电力系统保护与控制2026，Vol.54Issue(16)：93-106,14.DOI:10.19783/j.cnki.pspc.260188
考虑策略安全层动态修正的配电网电压优化控制
Optimal voltage control for distribution networks considering dynamic correction by policy safety layer
摘要
Abstract
The high penetration of distributed energy resources has significantly increased the risk of voltage limit violations in distribution networks.Voltage optimization control methods based on deep reinforcement learning(DRL)struggle to ensure constraint satisfaction during online execution and lack adaptability to network topology changes.Additionally,model predictive control(MPC)incurs high computational costs due to full-horizon optimization.To address these challenges,an optimal voltage control method for distribution networks using deep reinforcement learning,considering dynamic correction by a policy safety layer is proposed.First,the spatial coupling features of the distribution network topology extracted by the graph attention network(GAT)are integrated into the state space of the deep deterministic policy gradient(DDPG)algorithm for offline training,thereby enhancing adaptability to frequent topology changes.Then,MPC is embedded into the DDPG framework as a policy safety layer.A value network is introduced to approximate the terminal value term,replacing the long-horizon terminal cost optimization in MPC,while the problem is then transformed into a control sequence sampling distribution using the cross-entropy method(CEM).Finally,the proposed method is validated based on an improved IEEE 123-bus system.Simulation results show that the proposed method not only ensures control performance and constraint feasibility but also exhibits excellent optimization control effects and robustness under uncertainties,topology changes,and measurement inaccuracies.关键词
配电网/电压优化控制/深度强化学习/模型预测控制/策略安全层Key words
distribution network/voltage optimization control/deep reinforcement learning/model predictive control/policy safety layer引用本文复制引用
李晓露,杜本杰,苏昊天,柳劲松,林顺富..考虑策略安全层动态修正的配电网电压优化控制[J].电力系统保护与控制,2026,54(16):93-106,14.基金项目
This work is supported by the National Natural Science Foundation of China(No.52577119). 国家自然科学基金项目资助(52577119) （No.52577119）
