摘要
随着高比例分布式光伏接入配电网,电压越限和网损增加等问题愈发显著,而传统电压控制方法难以实时处理新能源出力快速变化导致的电压剧烈波动,无法满足未来新型配电网的安全稳定运行要求。为此,该文提出一种基于时空伪孪生网络的图多智能体深度强化学习的配电网分区电压控制策略。首先,在双重约束条件下界定光伏逆变器无功调节范围;其次,将配电网分区电压控制问题建模为分布式部分可观测马尔可夫决策过程(partially observable Markov decisionprocess,POMDP);再次,在算法中嵌入动态图注意力网络和长短期记忆(longshort-termmemory,LSTM)网络组成的时空伪孪生网络,生成时空融合的特征向量;最后,在改进的IEEE141节点配电网系统中进行算例验证。结果表明,相比于传统电压控制方法,所提算法在有效减小电压偏差和功率损耗的同时,还具备较强泛化性和实时性,可为实现新型配电网分区电压控制提供灵活高效的解决方案。
With the increasing integration of distributed photovoltaic systems into the distribution network,issues such as voltage limit violation and network losses have become more prominent.However,traditional voltage control methods fail to address the voltage fluctuations caused by rapid changes in the output of new energy sources,making it challenging to ensure the safe and stable operation of distribution networks.To address this,this paper proposes a zonal voltage control strategy for the distribution network based on graph multi-agent deep reinforcement learning with a spatial-temporal pseudo-twin network.First,the reactive power regulation range of photovoltaic inverters is defined under dual constraints.Next,the zonal voltage control problem is formulated as a distributed partially observable Markov decision process(POMDP).The algorithm incorporates a spatial-temporal pseudo-twin network that consists of a dynamic graph attention network and a long short-term memory(LSTM)network,to generate a spatial-temporal fused feature vector.Finally,the proposed method is validated through an example by using a modified IEEE 141-node distribution network.The results demonstrate that,compared with traditional voltage control methods,the proposed algorithm effectively reduces voltage deviations and power losses,and also presents strong generalization ability and real-time performance.This approach provides a flexible.
作者
崔杨
祝福
王议坚
黄思宇
赵钰婷
杨茂
CUI Yang;ZHU Fu;WANG Yijian;HUANG Siyu;ZHAO Yuting;YANG Mao(Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology,Ministry of Education(Northeast Electric Power University),Jilin 132012,Jilin Province,China;State Grid Jiangsu Electric Power Company,Xiangshui County Power Supply Branch,Yancheng 224000,Jiangsu Province,China;Changchun Power Supply Company,State Grid Jilin Electric Power Company,Changchun 130000,Jilin Province,China)
出处
《中国电机工程学报》
北大核心
2025年第21期8295-8307,I0003,共14页
PROCEEDINGS OF THE CHINESE SOCIETY FOR ELECTRICAL ENGINEERING
基金
国家重点研发计划项目(2022YFB2403000)。
关键词
伪李生网络
多智能体
深度强化学习
分区电压控制
动态图注意力网络
时空融合
pseudo-twin network
multi-agent
deep reinforcement learning
zonal voltage control
dynamic graph attention network
spatial-temporal fusion
作者简介
崔杨(1980),男,博士,教授,博士生导师,主要研究方向为电力系统运行分析、新能源联网发电关键技术等,cuiyang0432@163.com;通信作者:祝福(2001),女,硕士研究生,研究方向为主动配电网无功电压控制,1918119493@qq.com。
