{"title":"基于强化学习的电力系统调度与电压稳定多目标优化","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":"{\"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}","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}
Multi-objective optimization by reinforcement learning for power system dispatch and voltage stability
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.