中国电机工程学报2024，Vol.44Issue(16)：6385-6403,19.DOI:10.13334/j.0258-8013.pcsee.240516
深度强化学习在含分布式柔性资源的电网优化调度中的应用研究综述
A Review of Research on the Application of Deep Reinforcement Learning in Optimization Dispatch of Power Grids With Distributed Flexible Resources
摘要
Abstract
Since China proposed the carbon peak and carbon neutrality goals in 2020,distributed flexible resources such as rooftop photovoltaic,electric vehicles,and flexible energy storage have exhibited a trend of massive development,providing significant potential for the balance of the new type power systems.However,as the multiple uncertainties of massive flexible resources increase,the spatiotemporal decision variables is becoming more complex and high-dimensional,and the difficulty of accurate mechanism modeling has surged sharply,causing traditional optimization methods to encounter bottlenecks when solving the power grid optimization dispatch problems with large-scale,highly random,and cognitively difficult flexible resources.In recent years,as a new generation of machine learning paradigm,deep reinforcement learning has demonstrated the ability to cope with such challenges by learning optimal strategies through interaction with the environment when there is no detailed model parameters.In this regard,the paper provides a comprehensive review of research on optimization dispatch of power grids with distributed flexible resources.Specifically,it first analyzes the operational characteristics of resources,problem modeling,and solution strategies.Then it briefly outlines the principles and classification of the algorithms.Following this,it divides scenarios into demand-side user energy management,aggregated layer cluster coordinated response,and grid-side optimization operation control according to the different focuses of the dispatch problem,analyzing typical applications,solution processes,algorithm effectiveness.Subsequently,it summarizes the advantages and disadvantages of existing methods,and suggests improvements.Finally,it analyzes future research directions from the perspectives of constructing simulation environments,improving solving strategies,and enhancing agent performance.关键词
分布式柔性资源/优化调度/深度强化学习/数据驱动方法/新型电力系统Key words
distributed flexible resources/optimization dispatch/deep reinforcement learning/data-driven method/new type power systems分类
信息技术与安全科学引用本文复制引用
高冠中,杨胜春,郭晓蕊,姚建国,李亚平,朱克东,严嘉豪..深度强化学习在含分布式柔性资源的电网优化调度中的应用研究综述[J].中国电机工程学报,2024,44(16):6385-6403,19.基金项目
国家重点研发计划项目(2022YFB2403200).National Key R&D Program of China(2022YFB2403200). （2022YFB2403200）
