{"title":"Multi-objective optimization by reinforcement learning for power system dispatch and voltage stability","authors":"Huilian Liao, Qinghua Wu, Lin Jiang","doi":"10.1109/ISGTEUROPE.2010.5638914","DOIUrl":null,"url":null,"abstract":"This paper presents a new method called Multi-objective Optimization by Reinforcement Learning (MORL), to solve the optimal power system dispatch and voltage stability problem. In MORL, the search is undertaken on individual dimension in a high-dimensional space via a path selected by an estimated path value which represents the potential of finding a better solution. MORL is compared with multi-objective evolutionary algorithm based on decomposition (MOEA/D) to solve the multi-objective optimal power flow problems in power systems. The simulation results have demonstrated that MORL is superior over MOEA/D, as MORL can find wider and more evenly distributed Pareto fronts, obtain more accurate Pareto optimal solutions, and require less computation time.","PeriodicalId":267185,"journal":{"name":"2010 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISGTEUROPE.2010.5638914","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 23
Abstract
This paper presents a new method called Multi-objective Optimization by Reinforcement Learning (MORL), to solve the optimal power system dispatch and voltage stability problem. In MORL, the search is undertaken on individual dimension in a high-dimensional space via a path selected by an estimated path value which represents the potential of finding a better solution. MORL is compared with multi-objective evolutionary algorithm based on decomposition (MOEA/D) to solve the multi-objective optimal power flow problems in power systems. The simulation results have demonstrated that MORL is superior over MOEA/D, as MORL can find wider and more evenly distributed Pareto fronts, obtain more accurate Pareto optimal solutions, and require less computation time.