基于多智能体深度强化学习的配电网电压分区协同控制  

Partition Cooperative Control of Distribution Network Voltage Based on Multi-Agent Deep Reinforcement Learning

作　　者：尹昕 曹丽鹏 王玉森 YIN Xin;CAO Li-peng;WANG Yu-sen(State Grid Shanxi Electric Power Company Changzhi Power Supply Company,Changzhi 046000,China)

机构地区：[1]国网山西省电力公司长治供电公司,山西长治046000

出　　处：《电工电气》2025年第2期63-71,共9页Electrotechnics Electric

基　　金：国网山西省电力公司资助项目(5205D0230002)。

摘　　要：为充分利用配电网中多类型调节资源的调节能力,提高新能源高比例接入下配电网的分区自治能力,提出了一种基于多智能体深度强化学习(MADRL)的配电网电压多分区协同控制策略。采用多智能体对配电网分区协同电压控制问题进行建模,并运用改进的反事实多智能体柔性动作-评价(COMASAC)深度强化学习模型求解配电网分区协同电压控制问题。通过实际配电网典型日运行数据的仿真算例,验证了所提基于多智能体深度强化学习方法在提高配电网电压稳定性与降低网络损耗方面的优势。In order to fully utilize the regulation capability of multiple types of regulation resources in the distribution network and improve the zonal autonomy capability of the distribution network under the high proportion of new energy access,this paper proposes a multi-zonal cooperative control strategy for distribution network voltage based on multi-agent deep reinforcement learning(MADRL).The problem of partition cooperative voltage control in distribution network is modeled using a multi-agent approach.Subsequently,an improved counterfactual multi-agent soft actor-critic(COMASAC)deep reinforcement learning model is applied to solve the zonal cooperative voltage control problem in distribution networks.Finally,simulation examples using typical day operational data from actual distribution networks demonstrate the advantages of the proposed multi-agent deep reinforcement learning method in improving voltage stability and reducing network losses in distribution networks.

关 键 词：多智能体 深度强化学习 配电网电压 分区协同控制 网络损耗 

分 类 号：TM714.2[电气工程—电力系统及自动化]