首页|市场环境下智能配用电系统分层协同优化运行:研究挑战、进展与展望

市场环境下智能配用电系统分层协同优化运行:研究挑战、进展与展望

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
随着分布式资源在配电网中的比例不断提高，如何在市场化交易机制下实现配用电系统安全经济运行成为当下的研究热点。在市场环境下，配用电系统各层的运行管理面临着不确定性逐层加剧、市场规模快速扩展、市场交易与系统安全运行难以有效衔接等多重挑战。该文首先梳理市场环境下配用电系统运行优化的关键问题;其次，对传统解析优化方法的研究成果与研究中仍待解决的问题进行总结;然后，针对配用电系统市场交易、运行优化等问题特点，系统性地介绍深度强化学习技术，分析归纳深度强化学习在配用电系统中的研究现状。最后，提炼出贯穿配用电系统多层多主体协同优化问题中的三重研究需求，并对深度强化学习技术未来的应用路径与发展趋势进行展望。
Hierarchical Coordinated Optimization for Power Distribution and Consumption System Operation in a Market Environment:Challenges,Progress and Prospects
With the increasing proliferation of distributed energy resources in the distribution network,how to establish an effective market-based trading mechanism in the power distribution and consumption system,while achieving efficient and coordinated optimization of market trading and power system operation has attracted unprecedented research interests in China and beyond.In the market environment,the operation and management of each layer of the power distribution and consumption system face multiple challenges,including the layer-wise increasing uncertainties,the increasing scale of market transactions,and lack of efficient coordination of market trading and safe operation of the system.This paper firstly outlines the critical scientific problems associated with optimal operation of power distribution and consumption systems in a market environment.Second,it critically reviews and summarizes existing research efforts in this area,employing conventional optimization-based solution techniques,and subsequently concludes remaining issues that deserve further research attention.Going further,this paper comprehensively reviews relevant deep reinforcement learning techniques and outlines their current applications in the examined research area,considering the primary characteristics pertaining to market trading and dispatch challenges associated with distribution and consumption system.Finally,this paper details three directions which require further research efforts,and also dives deep in revealing how deep reinforcement learning techniques can be developed and extended to support relevant research activities.

distribution market operationdistribution system dispatchtransactive energydemand side managementreinforcement learningmulti-agent systems

叶宇剑、吴奕之、胡健雄、汤奕、陈涛、Goran STRBAC

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东南大学电气工程学院,江苏省 南京市 210096

伦敦帝国理工学院电气与电子工程系,英国 伦敦 SW72AZ

配电市场运营 配电系统调度 可交易能源 需求侧管理 强化学习 多智能体系统

国家自然科学基金青年基金江苏省基础研究计划自然科学基金青年基金江苏省基础研究计划自然科学基金青年基金

52207082BK20220842BK20210243

2024

10.13334/j.0258-8013.pcsee.222708

中国电机工程学报
中国电机工程学会

中国电机工程学报

CSTPCD北大核心
影响因子：2.712
ISSN：0258-8013
年,卷(期)：2024.44(6)
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