电力系统保护与控制2024，Vol.52Issue(22)：1-11,11.DOI:10.19783/j.cnki.pspc.240117
考虑分区与模仿学习的深度强化学习配电网电压优化策略
Voltage optimization strategy for a distribution network based on deep reinforcement learning considering regionalization and imitation learning
摘要
Abstract
The current deep reinforcement learning(DRL)method has some issues with voltage optimization,such as challenging credit allocation and low exploration efficiency.These all lead to poor performance in model training speed and optimization effect.Considering regionalization and imitation learning,a voltage optimization strategy based on the guidance signal-based multi-agent deep deterministic policy gradient(GS-MADDPG)is proposed.First,electric vehicle(EV)clusters,distributed generation(DG)and reactive power regulators are taken as decision agents to build the reinforcement learning optimization model.Secondly,the external reward is decoupled through regionalization of the distribution network,and combined with imitation learning,an internal reward is introduced through the guidance signal to help agents search for optimization quickly.Finally,an example test is conducted on the improved IEEE 33-node distribution network.The results indicate that the proposed voltage optimization strategy has higher sample utilization,more stable convergence,and higher model training efficiency than the traditional DRL method,and improves the voltage optimization effect.关键词
配电网电压优化/深度强化学习/分区降损/模仿学习/指导信号Key words
voltage optimization of distribution network/deep reinforcement learning/zoned loss reduction/imitation learning/guidance signal引用本文复制引用
李士丹,李航,李国杰,韩蓓,徐晋,李玲,王宏韬..考虑分区与模仿学习的深度强化学习配电网电压优化策略[J].电力系统保护与控制,2024,52(22):1-11,11.基金项目
This work is supported by the National Key Research and Development Program of China(No.2022YFE0105200). 国家重点研发计划项目资助(2022YFE0105200) （No.2022YFE0105200）
国网浙江省电力有限公司科技项目资助(5211JX230004) （5211JX230004）
