南方电网技术2025，Vol.19Issue(2)：68-79,12.DOI:10.13648/j.cnki.issn1674-0629.2025.02.008
基于多智能体深度强化学习的配电网双时间尺度电压控制策略
Distribution Network Dual Time Scale Voltage Control Strategy Based on Multi-Agent Deep Reinforcement Learning
摘要
Abstract
The increasing penetration rate of wind power and photovoltaics(PV)in new power systems exacerbates voltage fluctua-tions in distribution networks,while energy storage(ES)and electric vehicles(EV)play important roles in reducing voltage fluctua-tions in distribution networks.At the same time,smart meters,smart sensors and improved communication networks are widely deployed,the amount of data available is increasing,and data-driven technology is emerging.This paper proposes a multi-agent deep reinforcement learning(MADRL)based dual time scale active and reactive power coordinated voltage control strategy for distribu-tion networks.Using the double deep Q-network algorithm(DDQN)to solve the optimization problems of capacitor banks(CBs),on line tap transformers(OLTC),and ES active and reactive power at a slow time scale.At a fast time scale,the EA-MASAC algorithm with attention mechanism is used to enhance the reactive power of PV,wind turbines(WT),and static var compensators(SVCs),as well as the active power of EVs.Finally,the effectiveness of the proposed method is verified on an IEEE-33 node system.关键词
数据驱动/多智能体深度强化学习/双时间尺度/电压控制/功率优化Key words
data driven/multi-agent deep reinforcement learning/dual time scales/voltage control/power optimization分类
信息技术与安全科学引用本文复制引用
赵晶晶,张超立,王涵,盛杰..基于多智能体深度强化学习的配电网双时间尺度电压控制策略[J].南方电网技术,2025,19(2):68-79,12.基金项目
国家自然科学基金资助项目(52007112). Supported by the National Natural Science Foundation of China(52007112). （52007112）
