基于深度强化学习的主动配电网动态重构综述  

Review of Active Distribution Network Dynamic Reconfiguration Based on Deep Reinforcement Learning

作　　者：江昌旭 郭辰 刘晨曦 林俊杰 邵振国[1] JIANG Changxu;GUO Chen;LIU Chenxi;LIN Junjie;SHAO Zhenguo(College of Electrical Engineering and Automation,Fuzhou University(Fujian Smart Electrical Engineering Technology Research Center),Fuzhou 350108,China)

机构地区：[1]福州大学电气与自动化工程学院(福建省电器智能化工程技术研究中心),福州350108

出　　处：《高电压技术》2025年第4期1801-1816,I0016-I0020,共21页High Voltage Engineering

基　　金：国家自然科学基金(72401069;52377087);福建省自然科学基金(2022J05125;2021J05134)。

摘　　要：随着双碳目标的快速发展,大量以风电、光伏为代表的分布式电源接入配电网,这将进一步加剧电源出力的间歇性与波动性。主动配电网动态重构属于一个复杂的高维混合整数非线性随机优化问题,传统算法在解决该问题的过程中存在着诸多不足之处。而深度强化学习算法结合了深度学习与强化学习的优势,非常适用于制定当前备受关注的主动配电网动态重构策略。该文首先对新型电力系统主动配电网特征进行总结,并对当前主动配电网动态重构研究在构建数学模型方面所取得的进展以及所面临的挑战进行了深入分析。其次,对配电网动态重构编码方式进行了探讨,并对深度强化学习算法进行了系统性地综述。进而,重点分析了现有算法在处理主动配电网动态重构时的不足之处,并对深度强化学习算法在主动配电网动态重构方面的研究现状与优势进行了总结与概括。最后,对主动配电网动态重构的未来研究方向进行了展望。With the rapid development of dual-carbon targets,many distributed power sources,represented by wind power and photovoltaics,are being connected to distribution networks.This will further exacerbate the intermittency and volatility of power output.Dynamic reconfiguration of active distribution networks constitutes a complex,high-dimensional,mixed-integer,nonlinear,and stochastic optimization problem.Traditional algorithms exhibit numerous shortcomings in addressing this issue.By integrating the advantages of both deep learning and reinforcement learning,the deep reinforcement learning algorithm is highly suitable for formulating dynamically reconfigurable strategies for active distribution networks,which are currently of great concern.This paper first summarizes the characteristics of the active distribution network of the new generation power system,and analyzes the progress and challenges of the current research on the dynamic reconfiguration of the active distribution network in mathematical models.Secondly,the coding method of the distribution network dynamic reconfiguration is discussed,and the deep reinforcement learning algorithm is systematically reviewed.Furthermore,the shortcomings of the existing algorithms in dealing with the dynamic reconfiguration of the active distribution network are analyzed,and the research status and advantages of the deep reinforcement learning algorithm in the dynamic reconfiguration of the active distribution network are summarized.Finally,the future research directions for the dynamic reconfiguration of active distribution networks are presented.

关 键 词：主动配电网 动态重构 深度强化学习 编码方式 机器学习 人工智能 

分 类 号：TM73[电气工程—电力系统及自动化]