控制理论与应用2024，Vol.41Issue(6)：999-1008,10.DOI:10.7641/CTA.2023.30091
面向主动配电网实时优化调度的图强化学习方法
Graph reinforcement learning for real-time optimal dispatch of active distribution network
摘要
Abstract
The renewable energy system,energy storage system and other energy resources of active distribution network can effectively improve the flexibility and reliability of operation.Meanwhile,renewable energy and load also bring uncertainty to the distribution network,resulting in large dimensions of real-time optimal dispatch and poor modeling accuracy of active distribution network.To solve this problem,a graph reinforcement learning method combining graph neural network and reinforcement learning is proposed to avoid accurate modeling of complex systems.Firstly,the real-time optimal dispatch is described as Markov decision process and dynamic sequential decision problem.Secondly,a graph representation method based on the physical connection is proposed to express the implied correlation of state variables.Then a graph reinforcement learning is proposed to learn the optimal strategy for mapping system state graph to decision output.Finally,the graph reinforcement learning is developed to distributed graph reinforcement learning.The simulations show that graph reinforcement learning achieves better results in optimality and efficiency.关键词
主动配电网/实时优化调度/图表示学习/图强化学习/图神经网络Key words
activate distribution network/real-time optimal dispatch/graph representation/graph reinforcement learn-ing/graph neural network引用本文复制引用
陈俊斌,余涛,潘振宁..面向主动配电网实时优化调度的图强化学习方法[J].控制理论与应用,2024,41(6):999-1008,10.基金项目
国家自然科学基金委员会-国家电网公司智能电网联合基金项目(U2066212),国家自然科学基金项目(52207105),中国博士后科学基金项目(2022M721184)资助.Supported by the Natural Science Foundation of China-Smart Grid Joint Fund of State Grid Corporation of China(U2066212),the National Natu-ral Science Foundation of China(52207105)and the China Postdoctoral Science Foundation(2022M721184). （U2066212）
