{'fraction_non_alphanumeric': 0.02280130293159609, 'fraction_numerical': 0.0, 'mean_word_length': 5.2693877551020405, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Techno-Economic Analysis and Optimization of a Microgrid Considering Demand-Side Management

The control and management of power demand and supply become very crucial due to the penetration of renewables in the electricity networks and energy demand increase in residential and commercial sectors. In this paper, a new approach is presented to bridge the gap between Demand-Side Management (DSM) and microgrid (MG) portfolio, sizing, and placement optimization. Although DSM helps customers to take advantage of recent developments in the utilization of Distributed Energy Resources (DERs) especially microgrids, a huge need for connecting DSM results to microgrid optimization is being felt. Consequently, we propose a novel model that integrates the DSM techniques and microgrid modules in a two-layer configuration. In the first layer, DSM is employed to minimize the electricity demand (e.g., heating and cooling loads) based on zone temperature set-point. Using the optimal load profile obtained from the first layer, all investment and operation costs of a microgrid are then optimized in the second layer. The presented model is based on the existing optimization platform developed by RU-LESS (Laboratory for Energy Smart Systems at Rutgers University) research group. As a demonstration, the developed model has been used to study the impact of smart HVAC control on microgrid compared to traditional HVAC control. The results show a noticeable reduction in the total annual energy consumption and the annual cost of microgrid