中国电机工程学报2025，Vol.45Issue(22)：8764-8778,中插8,16.DOI:10.13334/j.0258-8013.pcsee.241080
基于约束增强安全强化学习的光-储-充高渗透配电网有功/无功优化决策方法
Constraint-enhanced Safe Reinforcement Learning-based Decision-making Method for Re/active Power Optimization in Highly Penetrated PV-storage-charging Distribution Network
摘要
Abstract
To address the uncertainties in power flow states and the complexity of power optimization problems brought about by the high penetration of electric vehicles(EVs)and photovoltaics(PVs)in distribution networks,this paper proposes an active/reactive power optimization framework for high-penetration PV-storage-charging distribution networks based on a constraint-enhanced safe reinforcement learning method.Firstly,an EV charging station(EVCS)model based on transfer reinforcement learning is constructed to reasonably describe the charging and discharging process of multiple types of EVs considering demand response.Then,the active/reactive power optimization problem is formulated as a Markov decision process,and a mixed-integer programming-continuous double deep Q network(MIP-CDDQN)algorithm is proposed to solve it.This algorithm transforms the Max-Q problem for the continuous action value network into an MIP model while considering the real-time operational constraints of the distribution network,which ensures efficiency and security of the optimization strategy.Finally,simulation experiments on the IEEE 33-bus system demonstrate the effectiveness of the proposed MIP-CDDQN algorithm and its advantages in enforcing constraints and computational efficiency.关键词
光-储-充高渗透配电网/有功/无功优化/电动汽车充电站/约束增强安全强化学习/混合整数规划Key words
PV-storage-charging high-penetration distribution network/active/reactive power optimization/electric vehicle charging station/constraint-enhanced safe reinforcement learning/mixed-integer programming分类
信息技术与安全科学引用本文复制引用
HONG Lucheng,WU Minghe,ZHU Jin,ZANG Hanzhou,WANG Ziqiu,DU Ai..基于约束增强安全强化学习的光-储-充高渗透配电网有功/无功优化决策方法[J].中国电机工程学报,2025,45(22):8764-8778,中插8,16.基金项目
国家自然科学基金项目(面上项目)(52077039). Project Supported by National Natural Science Foundation of China(General Program)(52077039). （面上项目）
