摘要
随着分布式资源和灵活负荷广泛接入,配电网正演变成为主动配电网,其能量管理与运行控制面临着巨大挑战:1)海量分布式资源并网使得调控需求大增,同时引入了大量随机性使得运行风险增加,需要挖掘其主动支撑能力;2)分布式资源量大且异动频繁,难以及时维护,配电网模型精度差,基于精确建模的运行控制和优化调度技术的工程应用困难。为应对上述挑战,文中介绍了基于机器学习的理论和方法,提出了“测-辨-控”一体化的主动配电网能量管理与运行控制技术体系,实现少/免模型维护的运行控制与优化调度。同时,分析了以下核心技术:1)配电网弱/无模型实时调控技术,实现自律优化;2)分布式资源集群自适应动态控制技术,实现对电网的主动支撑;3)风险量化的概率优化调度方法,实现风险与经济的平衡。最后,简要介绍了适应含极高比例分布式资源的主动配电网的能量管理与运行控制系统架构。
With the large-scale integration of distributed energy resources(DERs)and flexible power loads,the distribution networks are transforming into active distribution networks(ADNs).This transformation poses significant challenges to the energy management and operation control:1)The integration of massive DERs requires additional scheduling capacity,which necessitates the active control of DERs to enhance system support capabilities.The variability of these sources also significantly increases the operation risk of ADNs.2)The complexity and frequent changes in DERs make timely maintenance impractical,and the accuracy of the distribution network model is poor.The engineering application of the precise modeling based operation control and optimal scheduling technology is difficult.To address these challenges,this paper introduces the theory and methods based on machine learning,proposes an energy management and operation control technology system for ADNs that integrates“measurementidentification-control”,and realizes operation control and optimal scheduling with minimal/no model maintenance.Meanwhile,the following key techniques are analyzed:1)the real-time scheduling technology for distribution networks with weak models or without models,achieving autonomous optimization;2)the adaptive dynamic control technology for DER clusters,enabling proactive support for the power grid;3)the probability optimization scheduling method for risk quantification,achieving a balance between risk and economy.Finally,the architecture of the energy management and operation control system suitable for ADNs with a extremely high proportion of DERs is briefly introduced.
作者
吴文传
蔺晨晖
孙宏斌
王彬
刘昊天
吴冠男
李鹏华
孙树敏
卢建刚
WU Wenchuan;LIN Chenhui;SUN Hongbin;WANG Bin;LIU Haotian;WU Guannan;LI Penghua;SUN Shumin;LU Jiangang(Department of Electrical Engineering,Tsinghua University,Beijing 100084,China;State Key Laboratory of Power System Operation and Control(Tsinghua University),Beijing 100084,China;Electric Power Research Institute of State Grid Shandong Electric Power Company,Jinan 250003,China;Guangdong Power Grid Co.,Ltd.,Guangzhou 510180,China)
出处
《电力系统自动化》
EI
CSCD
北大核心
2024年第20期2-11,共10页
Automation of Electric Power Systems
基金
国家重点研发计划资助项目(2022YFB2402900)
国家自然科学基金资助项目(52307101)。
关键词
主动配电网
分布式资源
能量管理
运行控制
机器学习
反馈优化
深度强化学习
active distribution network(ADN)
distributed energy resource
energy management
operation control
machine learning
feedback optimization
deep reinforcement learning