南方电网技术2024，Vol.18Issue(12)：77-86,10.DOI:10.13648/j.cnki.issn1674-0629.2024.12.009
基于多智能体强化学习的两阶段电压控制策略
Two-Stage Voltage Control Strategy Based on Multi-Agent Reinforcement Learning
摘要
Abstract
With a large number of distributed photovoltaic(PV)access to the distribution network,the distribution network faces greater challenges in dealing with network reconstruction and source-load-storage uncertainty,etc.Therefore,a two-stage voltage control strategy for active distribution networks is proposed.In the first stage,the contact switch of the active distribution network is centrally controlled.The network reconstruction is carried out with the goal of minimizing the network loss in an hourly scheduling period,and a mixed integer second-order cone planning model is established to solve the problem.In the second stage,the real-time voltage control of photovoltaic and energy storage systems is carried out,and the real-time voltage control problem is converted into the Markov game process(MGP).The multi-agent model is implemented and the offline training-online operation method is adopted.Compared with the traditional two-stage mathematical planning approach,the control strategy proposed does not rely on an accurate distribution network power flow model,has low communication requirements and a faster solution speed.Finally,the ef-fectiveness of the proposed control strategy is verified by the improved IEEE 33-node calculation examples.关键词
分布式光伏/分布式储能/配电网重构/多智能体强化学习/电压控制Key words
distributed photovoltaic/distributed energy storage/distribution network reconstruction/multi-agent reinforcement learning/voltage control分类
信息技术与安全科学引用本文复制引用
张涛,郝正航,徐玉韬,马启鹏,李超,杨玉杰..基于多智能体强化学习的两阶段电压控制策略[J].南方电网技术,2024,18(12):77-86,10.基金项目
国家自然科学基金资助项目(522670031001610).Supported by the National Natural Science Foundation of China(522670031001610). （522670031001610）
