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Scenarios simulation and analysis on electric power planning based on multi-scale forecast: a case study of Taoussa, Mali from 2020 to 2035

A long-term forecast study on the electricity demand of Taoussa of Mali is conducted in this paper, with various scenarios of socioeconomic and technological conditions. The analysis tool, which is applied in scenarios simulation, is the Model for Analysis of Energy Demand from the International Atomic Energy Agency. The analysis results are annual electricity demand and peak load forecast for the electrification from the period 2020 to 2035. During the planning period, the analysis results show that the electricity demand will increase to 49.40 MW (332.57 GWh) for the low scenario (LS), 66.46 MW (472.61 GWh) for the reference scenario (RS), and 89.47 MW (635 GWh) for the high scenario (HS). In addition, the total electricity demand increased at an average rate of 8.13% in the LS, 10.31% in the RS and 12.56% in the HS in all sectors. The electricity peak demand is expected to grow at 7.92%, 10.53% and 12.91% corresponding to the three scenarios; in this case, the system peak demand in 2035 will increase to 64.88 MW for the LS, 92.2 MW for the RS and 126.22 MW, the days of peak load are between 17th -23rd in May. The Industry sector will be the biggest electricity consumer of Taoussa area.


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is temperature and humidity, however, in long term load forecasting, GDP, expected economical situations, changes in the demography etc. are the most common factors which affect load usage [8].
The forecasting methods are classified into traditional (e.g. Fuzzy Logic, Grey model, Metaheuristic algorithms, Regression models, Simulation model, Time series model) and intelligent (e.g. Machine Learning and Neural Network) models.
Furthermore, the advantages and disadvantages of both traditional and intelligent forecasting methods as well as research limitations and future researches are determined [9]. The historic time series data is used in many studies to predict the energy consumption, the effect of variables such as GDP, Consumer Price Index (CPI), humidity, temperature, population, energy price, daylight time, number of rainy days, etc. on energy consumption is analyzed in many others. Proposed models can be applied by the producers, suppliers and regulatory authorities who want to securely supply the electricity at a reasonable cost [10].
Researchers use different models and methodologies to forecast electricity demand.
For example, Abdelmonaem [11] used a hybrid load-forecasting method that combines classical time series formulations with cubic splines to model electricity load, only the hourly temperature is used in the proposed model and predictive power gains are achieved through the modelling of the 24-hour load profiles across weekends and weekdays while also taking into consideration seasonal variations of such profiles.
Long-term trends are accounted for by using population and economic variables. Ivana [12] proposed a deep residual neural network model for day-ahead household electrical energy consumption forecasting and integrate multiple sources of information (weather, calendar and historical load). Ali et al. [13] employed a fuzzy logic model for long-term load forecasting of one year based on the weather parameters (temperature and humidity) and historical load data. Abdulla et al. [7] presented long-term forecasting of electrical loads in Kuwait using Prophet and Holt-Winters models of ten years based on the real data of historical electric load peaks (ten years). Hamida et al. [14] also design enhanced machine learning techniques for electricity load and price forecasting, hourly data of one year is used for the forecasting process. The final energy demand and the final electricity demand of Syria will grow up annually respectively. Both final energy and electricity demand growth rates are lower than the corresponding GDP growth rates, which indicate positive development trends in the elasticity evolution. However, the growth of electricity is continuously higher than that of final energy with increasing tendency over the whole study period. This is a direct result of more automation in industry and more electric equipment in households and services [15]. MAED Model was executed for the above three scenarios and Turkey's 4 of 24 annual electric energy demand values were predicted from the study period. The result shows that the future electricity demand will be growing rapidly in Turkey [16]. The forecast of the energy demand of Tanzania for all economic sectors is analyzed by using the Model for Analysis of Energy Demand (MAED) for a study period. In the study, three scenarios were formulated to simulate possible future long-term energy demand based on socio-economic and technological development with the base year. The study results show the highest growth rate of electricity demand is in the industry sector followed closely by service and household sectors [17].
The second module of MAED, MAED_EL (EL = Electricity) used to determine the annual peak load of future Syrian electric power demand. The results indicate that the current residential behaviour of the Syrian power system will shift in the reference scenario more and more to the typical industry behaviour characterized by higher load factors. The future peak load will grow annually [18].
To improve estimates of future electrical energy needs, the International Atomic Energy Agency (IAEA) developed the Model for Analysis of Energy Demand (MAED) on the long-term evolution of energy demand. MAED software has been used to do the simulation of future electric demand for this paper.

Information on the Taoussa area
Mali is one of the landlocked countries of the Sahelian, half of the national territory is located in the Saharan zone of West Africa, as shown in Figure 1.  [19]. the Niger River, it is 280 km downstream from Timbuktu and 120 km upstream from Gao [20].
For a multifunctional dam such as the Taoussa dam, electricity is only one of the components of the project. Its supply alone will not solve the other problems related to irrigation (development of irrigated areas); and access (the road) [20].

Energy potentials in the Taoussa area
As energy potentials, options include biomass, solar power, hydroelectric power, wind power, uranium, gas and petroleum, as shown in Figure 2 and summarized in Table 1.

Module 2: MAED_EL
Module MAED_EL is used for (hourly electric power demand. The module uses the total annual demand of electricity for each sector (calculated in MAED_D) to determine the total electric power demand for each hour of the year or, in other words, the hourly electric load, which is imposed on the power system under consideration. The model can achieve the following:  The trend of the average annual growth rate of electricity demand;  The seasonal changes in electricity consumption (this variation may be reflected on a monthly or weekly basis, depending on available information);  The changes in electricity consumption owing to the type of day being considered (i.e. working days, weekends, special holidays, etc.);  The hourly variation in electricity consumption during the given type of day is considered.
The total energy demand for each end-use category is aggregated into three main "energy consumer" sectors: household/service; industry, including agriculture, mining, construction and manufacturing; and the transportation sector [24].
The sequence of MAED methodology is presented in Figure 3. The evaluation of output and the modification of initial assumptions are the basic processes that arrive at reasonable results.

Scenarios and background information for MAED
It is necessary to analyze the evolution of energy demand by building scenarios for future development. Scenarios are not predictions or forecasts but are descriptions of images of the future, created from models that reflect different perspectives on the past, present and future.
For the case of Mali, three scenarios were adopted, including one reference scenario and two alternative scenarios (Low and High):  Reference scenario: it reflects the historical trend (from 2014 to 2019) taking into account the variations in GDP.
 Low scenario: under this scenario, climate change, socio-political and economic crises, threats to territorial integrity will plague Mali and continue.
 High scenario: It reflects a more optimistic view of the future, according to state forecasts.
The three scenarios were developed based on four groups of coherent hypotheses concerning:  Demographic evolution;  Economic development;  Lifestyle change;  Technological change.

Assumptions on demographic development
The demographic change assumption is identical for all scenarios as shown in Table 2.    Growth in the energy intensities of fuels and specific uses of electricity in all sectors is based on the following aspects:  Introduction of agricultural machinery in Agriculture;  Mechanisation of construction and mining activities;  Introduction of engines and other supplementary equipment in the manufacturing industry;  Introduction of IT equipment and other technologies in services.
Thermal process efficiencies will increase in all scenarios, but at different rates:  Low Scenario: more pessimistic than the historical pace;  Reference scenario: keep the historical pace;  High Scenario: more optimistic than the historical pace.
Traditional fuels will be replaced by commercial fossil fuels, but at different rates:  Low scenario: at a slow pace;  Reference scenario: at a moderate pace;  High Scenario: at an accelerated pace.

Technological parameters
The energy demand is calculated separately for four major aggregated sectors: industry, transportation, service and household. The calculation of the energy demand of each sector is performed in a similar procedure, in which the demand for each end-use category of energy is driven by one or several socioeconomic and technological parameters, whose values are given as part of the scenarios. MAED_D requires data on technological parameters [25].

Input data for MAED_EL
Related input parameters are shown in Tables 6 to 8.         In Figure 18, the growth rate of electricity demand is slightly higher during the 2020-2025 period, 12.51% for the low scenario (LS), 15.37% for the reference scenario (RS) and 18.6% for the high scenario (HS), there is a slow increase in electricity demand for 5.94%, 7.79% and 9.54% in the LS, RS and HS respectively within 2026-2035 period.  Figure 19. Evolution of the demography and the GDP by scenario.

Demography and comparative evolution of GDP by scenario
The projection for the total population and GDP of the Taoussa area are presented in Figure 19, it has been found that the demography will grow up annually at an average rate of 3.97% and the annual average GDP growth rates will be 2.33%, 4.20% and 6.33% for LS, RS and HS, respectively. The forecast annual average growth rate of GDP/Cap and electricity demand/Cap is shown in Figure 20, during 2020-2035, the GDP/Cap will be 4% for the LS, 6.3% for the RS and 8.5% for the HS whereas the electricity demand/Cap is projected to account for 5.6%, 6.1% and 6% in the LS, RS and HS respectively.

Total annual electric energy demand and peak load
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 12 October 2021 doi:10.20944/preprints202110.0182.v1 must meet the peak load time, it is a short period of critical time during which electricity consumption is highest within a year, could have an interesting strong influence on the reliability of electricity supply.
In Table 10, the times ahead system peak load have some illustrative observation that the average annual growth rate of the system peak between 2020 and 2035 represents 7.92% in the Low scenario, in this case, the system peak demand will increase from 20.8 MW in 2020 to 64.88 MW in 2035.   Figure 21) presents a comparison of these forecasts for three scenarios. The ways to reduce the electricity demand during peak load include replacing energy-intensive appliances with more energy-efficient ones, shifting the operation of electricity-consuming equipment during a peak period to periods of inactivity (off-peak hours), organising the implementation of equipment according to a plan, raising awareness of the population towards energy saving, and relieving certain groups of necessary equipment.  The future work will be based on the hybrid renewable energies (solar power, hydroelectric power, wind power) optimization technique using the MESSAGE model to satisfy this electricity demand.

Conclusion
During the study period 2020-2035 MAED analysis has shown that the GDP, electric capacity and electricity demand will increase to 1.