ($/kWy)Energy-only New wind Region CA NWPP AZNM RMPP CEMS load 113.24 123.49 138.31 158.39 14% of CEMS 112.11 106.48 135.00 142.03 Capacity Market New Wind Region CA NWPP AZNM RMPP CEMS load 126.74 123.49 138.35 158.39 14% of CEMS 124.82 106.48 135.59 143.02 New Wind 28% of CEMS 122.31 105.17 136.27 144.36 New wind 28% of CEMS 109.27 105.17 132.75 135.17
Note that under the energy-only market, the revenues for an average profile wind plant decline in each region. This is because prices are being influenced increasingly by wind availability, and a profile that mirrors the system wind profile would be producing during hours of glut and not producing during hours of wind production shortfall. If revenues are instead based on a combination of capped energy market revenues and capacity payments, wind producers do a little better than under the energy-only paradigm. This is a much stronger effect under the high wind penetration scenarios.8 Revenues in the RMPP area are about 5% higher with a capacity payment. This is in part because the capacity payment rewards production during high-demand hours, whereas the energyonly market rewards production during high-price hours. As wind penetration increases, the highprice hours are relatively more focused on lowwind hours than on high-demand hours.
CO2 markets with a price of $25/metric ton. This value is approximately the 2012 futures price for one ton in the European Unions Emissions Trading System (ETS) market for CO2. It is also assumed that the imposition of CO2 prices affects only marginal and not capital or fixed costs of any of the thermal generation technologies. The same approach as before can be used to calculate the resulting equilibrium investment levels of thermal capacity and revenues for wind facilities. Only the equilibrium is calculated under the energy-only market paradigm. Figure 9.10 illustrates the equilibrium investment capacities under the different wind scenarios. Note that coal is much less used in all markets and is driven out of the CA and RMPP markets completely under high wind penetration. Table 9.9 summarizes the energy market revenues earned by the hypothetical wind plant under various levels of wind penetration. With the $25/ton carbon price, wind revenues are substantially higher overall. The degradation of these revenues with increasing wind penetration is also more pronounced, however. With a price on CO2, revenues in the RMPP region are only 5% lower per kilowatt-year under high wind penetration than they would be for the first megawatt of wind capacity added to that region. This is contrasted to the almost 15% decline in revenues for the same comparison in the absence of a carbon
market. With carbon at $25/ton, wind resources are able to earn relatively more revenues during even off-peak hours when coal would be setting the price. Increasing wind penetration leads to more of these hours, but the differential between these off-peak and on-peak hours is smaller than in the absence of a carbon price.Table 9.9. Wind revenues with carbon price at $25/ton (in $/kWy)New wind Region CA NWPP AZNM RMPP CEMS load 194.15 188.06 231.09 251.85 14% of CEMS 193.00 173.46 228.41 246.10 New wind 28% of CEMS 185.29 172.81 227.96 238.87
costs of serving the residual demand (e.g., that which is left over after the new energy is applied) are calculated under the assumption that new energy appears in a manner consistent with the two wind penetration scenarios described above. In other words, the average cost of constructing and operating thermal plants to meet the demand that is not met by the renewable production is calculated. Second, the same amount of energy is used as in the wind scenarios, but instead assuming that it is applied as a base load supply source. In other words the new energy is distributed evenly across all hours. The results of this calculation are summarized in Table 9.10. The columns labeled As wind refer to the same wind distributions that have been applied to previous results, and those labeled as base load show the results for the evenly distributed energy. Because the more volatile wind profiles require the construction of fewer base load plants and less frequent operation of peaking plants, average costs are higher under the wind profiles. In California, average costs from the variability of supply increase about 4% ($3/MWh) under 14% wind penetration and close to 9% ($7/ MWh) with high wind penetration. In the highpenetration RMPP region, costs rise close to 25% under the high-wind-penetration scenario.
ConclusionsThe increasing deployment of renewable resources whose intermittent production is determined by natural forces will reshape the economics of power generation in developed electricity markets. This chapter has presented calculations on what the optimal mix of major conventional generation sources would be under various assumptions of end-use demand and penetration of wind generation. Data on actual demand for thermal generation in the western United States were combined with highly detailed estimates of production from new wind resources for this region of the country. The result is a load shape with relatively higher spreads between peak and average demand for thermal production. As demonstrated in the equilibrium model, the amount of coal-fired base load production that would be an economical equilibrium investment steadily declines as wind penetration increases. The reliance on the low-capital-cost combustion turbine technology increases. Another key change in the economics of power systems will come from the rising importance of intermittent production as a driver of market prices. As these simulations demonstrate, the availability (or lack) of wind resources will be an important contributor to market clearing prices. The normal relationship between end-use demand levels and market prices becomes redefined as wind resources grow to take a substantial share of the market. Implications of this are
that wind resources that are typical, in the sense that their output is correlated with the bulk of other wind resources, will earn less, and the total capacity of wind resources is ramped up. Their production will be correlated with hours of surplus and therefore increasingly less correlated with prices. In the presence of a capacity market, this effect is more muted. This is because capacity marketsat least at presentaward capacity payments based on availability during high-demand periods, rather than high-price periods. This too may change, however, as the underlying economics of the energy markets become more strongly influenced by the ebbs and flows of intermittent generation. Overall, increasing reliance on intermittent resources creates, or increases, costs in a fashion similar to that caused by fluctuating end-use demand. In planning to serve a system where consumption fluctuates widely, firms must turn to resources that are more flexible, but also more expensive on an average cost basis. While the added costs associated with fluctuating end-use demand can be greatly mitigated by enabling price-responsive consumption, the intermittency of renewable supply is a fact of nature. Storage technologies can play a valuable role here, and estimates such as those developed in this chapter can provide an indication of the potential value of such storage options. Although the analysis in this chapter was grounded in data taken from actual energy markets, it is important to recognize the limitations of
this exercise. Two important elements of some electricity markets are missing here, although their effects work in opposite directions. The short-term operational constraints of thermal generation units have not been modeled. The presence of such constraints would tend to favor the nimble combustion turbine technology even more heavily. Also not modeled is the potential reallocation of production from energy-limited resources, namely hydroelectric. In the Pacific Northwest in particular, this will be an important resource that can go a long way toward counteracting the effects of intermittent generation. In fact, even with these limitations, the effects of wind penetration in the NWPP region are relatively minor in contrast to the wind-rich but hydro-poor Rocky Mountain region. The potential for increased trade among the regions also has not been modeled, although such trade will be limited by the availability of transmission capacity. Of course, the real western United States is not starting from scratch in building its investment portfolios. Outside of California, coal-fired generation is currently a mainstay of electric companies west of the Mississippi. To the extent that these results portend changes in the economics of these technologies, they would affect the earnings of the owners of these technologies more than the actual mix of generation resources.
The perfectly competitive firms in the model continue to add production in any given hour, and capacity overall, as long as the revenues from adding the production or capacity exceed the costs. In equilibrium, therefore, production levels in any hour will be set such that the marginal cost of production equals the market price. This equilibrium point can be represented with the following complementarity condition: qit 0 pt ci it 0 i, t (9.1) where it represents the equilibrium shadow value of the capacity of technology i and will never be positive if price is below marginal costs ci. This is the shadow price on the constraint that production be no greater than installed capacity, as reflected in the following condition: it 0 qi t Ki 0 i, t (9.2) Equation (9.1) is therefore equivalent to setting price equal to marginal operating costs as long as production quantities are below the capacity constraint Ki. The equilibrium level of investment will arise from the condition that the value of a marginal unit of capacity equals the cost of that capacity. Ki 0 Fi tit 0 i (9.3) where tit represents the cumulative value of an extra unit of capacity type i aggregated over all time periods. Recall that this value is zero for a given period if the capacity is unneeded in that period, which in this model is equivalent to prices falling below the marginal cost of production of technology i. The equilibrium level of investment and production can be found by simultaneously solving for the above three conditions. These conditions form a complementarity problem (see Cottle et al. 1992) of size t i. The following sections describe the data used in formulating the empirical model.
Market DemandDemand is represented with the partial-log function: Qrt (prt) = art br ln(prt)
where r is used to denote the region. The value for b was set at 800 for CA, NWPP, and AZNM, and a value of 400 was used for the smaller RMPA. The price elasticity for this functional form of demand is equal to br /Qr, so at the mean observed demand level (summarized in Table 9.1), elasticity is about 0.05 in each market. In other words, a nonzero but still very modest level of demand response is assumed. One advantage of this functional form of demand is that its convexity implies little price response at levels around the marginal costs of generation, but more response when prices reach scarcity levels over $500/MWh.
Notes1. Private independent power producers (IPPs) own 83% of cumulative wind capacity in the United States (Wiser and Bolinger 2009). The passage of the Emergency Economic Stabilization Act in November 2008 could constitute a major shift in this trend. Among the acts many provisions was the extension of investment tax credit (ITC) for certain forms of renewable generation. The act also allows, for the first time, utility companies to take advantage of the ITC, which had previously been reserved only for nonutility producers. The CEMS data are available at www.epa.gov/ cems. The Platts datasets, POWERDAT and BASECASE, are available via paid subscription service at www.platts.com. The study assumes a 15% total renewable penetration, but only half of that is estimated to come from wind. However, in 2007, about half of the existing energy currently generated in the WECC came from non-CEMS sources. So the wind portion of our residual demand profiles is roughly 7.5% of total load in the base case, and 15% under the assumption of doubled wind capacity. The mechanisms and levels for limiting prices varies by market. Markets in the eastern U.S. technically limit offer prices to $1,000/MWh. In theory market clearing prices can rise above this level, but they have not in practice done so. One hypothesis (Joskow 2005) is that actions taken by operators to preserve reliability also coincidently limit prices below scarcity levels required to recoup investment costs. An example of such a calculation for the New York ISO region can be found in NERA (2007). The newly adopted California rule also uses an exceedance measure, rather than a capacity factor.
5. 6.
This means that the capacity payment is based on the percentage of peak hours in which production exceeds a given threshold (e.g., 70%) of nameplate capacity. Using the data in this discussion, this measure gave extreme results, so instead the focus here is on a measure of peak hour capacity factor. Recall that these are their annual entry costs, and the equilibrium conditions equilibrate net operating costs with these annual fixed costs. The $1,000/MWh price cap was almost never binding in the NWPP and AZNM regions; therefore, the results for the energy-only market and capacity markets are virtually the same.These markets have no missing money, and thus no capacity payment was necessary even with the price cap in place.
ReferencesAndo, Amy W., and Karen L. Palmer. 1998. Getting on the Map: The Political Economy of State-Level Electricity Restructuring. Working paper 9819REV. Washington, DC: Resources for the Future. Borenstein, Severin. 2005. The Long-Run Efficiency of Real-Time Electricity Pricing. Energy Journal 26 (3): 93116. Borenstein Severin, and James B. Bushnell. 2000. Electricity Restructuring: Deregulation or Reregulation? Regulation 23 (2). Bushnell, James. 2005. Electricity Resource Adequacy: Matching Policies and Goals. Electricity Journal 18 (8): 1121. Cottle, R. W., J.-S. Pang, and R. Stone. 1992. The Linear Complementarity Problem. London: Academic Press. Cramton, Peter. 2003. Electricity Market Design: The Good, the Bad, and the Ugly. Presented at the 36th Annual Hawaii International Conference on Systems Science. January 2003, Hawaii. Cramton, Peter, and Steven Stoft. 2005. A Capacity Market that Makes Sense. Electricity Journal 18 (7): 4354. Ellerman, A. D, P. L. Joskow, Richard Schmalensee, J. P. Montero and E. Bailey 2000. Markets for Clean Air. Cambridge: Cambridge University Press. EIA (Energy Information Administration). 2007. Annual Energy Outlook. Washington, DC. Hogan, William H. 2005. On an Energy Only Market Design for Electricity Resource Adequacy. Working paper. Cambridge, MA: Harvard Univer-
sity. http://ksghome.harvard.edu/~whogan/ Hogan_Energy_Only_092305.pdf (accessed 12 October, 2009). Joskow, Paul L. 1997. Restructuring, Competition and Regulatory Reform in the U.S. Electricity Sector. Journal of Economic Perspectives 11 (3): 119138. . 2005. The Difficult Transition to Competitive Electricity Markets in the United States. In Electricity Deregulation: Choices and Challenges, edited by James Griffin and Steve Puller. Chicago: University of Chicago Press, 3197. Joskow, Paul L., and Richard Schmalensee. 1985. Markets for Power:An Analysis of Electrical Utility Deregulation. Cambridge, MA: MIT Press. Kahn, Edward E. 1988. Electric Utility Planning and Regulation. Washington, DC: American Council for an Energy-Efficient Economy. Lamont, Alan D. 2008. Assessing the Long-Term System Value of Intermittent Electric Generation Technologies. Energy Economics 30: 12081231. Nemet, Gregory F. 2006. Beyond the Learning Curve: Factors Influencing Cost-Reductions in Photovoltaics. Energy Policy 34: 32183232. NERA (National Economic Research Associates). 2007. Independent Study to Establish Parameters of the ICAP Demand Curve for the New York Independent System Operator. www.nyiso.com/public/ webdocs/committees/ (accessed October 14, 2009). NERC (National Electricity Reliability Council). 2009. 2009 Long-Term Reliability Assessment. www.nerc.com/files/2009_LTRA.pdf (accessed October 14, 2009). Nickell, Bradley M. 2008. TEPPC Renewable Energy Cases: Renewable Generation Information. Salt Lake City: Western Electricity Coordinating Council. Oren, Shmuel S. 2005. Ensuring Generation Adequacy in Competitive Electricity Markets. In Electricity Deregulation: Choices and Challenges, edited by James Griffin and Steve Puller. Chicago: University of Chicago Press, 388414. Schweppe, Fred C., Michael C. Caramanis, Richard D. Tabors, and Roger E. Bohn. 1988. Spot Pricing of Electricity. Boston: Kluwer Academic Publishers. Stern, Nicholas. 2006. The Stern Review:The Economics of Climate Change. London: HM Treasury. Wiser, Ryan, and Galen Barbose. 2008. Renewable Portfolio Standards in the United States, Berkeley, CA: Lawrence Berkeley National Laboratory. http://eetd.lbl.gov/ea/ems/re-pubs-html (accessed September 9, 2009).
Wiser, Ryan, and Mark Bolinger. 2009. 2008 Wind Technologies Market Report. DOE/GO-1020092868. Washington, DC: U.S. Department of Energy. Wolfram, Catherine. 2005. The Efficiency of Electricity Generation in the U.S. after Restructuring. In
Electricity Deregulation: Choices and Challenges, edited by James Griffin and Steve Puller. Chicago: University of Chicago Press, 227255.
10Developing a SupergridChristian von Hirschhausen
arnessing renewables effectively requires transporting electricity generated by renewable energy to centers of demand. The difficulty of doing this is sometimes underestimated in the policy debate on renewable targets. Renewable energy sources are fuel from heaven only if the derived products, mainly electricity, are delivered to the customers locale on demand. In this context, the concept of a supergrid, or transmission overlay network, is being intensely debated on both sides of the Atlantic. This concept has been propelled to center stage by politically bold statements about the future share of large-scale renewable energy sources in a carbon-constrained world. In addition, China has recently started to develop long-distance high-voltage direct current transmission to transport more hydroelectricity from the center of the country to demand locations on its east coast. As the political debate accelerates, however, the pros and cons of supergrids must be objectively assessed and the different, sometimes competing, visions put into a comparative perspective. This chapter discusses issues related to the development of supergrids that are currently debated in the context of harnessing renewable energy. The term supergrid refers to the assimilation of a high-voltage direct and alternating current (HVDC and HVAC) network overlay with
the existing traditional alternating current (AC) network, with the objective of integrating largescale renewable energy sources (L-RES) into the grid. This chapter does not address the link between renewables and existing low-voltage networks, nor does it cover operational issues, balancing, or other topics at the local low-voltage level. Instead, it highlights some of the key issues, conceptually and by using a variety of existing concepts for potential supergrids. A distinction is made between studies focusing more on the harnessing aspect of renewables, such as the Solar Grand Plan, and those that include a more serious network component, such as the Joint Coordinated System Plan developed for a group of North American system operators. An inverse relation exists: the more a supergrid study looks at the obstacles to developing the appropriate transmission infrastructure, the less such studies tend to prefer supergrids, instead arguing in favor of more decentralized, regional solutions. If it is commonly accepted that network development is important for harnessing L-RES, few researchers to date have looked closely at the engineering-economic aspects of expansion projects on both sides of the Atlantic. The concept of linking European electricity markets to renewable energy potential in the Middle East and North African (MENA) region via HVDC trans-
mission is particularly interesting and is presented as an in-depth case study. The European Economic AssociationMiddle East North Africa (EEA-MENA) supergrid interconnection plan for 2050 is a variant of the Desertec project and the Mediterranean Solar Plan (MSP), but with its own distinctive features. Thus, in addition to including different amounts of concentrated solar power (CSP) electricity imported from the MENA region to Europe by constructing a supergrid, the study also considers northern European offshore wind and hydro resources and finds that the consideration of a truly European market for renewable electricity changes the topology of the optimal supergrid significantly, tending toward more regional clustering. Also highlighted is an important distributional issue that arises between winners of more transmission lines, whose electricity prices fall, and losers, whose prices increase. Next, several critical issues for the further development of supergrids are examined. Transmission investment has been problematic in both the United States and Europe, and the development of a supergrid poses even more complex long-term challenges to industry and policymakers. For industry, a balance has to be struck between lucrative investments in generation and transmission assets and the economic, political,andregulatoryrisksrelatedthereto.Policymakers, on the other hand, face the challenge of having to design instruments and regulatory frameworks to achieve very long-term objectives given high uncertainty and widely diverging incentives between stakeholders, such as federal versus regional levels and producers versus consumers. Surprisingly little attention has been given to long-term planning mechanisms, a critical element in such complex projects. Planning and regulatory issues are closely related to financing issues, and the optimal organizational structure for a supergrid has yet to be identified. The discussion also includes a political economy interpretation of the obstacles to overcome, among other compensating mechanisms between potential winners and losers. The chapter therefore concludes on a skeptical note. The findings that have been pre-
sented temper the enthusiasm that generally accompanies supergrid concepts: although the underlying objectives for their development may be well founded, the large number of obstacles makes a rapid emergence of supergrids unlikely. Instead, it seems more reasonable to focus on a stepwise, bottom-up approach by which transmission expansion follows a gradual, more traditional path.
Developing a Supergrid
Also, the term transmission overlay is used to characterize a portfolio of high-voltage transmission additions to existing electricity networks that together serve and link entire regions and markets (Kaupa 2009; Midwest ISO et al. 2008). Supergrids are thus characterized by the following:
735 kV AC 6.7% 3 GW
500 kV DC 6.6% 3 GW
flexibility in system balancing; high capacity for bulk power transmission; and geographically long distances.
In addition to the prevailing alternating current (AC) technology in power transmission, HVDC or Flexible AC Transmission Systems (FACTS)1 technologies are increasingly used in supergrids. These hybrid systems can improve transmission capacity and system stability by bypassing heavily loaded AC systems. The relevance of these features increases with the prospective integration of large-scale renewable generation capacities that produce higher fluctuations in loads. Moreover, power generation becomes increasingly distributed and a growing number of generation capacities are located far from load centers, leading to greater transmission distances. In this context, HVDC offers two important advantages over AC technologies:
The multiple visions of future supergrid developments can be classified according to technical, economic, or institutional criteria. To structure the subsequent discussion, electricity generation and transmission are differentiated as follows:
generation: supergrid projects that rely on one source of renewable generation (singlesource) or integrate a variety of L-RES (multiple sources); and transmission: gradual extension of existing AC with additional local and interregional power lines or radical changes that include a transmission overlay with multigigawatt highways interconnecting entire continents.
On the one hand, higher bulk power capacities can be realized per line. Thus fewer lines are necessary, an important factor in the publics acceptance of new transmission projects, which is often low because of the not-in-mybackyard (NIMBY) mindset. On the other hand, HVDC lines have lower power losses over long distances, as shown in Table 10.1. Despite the higher initial investment costs of HVDC and FACTS, these characteristics make DC bulk power transmission ( 1,000 megawatts) more economical than AC for transmission distances above 600 kilometers (Claus et al. 2008). HVDC transmission technology is rapidly evolving. It allows longer water crossings and does not require phase synchronization.2
Table 10.2 classifies the major existing visions and concepts for the United States and Europe based on these criteria. The subsequent sections review studies with different grades of detail and focus, ranging from conceptual ideas to more detailed calculations, and outline some of the most relevant supergrid visions. This discussion follows the structure in the table, starting with the U.S. projects and then looking at those in Europe and North Africa.
Krapels (2009), Integrating 200,000 MWs of Renewable Energy into the US Power Grid Egerer et al. (2009), Sustainable Energy Networks for Europe
This study builds on a wind-only study for the United States; see AEP 2007.
across the United States, called the Solar Grand Plan. In essence, it is based on a combination of photovoltaic (PV) facilities spread across different areas and concentrated solar power (CSP) systems in the Southwest, where the highest average solar irradiation in the country can be harnessed (Figure 10.1). The Solar Grand Plan also includes a substantial amount of compressed-air energy storage (CAES) located close to demand. New HVDC lines would connect the Southwest to the rest of
the country. The technologies, scale, and learning for a project of this size are substantial; thus 2,940 GW of PV should be developed through a national energy plan built around solar power. Increased thin-film module efficiency from 10% to 14% and reduced installation costs from $4 to $1.20 per watt (W) in 2050 would lead to levelized electricity costs of $0.05 per kilowatthour (kWh). A corresponding development is also expected for CSP, though at a somewhat smaller scale: cumulated 558 GW in 2050 will lead to
decreasing installation costs from $5.30 to $3.70/W. Thus electricity generation costs for CSP drop to $0.09/kWh. By 2050, solar power would provide 35% of total energy consumed in the United States and 70% of electricity. If implemented, the Solar Grand Plan would substantially reduce fossil-fuel consumption, CO2 emissions, and U.S. import dependence.The plans total costs sum up to $420 billion until 2050.3 Fthenakis et al. (2009) concentrate on the feasibility of the vision laid out by Zweibel et al. (2008). They refine the proposal of a two-step implementation plan: Stage I (from the present to 2020) comprises a 10-year solar deployment and incentive program that includes guaranteed loans, a mandatory solar portfolio standard for electric utilities, and a solar feed-in tariff scheme. Costs are estimated at $300 billion; this amount would ensure a transition to self-sustained growth in the CSP and PV markets after 2020. Nevertheless, Stage II (from 2020 to 2050) should include a commitment to an annual deployment schedule to sustain growth for renewable technologies. It is evident that HVDC transmission is a crucial part of the Solar Grand Plan, as 90% of the plans solar energy production is located in the Southwest. Transmission-wise, the Solar Grand Plan therefore proposes a radical approach: an entirely new HVDC power transmission backbone. Interestingly, no major technical advances are deemed necessary. During Stage I, the first expansion of the DC transmission system would occur, extending the network via existing rightsof-way along interstate highway corridors, which would minimize land acquisition and regulatory hurdles.4 In Stage II, the remaining extensions would occur. The HVDC transmission companies would not need to be subsidized, because they would finance construction of lines and converter stations just as they now finance AC lines, earning revenues by delivering electricity (Zweibel et al. 2008, 57).5 However, three issues are not addressed by the authors. First, the role of other L-RES, such as wind power in the Great Plains, as potential competitors to solar power is underrepresented. In this context, it appears questionable that solar power from the Southwest will serve the majority of the
U.S. electricity supply until 2050. Second, nationwide transmission planning is key, yet Fthenakis et al. (2009) give a vision of HVDC transmission only for the Southwest. The anatomy of their HVDC transmission overlay does not take into account storage sites, also a key element of the plan. Third, economic analyses of the welfare effects as well as profit considerations have not been included so far.
transmission overlay consists of 765 kV AC or 800 kV DC. Supported by a network of 345 and 500 kV AC lines as well as several 800 kV DC feeder lines, seven major HVDC lines will trans-
port electricity from renewables and thermal plants in the Midwest to the load centers in the East and Southeast.
JCSP Option 3 3 1 Two Circuit lines by Voltage kV 345 (4) 500 (5) JCSP Option 3 3 1 Lines by Voltage kV 230 (1) 345 (48) (18) 500 (27) 765 800 HVDC (2) JCSP Option 3 3 1 HVDC Feeder Lines by Vo tage kV 400 and 800 HVDC Existing Lines greater than 230 by voltage kV 800 to 801 (2) 765 to 800 (34) (560) 500 to 765 (1324) 345 to 400 230 to 345 (4111)
Methodologically advanced, the highrenewables scenario for transmission planning can be critically assessed with respect to the concentration on onshore wind. Canadian hydro potential as well as offshore wind capacities should be more strongly reflected in such a scenario to determine future transmission expansion. Midwest ISO et al. (2008) also recommend that future analyses should pay more attention to local and decentralized energy generation scenarios rather than long-distance production and transmission; this would affect the type, location, and cost of necessary transmission infrastructure and could change the associated social benefits.
in Texas, connect the wind and solar in the North and West to the population centers in the Center and South; and in the South, where wind resources are meager, allow nuclear power plants to meet RPS and carbon targets (Krapels 2009, 4).
Krapels rejects the economic and political feasibility of a coast-to-coast electricity superhighway and argues for a series of initiatives from coastal states that would essentially confine the superhighway to a smaller area complemented with a system enabling the coastal states to harness nearterrestrial and offshore wind (see Figure 10.3). This coastal complement would eliminate the need for midwestern wind delivered via the proposed national electricity superhighway. According to Krapels, regional transmission planning in the United States has not led to the desired interstate transmission expansion to increase competition, improve security of supply, and lower average consumer prices.8 These traditional mechanisms will not allow L-RES to be built at the envisioned scale and thus must be addressed in detail. Despite the regional approach, institutional hurdles remain the most challenging part of transmission expansion.
Figure 10.3. Integrating 200,000 MW of renewable energy in the U.S. grid (exemplified for wind power)
lines (see Figure 10.4). The connection of distributed wind over long distances reduces wind farms power variability. It has been shown that power output correlation decreases as distance between wind farms increases (see, e.g., Sinden, 2007), because wind patterns are regional and change over longer distances as a result of areas of high and low barometric pressure. The question, however, is to what extent wind power will prove to be a reliable and predictable source of energy (Airtricity 2006). Considering the demand side, the differences in lifestyles, time zones, and uses of electricity will lead to elongated peak demand and therefore result in a greater capacity credit for the supergrid than for offshore wind farms being connected to a single national electricity system. Moreover, the study argues that the HVDC submarine grid would resolve much of the complexities associated with achieving a single European electricity market, as the supergrid would serve as an interconnector among national markets. The study delivers neither capacity projections nor cost estimations for the proposed panEuropean submarine supergrid. Instead, it suggests an initial project in the North Sea as a nucleus for further HVDC expansion.10 Airtricitys (2006) calculation for this 10 GW initial project deserves critical evaluation with respect to two issues. First, the projects economics are strongly dependent on the assumed finance
structure, but the proposed debt interest rate of 5.5% per annum seems rather low for a project of this size and scope. Hence it implicitly requires financial guarantees by public institutions to leverage private capital. Second, because a predictable yield for investors is necessary to attract investment, it will likely require a fixed electricity price for wind power and preferred feed-in guarantees. Institutional and political barriers will remain the major roadblocks to such supranational agreements.
oped jointly by the Trans-Mediterranean Renewable Energy Cooperation (TREC), a network of scientists, politicians, businesspeople, and the German society of the Club of Rome.11 The technical studies were carried out by the German Aerospace Center (DLR) and financed by the German Ministry for the Environment (BMU) (see Trieb et al. 2005, 2006).12 The Desertec project focuses on the generation of about 2,400 TWh per year of mainly CSP by 2050. This is based on generation cost reductions for CSP, where electricity costs are expected to drop from 9.8 euro cents (U.S. 13.3 cents) per kWh in 2015 to 5.5 euro cents (7.5 cents) per kWh in 2050.13 About 700 TWh of this solar electricity will be supplied to the European Union (EU) via a new HVDC overlay network. For this, Trieb et al. (2009, 5) assume an HVDC transfer capacity of 20 5 GW cables. Investment is in the same range as for the U.S. Solar Grand Plan, 400 billion ($545 billion) until 2050, of which 45 billion ($61 billion) is dedicated to the HVDC overlay network. Figure 10.6 shows the structural network that has been developed using
least-cost algorithms with several constraints. An HVDC core connects 11 CSP production sites in the MENA region with 27 European demand centers, including corridors for exports of Saudi Arabian CSP to destinations as far away as Milan, Paris, and even London. The Desertec project is more explicit about the institutional structure of the transmission lines than its U.S. equivalent, the Solar Grand Plan. It proposes the creation of an international transmission association to be owned by the companies participating in the construction and operation of the HVDC lines. The Nabucco Gas Pipeline International GmbH, a project company set up to develop a multinational natural gas pipeline from Turkey to Austria, is considered a role model. The transmission project association would be responsible for planning, financing, constructing, operating, and marketing of the supergrid. A close relation between the national and interregional transmission system operators (TSOs) would need to be ensured either through direct ownership or contractual arrangements. The main criticism of the transMediterranean supergrid as proposed in Trieb et al. (2009) is related to other L-RES deployment. The proposed HVDC network overlay does not take wind and hydropower in the North Sea and
Scandinavia into account. Thus the authors imply that solar power from the MENA region can compete with these renewables in the United Kingdom and the countries adjacent to the North Sea. Considering the potential for other L-RES, the supergrids layout (as shown in Figure 10.6) appears questionable. In addition, the implementation of the proposed HVDC grid overlay, stretching from Saudi Arabia to the United Kingdom, seems challenging in terms of its institutional and political obstacles even under the proposed institutional design.
projects require reaching an operational level of transmission planning and regulation. Surprisingly, in none of the numerous studies surveyed for this chapter is there any reference to technology as an obstacle. There is also a large consensus that transmission issues are critical, whereas expanding the use of renewable energy sources as such is less difficult. This holds even though the pure investment sums for tapping the renewable energies significantly exceed the transmission investments. Few studies surveyed include an economic analysis beyond some rough financial indicators, such as costs. In the United States, Midwest ISO et al. (2008) calculate consumer benefits under constraints of security of supply. For Europe, Trieb et al. (2009) provide some cost predictions but do not address welfare considerations. The next section therefore explores these aspects in the analysis of another specific project, the EEAMENA supergrid, before the discussion turns to the general issues faced by all of the supergrid projects.
Modeling ApproachThe subsequent simulation, applies an engineering-economic DC load flow model based on ELMOD (Leuthold et al. 2008). ELMOD is a welfare maximization model with
technical constraints, including thermal limits, electricity losses, loop flows, and security constraints. The applied model consists of 105 regional zones, represented by nodes. The grid consists of interzonal high-voltage lines connected to the nodes by auxiliary nodes and lines. Each main node has its own demand and generation portfolio. The existing European electricity grid, comprising the UCTE region, NORDEL, the United Kingdom transmission system operator, and other UKTSO members, is modeled, with the model limited to the high-voltage level (132 to 750 kV). The existing AC grid includes all existing interzonal links and high-voltage AC lines according to ENTSO-E (2008) and the other European TSOs. Concerning the future development of the AC grid, all projects planned by the European system operators until 2030 are assumed to be completed (ENTSO-E 2009).15 Further AC grid expansion is not implemented because of the lack of available information beyond 2030. In addition to the AC grid, existing DC connections are implemented in the model with its technology-specific parameters.16 Electricity demand is characterized by a reference demand and a reference price. While the reference demand is given by the scenarios, the reference price equals the costs of the marginal plant under base load conditions. A linear inverse demand function is determined for each region with an assumed price elasticity of 0.1. The CSPexporting regions are modeled as simple export nodes. Generation capacities comprise 14 types of plant according to three groups: fuel-fired (nuclear, lignite, hard coal, gas-fired combined cycle gas turbines [CCGT], gas, oil, and biomass); renewable generation (run of river, wind on- and offshore, PV, CSP, and geothermal plants); and storage (hydropower reservoirs). As the fluctuating character of wind generation cannot be determined by season or time of day, three caseshigh, medium, and loware included for each load level. This leads to a total of 24 model cases, distinguishing among seasons, day and night, demand, and wind availability. All model cases have equal weights.17 An overview of the endogenous and exogenous model parameters is presented in Table 10.4.
a Prices for emissions certificates are based on a review of price projections (see Egerer et al. 2009, 44). CO2 prices have been allocated to generation costs for different technologies via technology-specific emission factors.
ScenariosGiven the high variance in any of the parameter estimations until 2050 (e.g., demand, technologies, climate policy), the study is limited to a business-as-usual (BAU) scenario as the reference case and a technological development (TD) scenario that captures the main characteristics of a renewable future as possible boundaries for the EEA-MENA supergrid. Figure 10.7 provides an overview of the generation capacities for the BAU and TD scenarios for 20102050. Fossil-fuel price
developments are based on current market prices and develop according to fuel-specific escalation rates ranging between 1% and 2.5% per annum (in real terms). In both scenarios, slightly increasing electricity demand is expected, from 3,500 TWh (2008) to 4,200 TWh (2050). In the BAU scenario, coal, lignite, and nuclear remain almost on the same level as today except in Germany, where nuclear is phased out. The appearance of L-RES is limited: only 60% of European offshore wind and no Scandinavian hydropower potentials are exploited, and CSP
Technological development
Business as usual Geothermal Biomass CSP Photovoltaic Wind/Offshore Wind/Onshore Reservoir Hydro Oil Gas CCGT Coal Lignite Nuclear
2010 2020 2030 2040 2050 2010 2020 2030 2040 2050Source: Egerer et al. 2009, 42
Figure 10.7. Installed capacities for the BAU and TD scenarios, 20102050
capacity in the MENA region does not exceed 12 GW until 2050. This development assumes moderate CO2 certificate prices, with 24 ($33) per metric ton in 2030 and 30 ($41) per ton in 2050. In contrast, the TD scenario adopts a perspective of an 80% reduction of CO2 emissions by 2050 (base 1990). To realize these targets, an increase of the CO2 price to 57 ($78) per ton in 2030 and 100 ($136) per ton in 2050 is assumed. Also assumed are intensive technology R&D and that the technical potentials described in terms of harnessing renewables, particularly wind, are attained. Nuclear is phased out, and any potential hydrogen developments are ignored. The deployment of smart grids allows a higher share of local decentralized renewables in addition to the L-RES that are modeled explicitly. Natural gas serves as backup capacity. In addition to CSP integration in North Africa, other L-RES potential in Europe is reflected, in the TD scenario, by two subscenarios for the integration of wind and hydropower capacities. The first subscenario, A, assumes reinforcement of the grid in addition to the modeled network described above to transport the vast amount of future offshore wind from the North Sea to demand centers in the United Kingdom, France, and Germany. This is reflected in the model by additional transmission in the countries adjacent to the North Sea. In the second subscenario, B, the HVDC lines connecting Scandinavia to the rest of Europe are expanded exogenously to a capacity of 8 GW per connection in 2050. These lines are modeled in addition to the transmission capacity according to subscenario A. Thus subscenario B can be considered to be close to the concept of the North Sea Energy Super Ring discussed above.18 These two subscenarios allow an examination of the welfare benefits of seasonal hydropower generation from reservoirs as possible balancing power to offshore wind.
welfare maximizing paths for grid expansion in the EEA-MENA supergrid. Third, effects from a stronger integration of the Scandinavian electricity market are considered.
ResultsFollowing are the results of the model. First, generation capacities and prices for the BAU and TD scenarios are discussed. The second part proposes
by 2050. For this limited electricity generation capacity, four transmission cables are built, and the capacity is split in half between the Moroccan and Tunisian export nodes. The first two lines are built in 2030, and the other two are installed in 2050. In 2030, CSP electricity exports from Tunisia to Italy yield only 3.8 euro cents (U.S. 5.2 cents) per kWh cost coverage and, because of higher electricity prices in Spain, 4.4 euro cents (6 cents) per kWh cost coverage for Moroccan CSP plants. Rising electricity prices in Spain until 2050 will lead to 6 euro cents (8.2 cents) per kWh cost coverage.20 These figures indicate that CSP will not become competitive in the BAU scenario. For the TD scenario, more lines30 connections until 2050 with a capacity of 4 GW each need to be built to transmit the 113.8 GW of peak export CSP generation from the MENA region in 2050 (Table 10.5). The TD scenario assumes extensive CSP generation in Morocco, Tunisia, and the Middle East. For 2020, the first line is built from Morocco to southern France; interestingly, it does not supply the Spanish market, as the Iberian electricity market has large shares of less expensive CCGT compared with more expensive coal from Italy and Germany that influences the electricity price in France. The other two MENA export nodes, Tunisia and the Middle East, choose one of the closest demand nodes for the HVDC connection because of lower line costs. Although the model is welfare-maximizing, CSPs profitability is crucial to potential investors. With the assumed price path for gas and coal, electricity price in the targeted sales market is around 5 euro cents (6.8 cents) per kWh. Considering transmission costs of roughly 1 to 2 euro cents (1.4 to 2.7 cents) per kWh, the amount left for generation is far from the break-even point of about 9 euro cents (12 cents) per kWh for CSP in
2020. Therefore, the first installations must either be subsidized or be built in the expectation of future profitability from rising electricity prices. A decade later, CSP reaches its break-even point. In 2030, CSP generators in the three exporting regions can sell electricity at prices of 7 to 7.7 euro cents (9.5 to 10.5 cents) per kWh net of transmission costs. By then, CSP plants built in the MENA region are assumed to have levelized generation costs of 7 euro cents (9.5 cents) per kWh.21 Continuously rising electricity prices would lead to even higher profits over the lifetime of the CSP facilities.Thus a rapid diffusion of CSP starting in 2030 seems possible. Under profitable generation conditions for CSP, transmission lines develop according to Figure 10.8 until 2050. However, no transmission is realized to any node of the reinforced northern European grid. This result suggests that a more regional integration of CSP generation capacity is the preferred mechanism. CSP from the export node in Morocco is connected to Spain and France, Tunisia delivers most of its exported electricity to Italy, and the CSP generation from the Middle East is connected to the southeastern part of Europe. No lines connect to Germany, the Benelux, or the United Kingdom.22
Table 10.5. Number of HVDC transmission cables and CSP generation capacity in MENA (TD scenario)2020 Morocco Tunisia Middle EastSource: Egerer et al. 2009, 53
1 1 1
Source: Egerer et al 2009, 57 Note: Each line has a capacity of 4 GW unless a higher number indicates multiple lines.
The welfare increase for the exogenous expansion scenario for HVDC connections outweighs the associated cost. Therefore, an endogenous optimization could be even more profitable (this has not been modeled here because of the increasing complexity). Electricity prices are converging: by 2050, prices in Scandinavia increase by 4 euro cents (5.4 cents) per kWh (price increase by 50%) and decrease in the rest of Europe by about 1 euro cent (1.4 cents) per kWh. Lower prices in the entire European electricity market (continental Europe) lower the profits of CSP generation in MENA. The welfare optimal expansion paths for HVDC connections from MENA to Europe remain the same until 2050.
On the one hand, a stronger integration of northern Europes electricity markets has a positive impact on the total welfare by balancing wind from the North Sea with reservoirs in Scandinavia. With the additional integration of the
Scandinavian electricity markets, the annual welfare gains increase strongly over time. In 2050, the annual welfare benefit exceeds 3 billion ($4 billion), while the levelized annual cost of the necessary connectors between Scandinavia and continental Europe is about half that amount. Therefore, a more detailed analysis of concepts like the North Sea wind power transmission ring seems reasonable. On the other hand, changes in welfare are accompanied by a modified distribution of consumer and producer surplus. Thus prices increase in Scandinavia when a higher share of inexpensive hydro can be exported to the United Kingdom and central Europe. Consequently, Scandinavian consumer surplus decreases while producer surplus increases. In the United Kingdom and central Europe, lower electricity prices have the opposite effect. Figure 10.9 presents estimated zonespecific price differences from the reinforced interconnections to Scandinavia in 2050. It becomes obvious that customers in the Nordic
Northern zones
Southern zones
euro cents/kWh 3 4 5
Figure 10.9. Electricity price changes with reinforced interconnectors to Scandinavia in 2050
countries have good reasons to favor a low integration of the Scandinavian electricity market, despite reduced welfare for the rest of Europe. The integration of Scandinavia and the lower prices in central and southern Europe are bad news for a rapid diffusion of CSP and, in fact, could postpone its development for years because of the lower prices in the zones connected to MENA by HVDC. It seems probable that an isolated analysis of CSP generation without considering electricity system developments, such as the integration of available potential for wind and balancing hydro capacity, may result in misleading forecasts. Thus a single focus on L-RES from the MENA region seems inappropriate.
volume. Nevertheless, generation and transmission have to be taken into account equally, because both stages in the value chain are intertwined: as long as generation capacity has not been built up, transmission lines are not necessary and vice versa. Not only because of this chicken and egg problem, it should also be determined how best to finance generation and transmission expansion. Several public and private initiatives are under way, such as the Trans-Mediterranean Energy Cooperation/Desertec Initiative, Union for the Mediterranean, and bilateral technology agreements. However, common institutional and regulatory platforms among the EEA and MENA countries need to be further developed to finance[bn ] 200150 100 50 0 2020 2030 CSP investment 2040 2050 Transmission investment
Figure 10.10. Required generation and transmission investment for the EEA-MENA supergrid
such large transnational infrastructure projects. This includes coordinated funding schemes by the EEA member states national development banks, multilateral development banks (European Bank for Reconstruction and Development, African Development Bank), the MENA states in close cooperation with the UNFCCC, the World Bank Group, private investors (private equity funds, venture capital firms, private-public partnership entities), and other stakeholders. In this context, the importance of a stable regulatory framework as a prerequisite for leveraging private capital should be kept in mind. Apart from institutions, funding could be provided for an EEA-MENA supergrid by several mechanisms:
renewable energies according to the recent EU Directive 2009/28/EC (EU 2009a, Article 9, 2009/28/EC).
AssessmentThe EEA-MENA case study provides insights into the potential unfolding of a supergrid and the related technical and economic aspects. Clearly, there may be economic benefits from developing a supergrid, such as the use of less carbonintensive sources of electricity and higher reliability within the transmission network. The EEAMENA case study showed welfare benefits of the grid development starting in 2030, given assumptions that do not seem to be outside the realm of reality. Transmission expansion develops gradually from a nucleus of point-to-point relations and spreads over the region over time. Taken as a whole, the project is profitable, but this ignores risk assessment and an analysis of real business cases. Similar results might be expected from welfare assessments or costbenefit analyses (CBA) of the other projects presented in this chapter. The assumptions of the model and the choice of technologies and regions considered have a significant effect on the results, and thus the results discussed here differ somewhat from those obtained in the Desertec study by Trieb et al. (2009). The model of the European electricity system applied here allows for a spatial and temporal resolution of demand, generation capacities, and electricity prices. In particular, applying a multisource approach (solar, wind, and hydro) in a truly pan-European context yields quite different results in terms of network design and prices. Thus the North Sea Energy Super Ring or any other North Sea energy grid topology (Seatec) may become a competitor to the solar-founded Desertec project. The results underline that the project needs to be seen in the context of other transmission expansion projects, capacity replacements, and evolving generation from L-RES. Compensation mechanisms need to be put in place as a cooperation scheme between the losers (transit countries) and the winners from lower electricity prices. The above discussion has touched on these institutional obstacles, including financing problems.
Extending transferability of certified emission reductions (CERs) from clean development mechanism (CDM) projects within the EU Emission Trading Scheme (EU-ETS); a less probable alternative is the de facto integration of MENA countries into the EU-ETS via direct substitutability of European emissions allowances by CERs from MENA countries. Establishing new, or actively exploiting, existing international technology transfer programs and funds, including the World Banks and multilateral development banks Climate Investment Funds and Clean Technology Fund, bilateral clean energy and technology funds, and future United Nations Framework Convention on Climate Change (UNFCCC) Multilateral Clean Technology Funds as proposed by the G77+ countries (see World Bank 2008; Seligsohn et al. 2009). Granting financial guarantees or extending policy instruments for renewable energy technology deployment (e.g., feed-in tariffs, tax incentives) by EEA countries to energy projects in the MENA region. Setting a generous limit for the upcoming determination of allowed renewable energy imports from joint projects between EU member states and third countries to meet the national targets on the promotion of
One issue that needs further exploration is the multiple incentive structure of the actors involved in the process. In particular, the North African countries may face conflicts of interest in considering the supergrid option. Previous rent earners, such as electricity consumers in the Nordic countries and the natural gas industry, may oppose the project. The domestic nonenergy industry has argued for the deployment of energy-intensive industries in the MENA region, using electricity from L-RES and exporting the products, such as green aluminium; this is expected to favor the emergence of new industries and employment. Also, instead of transmitting electricity physically to the EEA, natural gas could serve as a currency for electricity from L-RES in MENA countries with natural gas resources, such as Libya and Algeria. Some actors have indeed argued that the EEA countries could pay a fixed price for CSP-generated electricity, which can be converted to an amount of gas with equivalent energy content. Exporting gas through existing pipelines and via LNG terminals might avoid the need for extensive electricity transmission expansion. Thus it is not clear whether the supergrid idea is the preferred solution of all players involved.
in transmission expansion might combine elements of both markets and hierarchies, becoming hybrid organizational forms.
Planning IssuesAny supergrid requires long-term planning to create the conditions under which large-scale transmission investment can unfold. Transmission planning arrangements include, among others, the planning process itself, the implementation and mechanisms for cost recovery, the role of markets (price signals) as a decision support, and trade-offs between transmission and nontransmission investment (Moselle and Brown 2007). Note that the supergrid planning process stretches well beyond conventional transmission planning procedures. Time horizons projected forward are much longer, and system changes must be considered. Moreover, the maximum span of demand and generation forecasts used in traditional national planning procedures is 7 to 10 years (e.g., in the United Kingdom), with the maximum of (less-detailed) forecasts of 20 years in Alberta (Moselle and Brown 2007). This is significantly shorter than the three- to four-decade horizon adopted by most supergrids. Clearly, the institutional framework for planning and developing the supergrid is not yet available in the United States and Europe. The United States is more advanced with respect to interregional transmission development. However, the Federal Energy Regulatory Commission (FERC) cannot impose the development of a supergrid on any of the regional transmission organizations (RTOs) or the individual independent system operators (ISOs). Neither can it, for the time being, consider RPS obligations as legitimate criteria for the development of new transmission (Krapels 2009). In Europe, there is still less ground on which the planning process for a supergrid could be based. Contrary to many restructured electricity systems, continental Europe lacks a regional transmission development plan (Moselle and Brown 2007). Transnational transmission expansion is only beginning to be coordinated at the European level. An emerging European regulatory agency,
the Agency for the Coordination of Energy Regulators (ACER), is set to negotiate planning ideas with its counterpart, the European Network of Transmission System Operators for Electricity (ENTSO-E).24 Given the lack of institutional support, it seems that without federal or European-wide planning procedures and some implementing power, the development of any supergrid is unlikely. Whether any such planning processes can reasonably be implemented is yet another open question.
Cost AllocationCost allocation and cost recovery are crucial in any transmission regime, but they become particularly difficult in the supergrid context, where more than one ISO or TSO is involved. A restructured market with open access requires some form of cost allocation mechanism for transmission expansion. Baldick et al. (2007) identify principles to follow: they propose using a standard measurement for societal benefits that incorporates economic values and having in place an open, transparent, and inclusive process for regionally based transmission planning and analysis. New transmission investment should be supported in federal or other wholesale rates, instead of in the retail rate base, and free entry should be permitted in transmission investment. There are two main approaches to allocate costs. First, those who clearly benefit from the investment are identified and consequently pay for it; note that the question of who benefits is not trivial either, particularly in a dynamic context with a changing network topology. Second, the cost of new transmission investment is socialized among all users in a region or market (Baldick et al. 2007). Another key issue of cost allocation is the ability of the institutional system to compensate parties who lose from transmission investment. Besides the question of cost allocation, other issues, such as consumer concerns about price development, can arise. Namely, parties protecting consumers in regions with low-cost generation may fear that transmission investment will cause generation-related prices to equalize over a larger geographic region and thus increase for a specific region.25
Vertical or horizontal coordination, or both, would make cost allocation for a supergrid even more complex. In the United States, the dual pricing regime, a structure of transmission cost recovery that is affected by both state and federal ratemaking practices, complicates the appropriate cost allocation. Currently, FERC, the federal regulator, must consider both the allocation of costs among different generators and load, and the reflection of those costs in retail rates. Obviously, this dual-pricing regime has a rather complicating effect. Europe has been slow to develop an interTSO compensation mechanism (Moselle 2008), which complicates any supergrid development. In addition, the atomization of European TSOs makes successful cooperation more difficult. The three Scandinavian system operators have developed a voluntary compensation mechanism (Nylund 2009). But even this relatively small jointly managed grid illustrates the weakness of voluntary approaches to inter-TSO cooperation in Europe and seems to suffer from the absence of legally binding arrangements. As the number of participating TSOs increases, transaction costs increase exponentially.
Market DesignBoth gradual transmission expansion and supergrids should deploy a market-oriented market design that is conducive to socially optional investment decisions. A critical element thereof is bid-based, security-constrained economic dispatch with locational marginal pricing (LMP), or nodal pricing, as proposed by Hogan (2008), to achieve welfare maximization and identify potential transmission bottlenecks. In many instances, LMP is the most cost-efficient solution to transmission problems because the prices identify the true scarcities in the system. Thus longer-term planning for a supergrid should incorporate this welfare-maximizing algorithm. In the United States, these principles are fulfilled in the more reform-oriented regions, most of which are also elaborating on supergrid proposals. Note that while LMPs are useful in providing locational signals for placing generation and load, their value for establishing transmission investments is more
doubtful; therefore, financial transmission rights (FTRs) based on LMP differences have not fully resolved the issue of transmission investment, as was initially hoped for. In Europe, no electricity system has a nodal pricing system in place, not even the United Kingdom, which applies static G-components, with generators compensated for relieving network congestion or penalized for contributing to it. Continental Europe is still largely dominated by uniform transmission pricing. Hence the conditions under which efficient supergrid investment could occur are not really in place. A critical issue in any intermittent-based system is the design of the balancing market and creating incentives for investment in backup capacity for intermittent generation from renewables. Most of the scenarios discussed early in this chapter have not gone the full way of installing storage capacity for the intermittent large-scale renewables (wind and solar), primarily because of cost. In this case, the generators themselves will need to analyze other technologies to cope with the intermittency of the renewables portfolio. Thanks to their high ramping flexibility, relatively low capital costs, widely available fuel, and ease of operation, gas turbines are likely to be the most economic backup technology.
logic of an investor but ignores larger economic benefits and costs. Calculating cost changes by producers and consumers as a result of the expansion of transmission lines; this corresponds to a more economic approach, as it approximates the welfare effects of investments by the change in producer and consumer surpluses. For example, PJM values the economic benefit of additional transmission investment by 0.7 PC + 0.3 LP, where PC is the change of production costs and LP is the change in load payments (Lin 2009). A comprehensive, sophisticated costbenefit analysis that also takes into account indirect effects of transmission expansion, such as increased system reliability or competition in one of the zones. For example, the California ISO (CAISO) uses a comprehensive transmission economic assessment methodology (TEAM) as a decision support tool for transmission planners, in which the annual benefits from an expansion include production cost benefits, competitivity benefits, operational benefits, generation investment cost savings, reduced losses, and emission benefits (California ISO 2004).26
CostBenefit AnalysesEven if a workable long-term planning process is in place, with cost recovery and price signals defined, an institutional design regarding the assessment of transmission expansion would be required. Long-term planning can define the big picture of generation and transmission expansion, but it cannot provide an assessment of whether an individual line, or a certain part of the network, should or should not be extended. For this, some form of costbenefit comparison might be useful. As a rule of thumb, three approaches can be applied:
All substantial public policy interventions of the type required for the supergrid projects discussed require justification, and a sound, transparent costbenefit analysis provides just that. Thus the CAISO-TEAM methodology might be developed further to become applicable to longer-term supergrid developments as well. This CBA framework could then be applied to the projects under discussion. This is particularly important for Europe, where the 10-year EU-wide plans are not yet systematically subject to costbenefit analysis. The normative aspects (yes or no) of such analysis have a strong positive effect, increasing transparency and accountability of the actors involved.
Traditional net present value (NPV) assessment of individual transmission investments, by relating (expected) private revenues to costs; this business perspective adheres to the
Regulatory OptionsWhile the optimal regulation of transmission expansion is already a controversial issue, this is even more the case for the regulatory framework
of any supergrid. The following issues merit consideration and await solutions:
Regulated versus merchant transmission investment: in general, the more meshed a transmission network, the more difficult it is to isolate the effects of a specific newly constructed line, and hence the more difficult it is to apply merchant investment. Krapels (2009) suggests that HVDC lines are able to attract private merchant investment because of the point-to-point character of these lines. The empirical underpinning related thereto is scarce, however: except for a few local transmission lines, there is little independent transmission expansion in the United States and Europe. It seems that the need for coordinated, long-term planning and inter-TSO coordination, information asymmetries between the incumbent network owner/ operator and the market entrant, and scale and scope effects are arguments in favor of regulated investment by the incumbent ISOs, RTOs, or TSOs. This does not exclude tendering of a large share of the activities that will be subject to market forces. Incentive versus cost-based regulation: opting for a regulatory approach necessitates determining the power of the incentive scheme, in other words the degree to which the network tariffs are fixed by the regulator, and thus does not correspond to some approximation of costs (cost-based regulation). Whereas incentive regulation has resulted in increased static allocative efficiency, its benefits in terms of dynamic investment efficiency are unclear. Although a general consensus exists that an incentivebased regulatory regime can theoretically induce an optimal level of transmission expansion under certain conditions, the realworld implementation of these mechanisms has not followed suit. The Hogan-RosellonVogelsang mechanism (Hogan et al. 2007) is an example of such a traditional, nonBayesian incentive scheme. However, it assumes constant demand and has difficulties accommodating intermittent renewable elec-
tricity sources. Finally, the danger of regulatory discretion and limited commitment, and the potential instability of the political and institutional framework, would suggest a reduction of the incentive power of the regulatory regime for a supergrid.
On the producer side, and even assuming that transmission can be treated as a cost block, allocating producer rent among the players depends on the behavioral assumptionsin other words, whether one assumes cooperative or noncooperative behaviorand the allocation of grandfathered rights. In addition, established incumbent electricity producers may hesitate to invest in low-cost capacity abroad that might undermine the price in their home markets as well (cannibalizing effect). On the consumer side, there will be clear winners and losers from the additional generation and transmission capacities. As the case study demonstrates, former locked-in low-price consumers tend to be adversely affected, while consumers in former highprice zones clearly benefit. Last but not least, the sharing of rents among different federal levels is a potential source of conflict as well. Welfare maximization over the entire supergrid region is, in fact, an oversimplification, because U.S. states and European nations are likely to influence decisions and seek to optimize only their own welfare. As in the planning process, this would speak in favor of centralization of decisionmaking with flexible side payments and compensation.
that do tend to prefer decentralized, regional network integration to supergrids. The case study in this chapter, the EEAMENA project, indicates that large-scale CSP generation in MENA countries and its HVDC transmission to Europe may increase welfare and even become profitable under certain conditions. However, a panEuropean perspective is required, and it is currently unclear whether the institutional arrangements necessary for the project, including adequate financing mechanisms, will be put in place in time. All supergrid projects also face several crosscutting issues: the necessity of efficient longterm planning procedures, the simplification of cost allocation issues, market-oriented pricing where possible, a comprehensive costbenefit analysis to identify economic solutions, and adequate regulatory incentives.
ConclusionsThe main message of this chapter is that harnessing renewable energy to generate electricity is relatively simple in comparison with transporting that electricity over long distances to large-scale demand centers. The idea of developing largescale supergrids, or transmission overlay grids, may be appealing, judging by the mushrooming number of pilot studies. However, large-scale harnessing of renewables may be limited by transmission bottlenecks that will be difficult to overcome in the current market and institutional environment:
Few of the reviewed case studies deal with transmission issues in a serious way, and those
Potential investors face a multitude of market, policy, and regulatory risks; technological uncertainties; coordination problems; and other barriers, such as local resistance. The current lack of business cases for network expansion can be overcome only if a clear, stable, and equitable longterm policy framework is in place. Welfare benefits of network expansion projects should be distributed in an incentive-compatible fashion. Hence an institutional analysis is required to determine opportunities and challenges in the development of such scenarios. Several issues that should be addressed in more detail include timing, irreversibility of investment, and risk considerations. This chapter has only touched on issues of the appropriate regulatory setting, the most critical being todays policy of favoring national subsidies over federal regulation. On both sides of the Atlantic, transmission planning competence and the ability to develop and install supergrids are limited. The most important issues to resolve are those linked to rent sharing. Thus future research should investigate the distributional effects of different scenarios on producer and consumer rents in each of the regions concerned, as well as ponder the proper compensa-
AcknowledgmentsThis chapter is based on the research program Electricity Markets at the Dresden University of Technology (TU Dresden). The author thanks Jonas Egerer and the study project 2050 for background research, and Florian Leuthold, Christina Beestermller, Johannes Herold, Robert Wand, and Hannes Weigt for comments and suggestions; thanks also to two referees for constructive comments.
Notes1. According to IEEE (1997), FACTS are defined as alternating current transmission systems incorporating power electronic-based and other static controllers to enhance controllability and increase power transfer capability. FACTS comprise a number of technologies, which are used to stabilize the frequency, control the real and reactive power flow, or compensate and manage the effective power flow. Improving the static stability of a multiline transmission system, FACTS can increase the power flow on existing AC lines. HVDC can be connected to AC networks via a converter station, using conventional linecommutated current source converters (CSCs), or self-commutated voltage source converters (VSCs). The latter are based on insulated gate bipolar transistors (IGBT) and can be adjusted with respect to load flows. These components also offer black start capacity. Whereas traditional HVDC was mainly limited to point-to-point operation,VSCs enable HVDC systems to work in more complex structures. This development offers the possibility to create entire HVDC transmission overlays as a supergrid. The Solar Grand Plan even stretches to the end of this century: by 2100, the L-RES could generate 100% of U.S. electricity and more than 90% of total U.S. energy (Zweibel et al. 2008). This backbone would reach major markets in Phoenix, Las Vegas, Los Angeles and San Diego to the west, and San Antonio, Dallas, Houston, New
Orleans, Birmingham (Ala.), Tampa (Fla.), and Atlanta to the east (Zweibel et al. 2008, 54). 5. For an average transmission distance of 1,500 miles, one converter per line, and a capacity utilization factor of 27%, Fthenakis et al. (2009) estimate a levelized transmission cost of $0.024/kWh on average. Investment and operation costs for transmission will be included in grid developers royalties. 6. For a U.S.-wide study on large-scale wind integration, see the transmission plan by American Electric Power (AEP 2007) at the request of the American Wind Energy Association, suggesting a 765 kV AC overlay grid. 7. The Midwest and the East Coast are connected by 765 kV AC lines and two 800 kV DC lines, with major reinforcements between Massachusetts and New Jersey. The two HVDC lines are fed by additional 400 and 800 kV DC lines. Moreover, twocircuit 500 kV AC lines build up the existing grid in Oklahoma and connect Arkansas and Alabama as well as Maryland and NewYork. A new 345 kV two-circuit line will be built in New York. 8. This is also reflected in the introduction of incentive-based rate treatments for transmission in interstate commerce by FERC as a measure to stimulate interstate transmission expansion (Energy Policy Act 2005, Section 1241, and amendment to the Federal Power Act, Section 219). 9. For more information on SUSPLAN (PLANning for SUStainability), see www.susplan.eu/; for the SOLID-DER project (Coordination Action to Consolidate RTD Activities for Large-Scale Integration of Renewable Energy into the European Electricity Market), see www.solid-der.org/; for WIND FORCE 12 by Greenpeace, see www.ewea.org/fileadmin/ewea_documents/ documents/publications/reports/wf122005.pdf; for Tradewind, see www.trade-wind.eu/; and for DENA 2, see www.dena.de/de/themen/themareg/projekte/projekt/netzstudie-ii. 10. The initial project is supposed to be connected to the United Kingdom, the Netherlands, and Germany. 11. The overall objective of the Desertec Foundation is to make productive use of the largest source of energy, solar, as a basis for electricity consumption and sustainable development around the globe, with a focus on desert countries. Besides the energy problem, the Desertec consortium also addresses issues of water supply, food security, and climate change.
12. Since the summer of 2009, the project has been accelerated by the Desertec Industrial Initiative (DII), a consortium of utilities, equipment producers, banks, and insurance companies. At the EU level, there is also activity in the form of the Union for the Mediterranean, which has developed the Mediterranean Solar Plan (MSP). The MSP is organized in three phases: building an institutional structure (ongoing); developing a Master Plan Study (until 2010); and implementing the plan until 2020. The following discussion refers to the most recent study at the time of this writing, Trieb et al. (2009). 13. Being highly site-dependent, this calculation is based on a solar direct normal irradiance (DNI) of 2,700 kWh/m/a and a CSP design for extensive heat storage (solar multiple of 4). 14. For technical calculations on the EEA-MENA project, we refer to an ongoing research project at the Chair of Energy Economics and Public Sector Management at Dresden University of Technology (see Egerer et al. 2009). 15. This includes the high-priority projects of European interest for inter-European electricity interconnection, and linkage with neighboring regions: according to the Trans-European Energy Projects (TEN-E) and Priority Interconnection Plan (PIP) (EC, 2006). 16. Compare Leuthold et al. (2008) and Egerer et al. (2009) for a detailed technical discussion. 17. For a detailed description, see Egerer et al. (2009, 32). 18. For the spatial configuration of the additional lines, see Egerer et al. (2009, 46). 19. The average electricity price (system price) is calculated from the models demand-weighted zonal prices. 20. Cost coverage is calculated by subtracting transmission costs from electricity prices in the export electricity market. 21. For a detailed overview of prices, transmission costs, and cost coverage for generation, see Egerer et al. (2009, 56). 22. In addition to zones with large wind capacity, zones with large hydropower capacity (e.g., Switzerland, Austria, and Serbia) are not served by CSP-generated electricity. Although these zones have seasonal storage capacity (reservoirs), for welfare purposes it seems preferable to substitute fossil-fueled base load capacity elsewhere. 23. These cost estimates are based on decreasing average investment cost for CSP from 5,300 ($7,220)
per kW in 2020 to 3,944 ($5,373) per kW in 2030 to 3,429 ($4,671) per kW in 2050 (Trieb et al. 2009, 83). 24. As a first step, Regulation 714/2009/EC (EU 2009b) requires that ENTSO-E adopt a nonbinding communitywide 10-year network development plan every 2 years, which is then assessed by the agency (ACER), consistent with the national 10-year development plans provided by the member states. 25. Beyond the underlying principles, a concrete cost allocation is the method of average participation (AP) (Prez-Arriaga and Olmos 2009). AP can track the actual upstream and downstream flows to the generators, and loads that can be associated plausibly with them. Theoretically, the cost of the used fraction of each line can be apportioned to generation and load in proportion to the aggregate economic benefits. 26. An essential component of the CAISO-TEAM is that it implements a market simulation model based on dynamic supply bids and incorporating a detailed physical transmission modeling capability for a reliability region. Besides, CAISO-TEAM includes uncertainty and risks about the future that can partly be quantified. Apart from these benefits, cross-sectoral positive externalities comprise simplified rights-of-way for the use of other network infrastructure being built, once the land is assigned to transmission purposes (such as optical fiber telecommunications), reflecting a positive externality of transmission expansion. Additionally, long-term resource cost advantages, synergies with other transmission projects, fiscal benefits from construction and taxes, and impacts on fuel markets should be taken into consideration according to Pfeifenberger and Newell (2007).
ReferencesAEP (American Electric Power). 2007. InterstateTransmission Vision for Wind Integration. www.aep.com/about/i765project/docs/ WindTransmissionVisionWhitePaper.pdf (accessed December 3, 2009). Airtricity. 2006. European Offshore Supergrid Proposal: Creating a More Powerful Europe. www.trecuk.org.uk/resources/airtricity_supergrid_V1.4.pdf (accessed December 2, 2009). AWEA/SEIA (American Wind Energy Association/ Solar Energy Industries Association). 2009. Green
Power Superhighways: Building a Path to Americas Clean Energy Future. www.awea.org/ GreenPowerSuperhighways.pdf (accessed December 3, 2009). Baldick, Ross, Ashley Brown, James Bushnell, Susan Tierney, and Terry Winter. 2007. A National Perspective on Allocating the Costs of New Transmission Investment: Practice and Principles. Blue Ribbon Panel on Cost Allocation. www.puc.nh.gov/Transmission%20 Commission/120108%20Progress%20Report/ Attachment%20M.pdf (accessed March 18, 2010). California ISO (California Independent System Operator). 2004. Transmission Economic Assessment Methodology (TEAM). Folsom, CA: California ISO. Claus, M., D. Retzmann, S. Srangr, and K. Uecker. 2008. Solutions for Smart and Super Grids with HVDC and FACTS. 17th Conference of the Electric Power Supply Industry. October 2008, Macau. www.ptd.siemens.de/CEPSI08_Art.pdf (accessed December 1, 2009). EC (European Commission). 2006. Decision No. 1364/2006/EC of the European Parliament and of the Council of 6 September 2006 Laying Down Guidelines for Trans-European Energy Networks and Repealing Decision 96/391/EC and Decision No. 1229/2003/EC. Official Journal of the European Union: L262/1-23. Egerer, Jonas, Lucas Bckers, Gregor Drondorf, Clemens Gerbaulet, Paul Hrnicke, Rdiger Surich, Claudia Schmidt, Simon Schumann, Sebastian Schwiedersky, Thorsten Spitzel, and Anja Thanheiser. 2009. Sustainable Energy Networks for Europe: The Integration of Large-Scale Renewable Energy Sources until 2050. www.tu-dresden.de/ wwbwleeg/publications/ wp_em_35_Egerer_et_al_2050.pdf (accessed March 1, 2010). ENTSO-E (European Network ofTransmission System Operators for Electricity). 2008. ENTSO-E GridMap. www.entsoe.eu/index.php?id=77 (accessed November 10, 2009). . 2009. UCTE Transmission Development Plan 2009. www.entsoe.eu/fileadmin/user_upload/ _library/publications/ce/otherreports/ tdp09_reporr_ucte.pdf (accessed November 10, 2009). EU (European Union). 2009a. Directive 2009/28/EC of the European Parliament and of the Council of 23 April 2009 on the Promotion of the Use of Energy from Renewable Sources and Amending and
Subsequently Repealing Directives 2001/77/EC and 2003/30/EC. Official Journal of the European Union L140/1662. . 2009b. Regulation No. 714/2009 of the European Parliament and of the Council of 13 July 2009, on Conditions for Access to the Network for Cross-Border Exchanges in Electricity and Repealing Regulation (EC) No 1228/2003. Official Journal of the European Union: L211/1535. Fthenakis, Vasili, James Mason, and Ken Zweibel. 2009. The Technical, Geographical, and Economic Feasibility for Solar Energy to Supply the Energy Needs of the US. Energy Policy 37 (2): 387399. Hogan, William. 2008. Electricity Market Design: Coordination, Pricing and Incentives. Presentation at the Toulouse Conference on Energy Economics. www.energypolicyblog.com/wp-content/uploads/ 2008/06/20080623_hogan.pdf (accessed December 8, 2009). Hogan, William, Juan Rosellon, and Ingo Vogelsang. 2007. Toward a Combined Merchant-Regulatory Mechanism for Electricity Transmission Expansion. Paper presented at the IAEE European Conference. April 2007, Florence, Italy. IEEE (Institute of Electrical and Electronics Engineers). 1997. Proposed Terms and Conditions for FACTS. IEEE Transactions on Power Delivery 12 (4): 1848 1853. Jacobsen, Mark Z., and Mark A. Delucchi. 2009. A Path to Sustainable Energy by 2030. Scientific American 299 (November): 5865. Kaupa, Heinz. 2009. Smart Grids and/or Super Grid? Presentation at the Alpbacher Technologiegesprche conference. August 2009, Vienna. www.bmvit.gv.at/ service/publikationen/innovation/downloads/ 3kaupa.pdf (accessed December 3, 2009). Krapels, Edward. 2009. Integrating 200,000 MWs of Renewable Energy in the US Power Grid. www.hks.harvard.edu/hepg (accessed November 3, 2009). Leuthold, Florian, Hannes Weigt, and Christian von Hirschhausen. 2008. ELMOD A Model of the European Electricity Market. Electricity Markets working papers WP-EM-00. www.tu-dresden.de/ wwbwleeg/publications/wp_em_00_ELMOD.pdf (accessed November 13, 2009). Lin, Jeremy. 2009. Market-BasedTransmission Planning Model in PJM Electricity Market. Energy Markets 2009. Paper presented at the EEM 2009 (6th Conference on European Electricity Markets). May 2009, Leuven, Belgium.
Midwest ISO, PJM, SPP, TVA, and MAPP. 2008. Joint Coordinated System Plan 2008: Economic Assessment. Report, Vol. 1. www.jcspstudy.org (accessed December 7, 2009). Moselle, Boaz. 2008. Reforming TSOs: Using the Third Package Legislation to Promote Efficiency and Accelerate Regional Integration in EU Wholesale Power Markets. Electricity Journal 21 (8): 917. Moselle, Boaz, and Toby Brown. 2007. International Review of Transmission Planning Arrangements. Brussels: Brattle Group. Natuur en Milieu (Netherlands Society for Nature and Environment). 2009. Office for Metropolitan Architecture Presents Master Plan Offshore Wind in the North Sea. Utrecht, Netherlands: Natuur en Milieu. Nylund, H. 2009. Sharing the Costs of Transmission Expansion: A Cooperative Game Theory Approach Applied on the Nordic Electricity Market. Proceedings of the IAEE 10th European Conference. September 2009, Vienna, Austria. OMA (Office for Metropolitan Architecture). 2009. Masterplan Zeekracht. www.zeekracht.nl/sites/ default/files/oma.pdf (accessed November 29, 2009). Prez-Arriaga, Ignacio, and Luis Olmos. 2009. A Comprehensive Approach for Computation and Implementation of Efficient Electricity Transmission Network Charges. Cambridge, MA: Center for Energy and Environmental Policy Research. Pfeifenberger, Johannes, and Sam Newell. 2007. Evaluating the Economic Benefits of Transmission Investments. Paper presented at EUCIs Cost-Effective Transmission Technology Conference. May 2007, Nashville, TN. Seligsohn, Deborah, Lutz Weischer, Shane Tomlinson, and Pelin Zorlu. 2009. Key Functions for a UNFCCC Technology Institutional Structure: Identifying Convergence in Country Submissions. Working paper. Washington, DC: World Resources Institute. www.wri.org/climate/cop-15 (accessed December 3, 2009). Siemens. 2009. Ultra HVDC Transmission System, Newsletter Issue 0910. www.trec-uk.org.uk/ resources/siemens_uhvdc_october_2009.pdf (accessed December 1, 2009). Sinden, Graham. 2007. Characteristics of the UK Wind Resource: Long-Term Patterns and Relationship to Electricity Demand. Energy Policy 35 (1): 112127.
Tierney, Susan. 2008. A 21st Century Interstate Electric Highway System: Connecting Consumers and Domestic Clean Power Supplies. Boston: Analysis Group. Trieb, Franz, Christoph Schillings, Stefan Kronshage, Uwe Klann, Peter Viebahn, Nadine May, Regina Wilde, Christian Paul, Malek Kabariti, Abdelaziz Bennouna, Hani El Nokraschy, Samir Hassan, Laila Georgy Yussef, Tewfik Hasni, Nasir El Bassam, and Honorat Satoguina. 2005. Concentrating Solar Power for the Mediterranean Region. Final Report. Stuttgart: German Aerospace Center (DLR) Institute of Technical Thermodynamics Section Systems Analysis and Technology Assessment. Trieb, Franz, Christoph Schillings, Stefan Kronshage, Peter Viebahn, Nadine May, Christian Paul, Malek Kabariti, Khaled M. Daoud, Abdelaziz Bennouna, Hani El Nokraschy, Samir Hassan, Laila Georgy Yussef, Tewfik Hasni, Nasir El Bassam, and Honorat Satoguina. 2006. Trans-Mediterranean Interconnection for Concentrating Solar Power. Final Report. German Aerospace Center (DLR) Institute of Technical Thermodynamics Section Systems Analysis and Technology Assessment, Stuttgart. www.dlr.de/tt/ Portaldata/41/Resources/dokumente/institut/ system/projects/TRANSCSP_Full_Report_Final.pdf (accessed December 1, 2009). Trieb, Franz, Marlene OSullivan, Thomas Pregger, Christoph Schillings, and Wolfram Krewitt. 2009. Characterisation of Solar Electricity Import Corridors from MENA to Europe. Stuttgart: REACCESS Report, German Aerospace Center (DLR) Institute of Technical Thermodynamics Section Systems Analysis and Technology Assessment.www.dlr.de/tt/ Portaldata/41/Resources/dokumente/institut/ system/publications/ Solar_imimpo_DLR_2009_07.pdf (accessed December 1, 2009). World Bank. 2008. Illustrative Investment Programs for the Clean Technology Fund. http://siteresources.worldbank.org/INTCC/ Resources/Illustrative_Investment_program_ May_15_2008.pdf (accessed November 26, 2009). Zweibel, Ken, James Mason, and Vasilis Fthenakis. 2008. Solar Grand Plan. Scientific American 298 (1): 4857.
Part IVNational Experiences
11Renewable Electricity Generation in the United StatesRichard Schmalensee
homas A. Edisons Pearl Street Station in New York, the first permanent, commercial electric generating plant, began operation on September 4, 1882 (IEEE 2008b). Just 26 days later, the first commercial generating plant using renewable energya hydroelectric facility began operation in Appleton, Wisconsin (IEEE 2008a). The United States has considerable hydroelectric potential and moved aggressively, particularly in the 1930s, to exploit it. By 1949, hydropower accounted for just under a third of U.S. electricity generation (EIA 2009b, Table 1.1). Since then, however, the relative importance of hydropower has waned, as potential dam sites were of lower quality than those already employed, the performance of other generating technologies improved, and the public became increasingly concerned about the environmental impacts of dams. In recent years, more attention has been given to the possible demolition of hydroelectric dams than to their possible construction. Hydropower accounted for only about 6% of U.S. electricity generation in 2007 (EIA 2009b, Table 1.1). Renewable generation technologies other than hydroelectricity, referred to in this chapter as nonhydro renewable (NHR) technologies, began to attract significant attention from public and private decisionmakers in the United States and
abroad after the energy crises of the 1970s. As environmental concerns, particularly those related to climate change, have become more important, support for these technologies has generally increased. In the United States the result has been a complicated saga of erratic and unfocused federal policy and widely divergent state policies, with results that have not surprisingly varied considerably over time and among the states. The chapter begins with a brief quantitative overview of the actual and potential importance of nonhydro renewable energy over time in the United States, until recently a leader in NHR generation of electricity. Lately, other nations have provided more effective support of these technologies and accordingly have taken the lead in using them. Next, the chapter outlines rationales and policy tools for supporting NHRs and examines policy at the federal level in the United States. It then considers state-level policies and their effects, with brief discussions of experiences in two major states that have played very different leadership roles in this area: California and Texas. This is followed by a look at the most rapidly growing NHR technology in the United Stateswindand some of the issues and concerns its growth has raised.
Richard Schmalensee
contribution from what is termed other solar the use of solar energy to produce heat, mainly to warm swimming poolsthese three nonelectric uses of renewable energy are much more important than the use of NHRs to generate electricity. Since the late 1970s, NHRs have been of interest to policymakers primarily because of their perceived potential to displace fossil fuels (and in some jurisdictions, nuclear energy) in electricity generation. Despite this interest, however, and a wide variety of policies aimed at encouraging the use of NHRs, these technologies have accounted for only 2% to 2.5% of total U.S. electricity generation since 1989, as Figure 11.2 shows. For all of the 1990s, NHRs played a more important role in generating electricity in the United States than in Europe. But major European nations, particularly Germany, were much more aggressive in promoting NHRs over most of this period, and the share of these technologies in European electricity generation has accordingly been rising. It is now almost double that in the United States. Figure 11.3 shows the contributions of the various NHR technologies to electricity generation since 1990. About 70% of biomass generation
Figure 11.1. Nonhydro renewable energy consumption as a percentage of total U.S. energy consumption, 19782008
19 7 19 8 7 19 9 8 19 0 8 19 1 8 19 2 8 19 3 8 19 4 8 19 5 8 19 6 8 19 7 8 19 8 8 19 9 9 19 0 9 19 1 9 19 2 9 19 3 9 19 4 9 19 5 9 19 6 9 19 7 9 19 8 9 20 9 0 20 0 0 20 1 0 20 2 0 20 3 0 20 4 0 20 5 0 20 6 0 20 7 08
YearGeothermal (electric) Biofuels Wind (electric) Other biomass Biomass (electric) Solar and other
YearUnited States Europe
YearGeothermal Solar thermal & photovoltaic Wood & wood derived Wind Other biomass
Figure 11.3. U.S. electricity generation from nonhydro renewable energy, 19902007
is fueled by wood and wood waste; the remainder is fueled by biogenic municipal solid waste, landfill gas, and a variety of other substances. Between 1990 and 2007, geothermal generation declined slightly, and biomass-fueled generation grew at only a 1.1% average annual rate. In these data, solar generation grew at an average annual rate of 3%, but from a tiny base. Because the U.S. Energy Information Agency (EIA) tracks genera-
tion only from solar installations with capacities above 1 megawatt (MW), it seems likely that solar generation at the end of this period was understated by at least 60%.1 Even correcting for this bias, however, solars share remains tiny. Wind, which grew at an average annual rate of 15.9%, accounted for the bulk of NHR growth over this period.
Table 11.1. Actual and potential NHR generation in the United StatesEstimated total realizable potential by 2020 (TWh) 501.6 300.4 85.2 36.0 2.3 925.5 Actual 2007 gross generation (TWh) 58.6 32.3 0.7 16.9 0.0 108.4 Actual as a percentage of potential 11.7 10.8 0.8 47.0 0.0 11.7
Sources: Estimated potentials from IEA 2008a, 65; generation from IEA 2008c, 396 Note: TWh = terawatt-hours
Many analysts contend that even with current technology, nonhydro renewables have the potential to play a much larger role in the United States than they do at present. Table 11.1 compares actual generation from nonhydro renewables in the United States in 2007 with estimates by the International Energy Agency (IEA) of total realizable potential by 2020.2 These estimates are intended to reflect both natural endowments (e.g., average solar radiation) and the relative costs of current NHR technologies, but they are inevitably imprecise and should accordingly be treated with caution. It is worth noting, however, that even though the IEA believes that solar generation has not even reached 1% of its potential, despite decades of attention by policymakers, the agency also estimates that its ultimate potential is much less than either biomass or wind.Table 11.2. Leading NHR generation states2007 NHR generation State Maine California Vermont Minnesota Hawaii Iowa Idaho Texas FloridaSource: EIA 2009c
There has been a great deal of variation in state-level experience with NHRs. In 2007, NHRs accounted for 2.53% of total U.S. net generation, but more than 5% in seven states and less than 1% in eleven states. This variation reflects differences in both the potential for various renewable technologies and state-level policies toward renewables. To shed quantitative light on the relative importance of these two sources of variation would require plausible estimates of state-level, technology-specific potentials comparable to the IEA estimates in Table 11.1, but no such estimates appear to exist.3 Table 11.2 provides information on the seven states for which NHR generation accounted for more than 5% of total generation, as well as the two states not in this set that were in the top five in terms of total NHR generation. These nine
Percent of state total 26.1 11.8 8.0 7.2 6.6 5.8 5.7 2.5 1.9
TWh 4.21 24.85 0.65 3.93 0.75 2.91 0.65 10.29 4.30
Main NHR technology or technologies Wood/wood waste Geothermal Wood/wood waste Wind Wind, geothermal Wind Wood/wood waste Wind Wood/wood waste, other biomass
states accounted for 92% of U.S. NHR generation in 2007. For most states, one technology is the dominant contributor to NHR generation, but two are nearly tied in both Hawaii and Florida. The importance of wood and wood waste is clear here, as it was in Figure 11.3, particularly in heavily forested states like Maine, Vermont, and Idaho. The so-called wind belt, extending northward from Texas to the Canadian border, is visible here, though some states in that belt are conspicuous by their absence. (The wind belt is discussed below in the section on Wind Power in the United States.) It is also interesting to note the unimportance of solar power, even in states with abundant solar resources such as California, Hawaii, and Texas.
time; energy security is less important now than earlier, and environmental concerns, particularly those associated with global climate change, have become more important in recent years.
Millions of 2007$
time in both relative and absolute terms, as Figure 11.4 shows. R&D for NHRs, and for most other energy technologies, peaked in 1980, fueled by intense concerns over energy security and rapidly rising oil prices. As oil prices receded, so did energy-related R&D. Since the early 1980s, R&D funding in support of NHRs has been both modest and variable from year to yearhardly conducive to long-term, sustained efforts aimed at major breakthroughs.
provide strong incentives for minimizing costs and maximizing production. Feed-in tariffs, however, generally do not provide stronger incentives for generating electricity when it is more valuable (e.g., by scheduling maintenance accordingly), and they provide an invisible subsidy by shifting all risk related to the supply of and demand for electricity to other market participants. An output subsidy, paid on top of market price, can eliminate both of these shortcomings while retaining the other good incentive properties of a feed-in tariff. Output subsidies are not widely employed, however, and like feed-in tariffs, they can provide incentives to operate NHR facilities even when the marginal value of their generation is negative (e.g., see the discussion of Texas in the section on State Policies and Experience). Investment subsidies are not particularly attractive economically, because they provide weaker incentives for reducing initial cost than do feed-in tariffs or output subsidies. Nonetheless, governments in the United States and abroad that promote deployment of NHRs almost all use investment subsidies as part of their policy packages. Finally, output quotas, known in the United States as renewable portfolio standards (RPSs), typically require load-serving entities to generate or procure a minimum fraction of energy from NHRs. This approach is not as popular as feed-in tariffs abroad, but it is very popular at the state level in the United States and is part of legislation being actively debated at the federal level.6 Not only has the United States adopted a different mix of policies than most other wealthy nations, but it also has implemented those policies in ways that significantly limit their efficiency and effectiveness.
city markets. PURPA required utilities to purchase electricity generated from certain defined qualified facilities at the utilities avoided costs. Qualified facilities could be either cogeneration facilities, which produced both useful heat and electricity, or certain small NHR generators. Because electric utilities at that time were almost all vertically integrated, avoided costs were to be determined by state regulators rather than market prices, and regulators in some states (notably California, as discussed later in the section on State Policies and Experience) responded by establishing feed-in tariffs that were based on the expectation of high and increasing generation costs. As costs of conventional generation in fact came down, this system became unsupportable, and it was largely dismantled by the early 1990s (Borenstein and Bushnell 2000, 48). Since then, federal policy has promoted NHR deployment primarily through favorable corporate income tax provisions: accelerated depreciation and tax credits for production and investment. After 1986, most NHR generating assets, which had been depreciated over 15 years for tax purposes, could be written off over 5 years. (The list of eligible NHR technologies was expanded in 2005 and 2008.) This increased the present value of tax deductions for depreciation by around half.7 The Renewable Electricity Production Tax Credit (REPTC) was first established by the Energy Policy Act of 1992.8 It provided for a corporate income tax credit of 2.1 cents per kilowatthour (kWh; 1.5 cents/kWh in 1993$, indexed for inflation) for generation using some technologies and half that for others for (generally) the first 10 years of operation. Favored NHR technologies are currently wind, closed-loop biomass, and geothermal; other eligible technologies include open-loop biomass, landfill gas, municipal solid waste, and certain hydroelectric, marine, and hydrokinetic facilities. The legislation establishing the REPTC also established a Renewable Energy Production Incentive (REPI) program, which authorized payments roughly equivalent to the production tax credit to entities such as state and local governments that were not corporate income tax payers.
This output subsidy policy has not been consistently or predictably implemented over time. Payments actually made under the REPI must be appropriated annually and thus are far from certain. Solar facilities were eligible for the REPTC only brieflyif they began operation in 2005. The REPTC expired at the end of 2001 and was then extended in March 2002. It then expired at the end of 2003 and was not renewed until October 2004, in legislation that extended it until the end of 2005. Legislation passed in 2005 extended it through the end of 2007, legislation passed in 2006 extended it through 2008, and laws passed in 2008 and 2009 revised and extended it through 2012 for wind and 2013 for other technologies. Figure 11.5 shows a surge in installation of wind capacity during 2001 before the REPTC expired, followed by a drastic drop-off during 2002, reflecting the uncertain status of the REPTC until March and the lag between project initiation and completion. Similarly, the unavailability of the REPTC during 2004 shows clearly in the figure. If investors cannot rely on a subsidys remaining in place, that subsidy provides at most weak incentives for long-term investments in such things as technology development and efficient production capacity. The Energy Tax Act of 1978, which was passed along with PURPA, established investment tax credits for a variety of NHR technologies. These were modified several times in the ensuing years (EIA 1999). Since 2005, the Residential Renewable Energy Tax Credit (RRETC) has provided personal income tax credits for up to 30% of investments in solar electric systems, solar hot water systems, wind turbines, fuel cells, and geothermal heat pumps. Also since 2005, the Business Energy Investment Tax Credit (BEITC) has provided a 30% corporate income tax credit for investment in essentially all solar systems except those used to heat swimming pools, as well as for fuel cells and small wind turbines. It provides a 10% investment tax credit for certain other technologies. Both of these provisions were initially scheduled to expire at the end of 2008, but legislation that year extended them to 2016. For solar systems, the initial investment accounts for most of the life cycle cost, so a 30%
YearSource: EIA 2009b, Table EIA-860
investment tax credit is a very large subsidy indeed. The Interstate Renewable Energy Council (Sherwood 2009) reports that the annual growth rate of photovoltaic capacity installed doubled in 2006, and capacity installed in 2008 was triple that in 2005. Similar dramatic growth occurred in solar hot water and space heating. It is important to note, however, that an investment tax credit is most valuable when it is less than current taxable income. This condition is probably satisfied for most homeowners who can seriously consider installing a solar system, but it is unlikely to be satisfied for any corporation specializing in grid-scale solar power. The need for such firms to use joint ventures and other devices to ensure that the full value of the investment tax credit is received can add significant friction to the process of financing solar projects. The REPTC and BEITC have been the most important sources of support for renewables deployment. In FY2007, the reductions in tax revenue caused by subsidized financing of renewables facilities under other programs was $100 million, compared with $690 million in such tax expenditures for the REPTC and BEITC. But programs supporting fossil fuels were considerably more costly, resulting in $2.7 billion in tax revenue reductions (GAO 2007, 37).
The American Recovery and Reinvestment Act of 2009, generally known as the stimulus bill, allows taxpayers eligible for the BEITC or the REPTC for facilities entering service or, generally, beginning construction in 2009 or 2010 to elect to receive a BEITC-equivalent cash grant instead. The rationale is that tax credits are of limited value during a period of unusually low corporate profits; but on the other hand, entitlement to a grant is of no value if Congress does not appropriate sufficient funds. Several relatively small federal grant and loan guarantee programs also exist, each targeted at certain classes of entities (e.g., municipal governments) and technologies. These programs are modified from time to time, and the actual funding available is determined each year by the appropriations process. In FY2006, excluding energy efficiency, the federal government made only $16.7 million in grants to support renewables and guaranteed just $23.8 million in loans (GAO 2007, 50). As with R&D, it is interesting to compare federal subsidies for the use of NHRs with subsidies for other generation technologies. Table 11.3 shows EIA estimates of total subsidies and support by technology for 2007, both in absolute dollar terms and per megawatt-hour (MWh) of genera-
Fuel/end use Coal & refined coal Natural gas & oil Nuclear Hydroelectric Solar Wind Other NHRa
Source: EIA 2008a, xvi a Includes biomass (and biofuels), landfill gas, municipal solid waste, and geothermal
tion. In absolute terms, coal (especially clean coal) and nuclear power were the most heavily subsidized, while per unit of output, solar and wind were by far the biggest winners. It is interesting to note that wind, in particular, received more than 12 times as much support as other NHR, even though those technologies accounted for more than twice as much generation.
States with RPSs accounted for 62% of U.S. net electricity generation in 2007. Table 11.4 lists these states in order of their initial RPS adoption and gives some of their most important current features. Five additional states have voluntary goals for renewable energy; these are listed in Table 11.5. While all RPS programs have the common goal of increasing the share of renewable resources, they differ considerably along multiple dimensions. One important difference is how compliance is to be achieved. In states with organized wholesale markets, entities that distribute power are generally responsible for meeting RPS targets and are given considerable freedom to choose how to do so. In states that have regulated, vertically integrated utilities, regulators oversee contracting and utility procurement. In New York and Illinois, a state agency has direct responsibility for the procurement of renewables under the RPS (Wiser and Barbose 2008). Some states have legislated explicit per-MWh penalties for noncompliance, whereas others allow the states public utility commission to determine the appropriate penalty. Differences also exist in the definition of resources that are deemed renewable and whether the RPS is applicable solely to investor-owned utilities or is extended to smaller retail suppliers with a lower target, as is the case in Colorado and Oregon. In some instances, the size of a facility is an important determinant of whether its output counts toward RPS requirements. Maine, for instance, requires facilities to be 100 MW or
Current target 105 MW 6% by 2005, 20% by 2015 15% by 2020 (Class 1) and 1% each year thereafter, 3.5% of sales each year starting in 2009 (Class 2)a 27% by 2020 22.5% by 2021 2,280 MW by 2007, 5,880 MW by 2015 30% by 2000 (Class 2), 10% by 2017 (Class 1)c 20% by 2020 10% by 2015 20% by 2010 20% by 2022 (Tier 1), 2.5% 20062018 (Tier 2)d 16% by 2020 24% by 2013 20% by 2020 18% by 2020 (8% Tier 1, 10% Tier 2)e 20% by 2020 5% in 2008, 10% in 2010, 15% in 2015 20% by 2019 15% by 2025 15% by 2020 25% by 2025, Xcel Energy 30% by 2020 23.8% by 2025 25% by 2025 25% by 2025 12.5% by 2020 20% by 2020 10% by 2015 15% by 2021 12.5% by 2025 20% by 2020
Other requirements
Massachusetts Connecticut New Jersey Texas Maine Hawaii Wisconsin California Maryland Rhode Island New York Colorado Pennsylvania DC Montana Delaware Arizona Washington Minnesota New Hampshire Oregon Illinois North Carolina New Mexico Michigan Missouri Ohio Kansas
Nov. 1997 July 1998 Jan. 1999 Sept. 1999 1999 2001 Oct. 2001 2002 May 2004 June 2004 Sept. 2004 Nov. 2004 Nov. 2004 April 2005 April 2005 July 2005 Nov. 2006 Nov. 2006 Feb. 2007 May 2007 June 2007 August 2007 Aug. 2007 Aug. 2007 Oct. 2008 Nov. 2008 Jan. 2009 May 2009
To be determined 20% Class 1, 3% Class 2, 4% Class 3b 2.12% solar by 2021 500 MW from sources other than wind
2% solar by 2022 Not specific 4% of RPSs to be solar in each year 0.5% solar by 2020 0.4% from solar by 2020 2.005% photovoltaic by 2019 30% of RPSs from distributed renewables after 2012 Xcel Energy 25% of RPSs from wind in each year 0.3% solar, 6.5% existing biomass, 1% existing small hydro Varies by utility 75% from wind 0.2% solar & thermal by 2018, 0.2% swine waste by 2018, 900,000 MWh from poultry waste by 2014 4% solar, 4% wind, 2% geothermal & biomass, 0.6% distributed renewables Varies by utility 0.3% solar by 2021 0.5% solar by 2025
Source: DSIRE 2009a Class 1: (in-state, on-site) solar, wind, ocean thermal, wave and tidal, fuel cells, landfill gas, qualifying hydroelectric, qualifying biomass, geothermal; Class 2: (in-state, on-site) systems dating prior to December 1997 using the same technologies as Class 1b a
Class 1: solar, wind, fuel cell, landfill gas, small hydroelectric, wave, tidal and ocean thermal, sustainable biomass; Class 2: trash to energy, biomass not included in Class 1; Class 3: customer-sited cooling-heating-power systems, recent savings from conservation and load management, recycled energy from heat pipes
c Class 1: RPS mandate to provide 30% of sales through renewables; Class 2: portfolio goal to increase new renewable capacity by 10% by 2017 d
Tier 1: solar, wind, qualifying biomass, landfill gas, geothermal, wave, tidal and ocean thermal, small hydroelectric, fuel cells; Tier 2: trash to energy, hydroelectric other than pump storage
e Tier 1: new and existing solar, wind, small hydro, geothermal, biomass, fuel cells, qualifying gas; Tier 2: new and existing waste coal, large hydro, waste to energy, distributed generation, demand-side management, certain biomass
Date goal adopted August 2007 February 2008 March 2008 March 2008 July 2009
Current goal 10% by 2015 10% by 2015 20% by 2017 20% by 2025 15% by 2025
smaller. In other states, the age of a facility determines whether it is considered eligible. For example, in Massachusetts, only capacity installed after 1997 is considered eligible. Hydroelectric facilities are generally eligible subject to the capacity constraint that each state sets on facilities. Some states, such as New Hampshire, are explicit in their consideration of small hydroelectric facilities. A number of states have tiers or set-asides, often with different time frames or target levels. Fifteen states and the District of Columbia have provisions favoring solar power or distributed generation; nine have minimum solar requirements of various sorts; and the others give extra credit for solar or distributed generation. Illinois, on the other hand, requires that 75% of renewable generation come from wind. Nine of the RPS jurisdictions give at least some credit for solar hot water systems as displacing nonrenewable generation, and Hawaii, Nevada, and North Carolina have provisions that allow demand-side efficiency to be used to meet a part of the RPS requirements (Wiser and Barbose 2008). The most common mechanism for demonstrating compliance with RPSs is the purchase of renewable energy certificates (RECs) (Corey and Swezey 2007). Renewable generators sell power at the market price and then also sell, in effect, a 1 MWh REC for each MWh of electricity they have sold. Distribution utilities and others obliged to obtain a minimum percentage of their electricity from NHRs demonstrate compliance by purchasing an appropriate number of RECs and surrendering these to the authorities. The ability to trade RECs ensures that costs are minimized within the state, as there are economic incentives to, in effect, produce the certificates using the
cheapest available NHR technology. (This regime does not, however, create any incentive to favor technologies with large spillover benefits.) Because the potential for NHR generation differs widely among states, even in the absence of a nationwide RPS, interstate trading of RECs would potentially be an important way of reducing the cost of meeting the states goals. But unfortunately, state RPS programs differ in so many dimensionsincluding the precise definition of an RECthat interstate trading is virtually impossible. Indeed, some state RPS programs prohibit it altogether. At the federal level, in 2005, the Senate passed a bill containing a national RPS that would have required 10% of electricity in the country to be generated by renewables by 2020, but the bill died in the House. In 2007, the House passed legislation containing a national RPS of 15% by 2020; this bill died in the Senate. Most recently, the American Clean Energy and Security Act of 2009, or WaxmanMarkey bill, passed by the House in June 2009, contains a national RPS with nationally tradable RECs. The bills standard, which could be met with a combination of energy efficiency savings and NHR generation, would start at 6% in 2012 and rise to 20% by 2020. As of this writing, the fate of this provision is yet to be determined. A majority of state RPS programs have only recently become operational10 of them are less than three years old, and 19 are less than five years old. Furthermore, RPSs are just one of the many state-level policies that have been adopted to promote renewable energy. As a consequence, it is difficult to make confident statements about the effectiveness of RPSs in increasing NHR genera-
tion, let alone assess their costs. A significant impact is suggested by the fact that in 2007, states with RPS programs accounted for an overwhelming 86% of new NHR generating capacity, as compared with just 22% of all new generating capacity (EIA 2009b, Data Table EIA-860). In a multivariate statistical analysis, Menz and Vachon (2006) find that RPS programs effectively encourage cumulative renewable energy investment and capacity deployment. However, in a later analysis using additional control variables, including the environmental orientation of each states legislators and the size of each states agencies concerned with natural resources, Carley (2009) finds that the adoption of RPS mandates does not effectively increase the share of renewable electric generation. There are at least reasons to be concerned that some of these programs may fail to meet their goals.
Type of incentive Personal tax: credits or other Corporate tax: credits or other Sales tax: exemption or deduction Property tax: exemption or special assessment Rebates programs Grant programs Subsidized bond or loan programs Production incentives Public benefit funds Net metering
Source: DSIRE 2009a Note: The District of Columbia is counted as a state in this table
The IEA lists the three most important state policies promoting renewables as RPSs, public benefit funds, and tax incentives (IEA 2008a). Public benefit funds are generally financed by a small surcharge on retail electric rates and are used to support renewable energy in a wide variety of ways. They are projected to total $7.3 billion by 2017 (DSIRE 2009a). All states except Arkansas offer some subsidy for investment in NHR generation, but the design and impact of tax benefits, rebates, grant, and subsidized bond or loan programs vary enormously. Beginning with Massachusetts and Wisconsin in 1982, 42 states and the District of Columbia have established net metering policies, and the Energy Policy Act of 2005 requires all utilities to provide net metering to customers that request it. Net metering allows utility customers with some NHR generators, generally only small residential or commercial installations, to sell electricity to the distribution entity that serves them at the retail rate the customer pays for electricity, not the typically much lower wholesale rate the distribution entity pays for other power. In Massachusetts in 2007, for instance, retail rates averaged $0.152 per kWh, while the average wholesale price in the New England market was only $0.068 per kWh (EIA n.d.b; FERC 2009). A small part of this difference reflects power losses in transmission and distribution, but these losses average only about 7% of net generation in the United States (see, e.g., EIA 2009b). Most of the wholesaleretail difference arises simply because regulated prices do not reflect incremental costs: retail rates are generally set to recover the fixed costs of distribution systems through a per-kWh charge added to wholesale electricity rates rather than through fixed charges of one sort or another. Although net metering programs are popular in state capitals, they are not yet widely used. Only 48,280 utility customers participated in net metering programs in 2007; 95% were residential, and 72% were in California (which established its program in 1995). But participation did grow at a 46% annual rate over the 20042007 period (EIA 2007, 2008b). Ten U.S. statesConnecticut, Delaware, Maine, Maryland, Massachusetts, New Hamp-
shire, New Jersey, New York, Rhode Island, and Vermonthave recently signed the Regional Greenhouse Gas Initiative (RGGI) agreement.10 The agreement obliges these states to cap their total CO2 emissions from the electric power sector through 2015, and then reduce them by 10% by 2018. Beginning at the start of 2009, electric utilities in these states have had to obtain and surrender allowances equal to their CO2 emissions. These allowances are mainly auctioned by the governments of the 10 states involved. In principle, this system caps utility CO2 emissions in the affected region, but allowance prices have so far been quite low: $2 to $3 per ton of CO2, a fraction of prices in the European Unions Emissions Trading System for CO2. It thus seems unlikely that this system has so far had much effect on the deployment of nonhydro renewables. Finally, it is interesting to note the relative unpopularity of production incentives such as feed-in tariffs and output subsidies in the United States. Perhaps the most important in this country is the California feed-in tariff discussed below, but it is available only to small generators.
40.0% 35.0% 30.0% Percentage 25.0% 20.0% 15.0% 10.0% 5.0% 0.0%90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 20 19 19 19 19 19 19 19 19 19 19 20 20 20 20 20 20
YearShare of nonhydro renewables in all California generation California's share of all U.S. nonhydro renewable generationSource: EIA 2009b, Data Table EIA-906
YearGeothermal Solar thermal & photovoltaicSource: EIA 2009b, Data Table EIA-906
for power generation in 1985, qualifying facilities that had already contracted with the CPUC were permitted to sell power at high rates. By 1986, California had nearly 90% of global wind generating capacity (DOE 2008, 6). Figure 11.7 shows that the bulk of the resulting NHR generation was powered by geothermal energy and biomass. It also shows that a number of these facilities shut down when their PURPA contracts expired between 1993 and 1995, though most continued to operate. In the ensuing years, California adopted a wide variety of policies to promote solar and other NHR generation, relying more heavily on subsidies of various sorts than on regulation or mandates, but Figures 11.6 and 11.7 show that these policies did not produce significant increases in the NHR share of total generation or even the absolute amount of NHR generation.11 In 1995, for instance, the CPUC proposed an RPS regime to increase renewable generation, but the following year, the legislature adopted a productionbased auction funded by surcharges on electric utility bills instead (Wiser et al. 1996). This program ran from 1998 to 2001, provided between $540 million and $640 million in funding each year, and supported 4,400 MW of existing and
1,600 MW of new renewable capacity (Ritschel and Smestad 2003; Wiser et al. 1996).12 Between 1998 and 2007, Californias Emerging Renewables Program, funded by the ratepayers of Californias four largest investor-owned utilities, has funded roughly 130 MW of capacity additions from smaller wind, solar, and fuel cell facilities (CEC 2009). The Self Generation Incentive Program (SGIP), funded at roughly $83 million each year, supports customer-generated renewable energy via wind, solar, and fuel cell sources. Between 2001 and 2007, the SGIP funded over 300 MW of total capacity additions.13 In 2006, California adopted the California Solar Initiative (CSI), a 10-year $3.2 billion program to fund the development of 3,000 MW of solar capacity (DSIRE 2009c). Part of the CSI is the New Solar Homes Partnership, a 10-year $400 million incentive-based program aimed at constructing homes with solar energy systems with a focus on energy efficiency (DSIRE 2009d). Beyond the big-ticket subsidy programs described above, the state of California runs a slew of smaller, more localized funding programs for renewable energy development. These include 5
YearWind Biomass
Source: EIA 2009b, Data Table EIA-906 Note: Solar-generated electricity accounted for up to 385,000 kWh of generation in 1990, but that number decreased to zero in 2001
utility-specific grant programs, 11 utility-specific loan programs, and 94 utility-specific rebate programs and other smaller financial incentives. And solar systems are exempted from local property taxes. Following eight earlier adopters, California enacted an RPS in 2002. The California RPS requires electric utilities to acquire 20% of their electricity from renewable sources by 2010. More recently, the state has adopted a nonbinding goal of 33% by 2020. Of the 7,000 MW of contracts for renewable generation signed between 2002 and 2007, 53% is wind, 23% solar, 12% geothermal, 7% biomass, and less than 1% small hydro and ocean energy (Wiser and Barbose 2008). It should be clear from the foregoing, however, that the RPS is but one in a large set of policies aimed at promoting NHR generation in California. In 2008, California established a feed-in tariff for small renewable energy facilities with capacities of 1.5 MW or less. It has the efficiencyenhancing feature that rates are to vary by time of day. Facilities that sell power under this tariff are not eligible for additional state incentives and programs (DSIRE 2009b).
Observers who have analyzed Texass RPS attribute its strong impact to several factors. At least initially, the system was technologyneutral,14 so the most economical resource wind in this casecould be used most intensively. As discussed below in the section on Wind Power in the United States, Texas, along with a few other states, is blessed with excellent wind resources. This is particularly true of western Texas, where wind speeds average 8 meters per second yearround, and wind power facilities can operate at capacity factors of 40% or more (Langniss and Wiser 2003), at the upper end of the 25%40% range commonly cited for wind generators (see, e.g., AWEA 2009). The remote location of these sites and the lack of transmission infrastructure initially inhibited exploitation of their potential. Subsequent legislation required utilities to upgrade their transmission systems as necessary to meet the states RPS goals and allowed them to recover the costs in retail rates (DSIRE 2009a; Langniss and Wiser 2003). As discussed further below, however, transmission from these sites nonetheless remains a problem.15 It is also important that the Texas RPS program applies to most of the states retail electricity load in a way that permits these remote sites to be fully exploited. While most RPS programs set generation requirements,Texass RPS sets requirements for capacity and thus for capacity additions year by year. Owners of renewable generating capacity can sell both electricity and renewable energy credits to any electricity retailer in the Texas market, and retailers can freely buy, sell, and bank these credits. A capacity conversion factor, now based on performance of renewable generating units in the two prior years, is used to convert capacity requirements into MWh requirements, which are allocated to retailers based on their shares of statewide electricity sales. The relatively large, organized, competitive wholesale power market in Texas (Potomac Economics 2009) has enabled an effective market for renewable energy credits.16 In addition, the structure of the Texas RPS enables renewable energy suppliers to sign longterm (10- to 25-year) contracts, which reduces their risk and makes it easier to raise capital, thus
lowering costs. Because the law sets annual requirements, each retailer can forecast how many credits it will need in the future and can thus confidently sign long-term contracts with renewables suppliers.17 Finally, the Texas RPS legislation provides strict, automatic penalties for noncompliance, while providing flexibility to electricity retailers through the ability to trade and bank renewable energy credits (Langniss and Wiser 2003). The California program, in contrast, seems to rely on the general enforcement powers and discretion of the Public Utility Commission to deal with any instance of noncompliance.
Sources: Estimated potentials are from AWEA n.d. and, for Washington and Oregon, personal communication with AWEA. Generation data are from EIA 2009c, and RPS dates are from Table 11.4
states listed in the table accounted for 30% of all U.S. electricity generation in 2007 and 92% of wind-powered generation. The table also shows when the states that have RPSs initially enacted them. Table 11.7 reveals a number of interesting patterns. First, all these states look very different from the rest of the country in terms of their potential for and use of wind generation. Second, Texas and California stand out in terms of total wind generation, together accounting for just over a third of the U.S. total. Third, there is enormous variation in the extent to which the potential for wind generation is exploited, with the three Pacific Coast states generating much larger percentages of their estimated potentials than any of the Wind Belt states. In the case of California, this is clearly attributable to very generous early subsidies for renewable generation. Oregon and Washington are also well known as green states, but they were not early adopters of RPSs, nor do they have sub-
sidy programs with California-like levels of generosity. Part of the reason for their relatively intensive exploitation of their wind potential may be that California utilities have taken advantage of their ability to meet their RPS obligations by purchasing NHR power generated elsewhere. Within this group of states, the share of total generation accounted for by wind also shows substantial variation. Here, though, neither Texas nor the Pacific states are outliers. On the high side, Iowa and Minnesota are the most reliant on wind generation. Iowa was an early RPS adopter, but Minnesota was not. On the low side, both in terms of reliance on wind generation and exploitation of wind potential, Nebraska, Wyoming, Montana, and North Dakota stand out. Among these four, only Montana had an RPS in 2007. Perhaps more important, all are relatively small in terms of population and total electricity generation, and all are distant from major population centers, so that the wind integration problems dis-
cussed below are unusually difficult. More generally, the Wind Belt suffers from the fact that only 7% of the U.S. population lives in the top 10 states for wind potential (NERC 2009a). As mentioned earlier, the rapid growth and significant penetration of wind power are raising important issues in power system design and implementation. Wind power is intermittent, meaning both that the output of wind-powered generators is variable over time and that it is uncertainit cannot be perfectly forecast. The output of hydroelectric facilities is also variable and uncertain because rainfall cannot be perfectly forecast, but these features manifest themselves over timescales of months or years. The output of wind facilities is variable and uncertain over timescales of hours; the output of solar facilities can vary from minute to minute as clouds pass by overhead. Because residential, commercial, and industrial demands also vary over these timescales, at small levels of penetration wind power poses no new issues. At large scales, however, an influential industry group has concluded that reliably integrating high levels of variable resources [including wind and solar] into the North American bulk power system will require significant changes to traditional methods used for system planning and operations (NERC 2009a).20 Perhaps the most talked-about manifestation of the variability of wind power took place in Texas on February 26, 2008 (ERCOT 2008; Galbraith 2008; Grant et al. 2009, 5152). As the evening electricity load was increasing, wind generation dropped from over 1,700 MW to 300 MW in a three-hour period because wind speeds decreased. This was roughly equivalent in magnitude to a single large fossil-fuel generating unit going offlinenot a rare eventand it happened gradually. (Moreover, commercial forecasts, not available to the grid operators, had predicted the fall in wind speeds.) This emergency, which was exacerbated by the unforeseen unavailability of some fossil-fuel-fired capacity, was mainly handled by curtailing service to large industrial and commercial users that had contracted for interruptible power, reducing system loads by 1,100 MW within a 10-minute period. Although wind provides only a few percent of generation in Texas
on average, winds in the western part of the state generally blow the strongest at night, when demand is low. As a result, wind can provide more than 10% of power in Texas on some occasions, making its variability a potentially more serious issue. In fall 2008, the operators of the Texas grid increased backup power requirements, particularly at night. With wind generation in Texas expected to more than double by 2015, winds variability is projected to be a more serious concern going forward, and more flexible gas-fired capacity will likely be needed. Since power prices tend to be low at night, however, and the gas-fired units used to provide backup power tend to have high marginal costs, some observers worry that the Texas market may not provide sufficient backup capacity (see OGrady 2009 and Chapter 9 of this volume).21 Similar issues have confronted the Bonneville Power Administration (BPA), which operates the grid in the Pacific Northwest with about 41,000 MW of peak generating capacity. During one 24-hour period in December 2008, BPA received near-zero levels of wind generation early and late in the day, punctuated by nearly 1,600 MW around midday. Between January 5 and 14, 2009, wind output varied from 500 MW to 1,500 MWfollowed by two weeks of zero output. In part, this high degree of variability has arisen because most of the relevant capacity is geographically concentrated along the Washington Oregon borderjust as much of Texass wind generation capacity is concentrated in the western portionand thus wind conditions are highly correlated within the generating fleet (Riner 2009, 8).22 As wind power gains in importance, it will become more important to enhance the flexibility of the overall electric power system (Grant et al. 2009; IEA 2008b; Milligan et al. 2009; NERC 2009a). The basic methods for doing this are well known. One can, in principle, increase demand responsiveness to supply conditions and go well beyond what was possible in the Texas emergency discussed above. A variety of institutional, regulatory, and technological barriers make this far from straightforward, however. On the supply side, one