中国电力2024，Vol.57Issue(3)：43-50,8.DOI:10.11930/j.issn.1004-9649.202311065
基于安全强化学习的主动配电网有功-无功协调优化调度
Coordinated Optimization of Active and Reactive Power of Active Distribution Network Based on Safety Reinforcement Learning
摘要
Abstract
A safe reinforcement learning method based on offline strategies is proposed.Through offline training of a large amount of historical operating data of the distribution network,it gets rid of the traditional optimization method.Dependence on complete and accurate models.First,combined with the distribution network parameter information,an active and reactive power optimization model based on the constrained Markov decision process(CMDP)was established;then,a new safety reinforcement learning method was designed based on the original dual optimization method.The cost function is minimized while maximizing future discount rewards;finally,simulations are performed on power distribution system.The simulation results show that the proposed method can online generate a dispatching strategy that satisfies complex constraints and has economic benefits based on real-time observation information of the distribution network.关键词
主动配电网/有功无功协调优化/安全强化学习Key words
active distribution network/active and reactive power coordination optimization/safety reinforcement learning引用本文复制引用
焦昊,殷岩岩,吴晨,刘建,徐春雷,徐贤,孙国强..基于安全强化学习的主动配电网有功-无功协调优化调度[J].中国电力,2024,57(3):43-50,8.基金项目
国家自然科学基金资助项目(U1966205) （U1966205）
国网江苏省电力有限公司科技项目(J2023121). This work is supported by National Natural Science Foundation of China(No.U1966205)and Science and Technology Project of State Grid Jiangsu Electric Power Co.,Ltd.(No.J2023121). （J2023121）
