期刊名称：International Journal of Electrical and Computer Engineering
电子版ISSN：2088-8708
出版年度：2022
卷号：12
期号：4
页码：3352-3364
DOI：10.11591/ijece.v12i4.pp3352-3364
语种：English
出版社：Institute of Advanced Engineering and Science (IAES)
摘要：The concept of the optimization energy management system, developed in this work, is to determine the optimal combination of energy from several generation sources and to schedule their commitment, while optimizing the cost of energy, power losses and voltage drops. In order to achieve these objectives, the non-dominated sorting genetic Algorithm II (NSGA-II) was modified and applied to an IEEE 33-bus test network containing 10 photovoltaic power plants and 4 battery energy storage systems placed at optimal points in the network. To evaluate the system performance, the resolution was performed under several test conditions. Optimal Pareto solutions were classified using three decision-making methods, namely analytic hierarchy process (AHP), TOPSIS and entropy-TOPSIS, compared to each other for more accurate results. The simulation results obtained by NSGA-II and classified using entropy-TOPSIS showed a significant and considerable reduction in terms of energy cost, power losses and voltage drops while successfully meeting all constraints. In addition, the diversity of the results proved once again the robustness and effectiveness of the algorithm. A graphical interface was also developed to display all the decisions made by the algorithm, and all other information such as the states of power systems, voltage profiles, alarms, and history.
关键词：battery energy storage system;economic dispatch;energy management;entropy-TOPSIS;non-dominated sorting genetic algorithm ii;optimization;photovoltaic systems
