陆晓星,肖小龙,叶海涵,刘建,陈武
LU Xiaoxing,XIAO Xiaolong,YE Haihan,LIU Jian,CHEN Wu

• 1:国网江苏省电力有限公司电力科学研究院
• 2:东南大学电气工程学院

摘要(Abstract)：

针对多微网配电系统中可再生能源出力波动与电动汽车负荷变化带来的系统调度困难与节点电压越限问题，提出一种基于模型-数据联合驱动的多微网配电系统经济调度与电压控制方法。首先将多微网配电系统建模为主从博弈模型，制定动态电价改善多主体功率互动与效益协调，通过内嵌主动电价更新环路增强日前调度模型的非线性适应性并提高计算速度；然后采用深度强化学习方法进行日前调度指令跟踪与实时分散式电压控制，从而使配电系统在兼顾多主体经济调度的基础上具备充分的无功电压自主调节能力以应对未建模功率扰动。仿真结果表明，所提方法纠正了峰谷电价激励下储能过度追求经济目标而带来的局部功率失衡与调度负担，改善了配电系统中各主体的功率互动，并实现了源荷不定性工况下的电压主动控制。
To address the dispatch challenges and node voltage violations caused by renewable energy output fluctuations and electric vehicle load variations in multi-microgrid distribution systems, a joint model–data-driven economic dispatch and voltage control method is proposed. First, the multi-microgrid distribution system is transformed as a Stackelberg game model, in which dynamic electricity pricing is designed to improve power interactions and benefit coordination among multiple agents. An embedded active price-updating loop is introduced to enhance the nonlinear adaptability of the day-ahead dispatch model while improving computational efficiency. Subsequently, deep reinforcement learning(RL) is employed to track day-ahead dispatch commands and implement real-time decentralized voltage control. This enables the distribution system to maintain autonomous reactive power regulation capability in the presence of unmodeled power disturbances while ensuring multi-agent economic dispatch. Simulation results demonstrate that the proposed method mitigates local power imbalance and dispatch burdens caused by excessive economically driven operation of energy storage systems under peak-valley price incentives, improves power interactions among different entities in the distribution system, and achieves active voltage control under source–load uncertainty conditions.

关键词(KeyWords)： 微电网;经济调度;电压控制;主从博弈;多智能体
microgrid;economic dispatch;voltage control;Stackelberg game;multiple agents

Abstract:

Keywords:

基金项目(Foundation): 国家电网有限公司科技项目（J2024163）

作者(Author): 陆晓星,肖小龙,叶海涵,刘建,陈武
LU Xiaoxing,XIAO Xiaolong,YE Haihan,LIU Jian,CHEN Wu

DOI: 10.19585/j.zjdl.202608011

参考文献(References)：

• [1]曾智基，杨苹，陈燿圣，等.基于目标级联法的微网群多主体分布式优化调度[J].华北电力大学学报（自然科学版），2019,46(2):25-33.Zeng Zhiji,Yang Ping,Chen Yaosheng,et al.Distributed optimization scheduling of multi-microgrids with multiple stakeholders based on analytical target cascading[J].Journal of North China Electric Power University,2019,46(2):25-33.
• [2]张玉晓，谢加新，李克豫.基于盒式鲁棒的主动配电网经济调度[J].浙江电力，2022,41(3):27-33.Zhang Yuxiao,Xie Jiaxin,Li Keyu.Economic dispatch of active distribution network based on box-type robustness[J].Zhejiang Electric Power,2022,41(3):27-33.
• [3]王书征，吴守豪，吴志，等.风光不确定条件下考虑双边交易的配微电网协同规划[J].电力工程技术，2025,44(3):97-107.Wang Shuzheng,Wu Shouhao,Wu Zhi,et al. Collaborative planning of distribution-grid and micro-grid considering bilateral transactions under wind-solar uncertain conditions[J].Electric Power Engineering Technology,2025,44(3):97-107.
• [4]林达，陈哲，李志浩，等.含多微电网的配电网双层互动式电能交易方法[J].浙江电力，2025,44(6):20-30.Lin Da,Chen Zhe,Li Zhihao,et al.A bilevel interactive energy trading method for distribution networks incorporating multiple microgrids[J].Zhejiang Electric Power,2025,44(6):20-30.
• [5]舒征宇，刘文灿，李黄强，等.基于合作博弈与动态分时电价的电动汽车有序充放电策略[J].电力工程技术，2025,44(3):179-187.Shu Zhengyu,Liu Wencan,Li Huangqiang,et al. An orderly charging and discharging strategy for electric vehicles based on cooperative game and dynamic time-of-use pricing[J].Electric Power Engineering Technology,2025,44(3):179-187.
• [6]赵宇，贺兴，艾芊.基于碳流追踪的电动汽车充电站定价方法[J].电力工程技术，2025,44(6):2-12.Zhao Yu,He Xing,Ai Qian.Pricing method for electric vehicle charging stations based on carbon flow tracing[J].Electric Power Engineering Technology,2025,44(6):2-12.
• [7]马刚，马健，颜云松，等.基于多智能体强化学习的配电网电压分散控制[J].综合智慧能源，2024,46(10):32-39.Ma Gang,Ma Jian,Yan Yunsong,et al.Decentralized voltage control of distribution network based on multi-agent reinforcement learning[J]. Integrated Intelligent Energy,2024,46(10):32-39.
• [8]徐博涵，向月，潘力，等.基于深度强化学习的含高比例可再生能源配电网就地分散式电压管控方法[J].电力系统保护与控制，2022,50(22):100-109.Xu Bohan,Xiang Yue,Pan Li,et al. Local decentralized voltage management of a distribution network with a high proportion of renewable energy based on deep reinforcement learning[J].Power System Protection and Control,2022,50(22):100-109.
• [9]习伟，李鹏，李鹏，等.基于深度强化学习的分布式电源就地自适应电压控制方法[J].电力系统自动化，2022,46(22):25-31.Xi Wei,Li Peng,Li Peng,et al.Adaptive local voltage control method for distributed generator based on deep reinforcement learning[J].Automation of Electric Power Systems,2022,46(22):25-31.
• [10]冯昌森，张瑜，谢路耀，等.配电系统双时间尺度电压管理的深度强化学习方法[J].电力系统自动化，2022,46(12):202-209.Feng Changsen,Zhang Yu,Xie Luyao,et al. Deep reinforcement learning approach for dual-timescale voltage management in distribution system[J]. Automation of Electric Power Systems,2022,46(12):202-209.
• [11]李钟平，向月.深度强化学习驱动的风储系统参与能量-调频市场竞价策略[J].电力工程技术，2025,44(3):30-42.Li Zhongping,Xiang Yue. Deep reinforcement learningdriven bidding strategy for wind-storage systems in energy and frequency regulation markets[J].Electric Power Engineering Technology,2025,44(3):30-42.
• [12]孙国强，殷岩岩，卫志农，等.基于深度确定性策略梯度的主动配电网有功-无功协调优化调度[J].电力建设，2023,44(11):33-42.Sun Guoqiang,Yin Yanyan,Wei Zhinong,et al. Coordinated optimal dispatch of active and reactive power in active distribution networks using deep deterministic strategy gradient[J]. Electric Power Construction,2023,44(11):33-42.
• [13]陈东旭，李岩松，许智光，等.基于BNN-IKAE双层多智能体深度强化学习的大电网母线电压智能自动调整方法[J].电工电能新技术，2024,43(11):68-79.Chen Dongxu,Li Yansong,Xu Zhiguang,et al.Intelligent automatic adjustment method of bus voltage in large power grid based on BNN-IKAE double-layer multi-agent deep reinforcement learning[J].Advanced Technology of Electrical Engineering and Energy,2024,43(11):68-79.
• [14]李鹏，钟瀚明，马红伟，等.基于深度强化学习的有源配电网多时间尺度源荷储协同优化调控[J].电工技术学报，2025,40(5):1487-1502.Li Peng,Zhong Hanming,Ma Hongwei,et al. Multitimescale optimal dispatch of source-load-storage coordination in active distribution network based on deep reinforcement learning[J]. Transactions of China Electrotechnical Society,2025,40(5):1487-1502.
• [15]张梓枭，崔明建，张程彬，等.基于反事实多智能体强化学习和有功无功协同控制的配电网电压优化[J].电力系统保护与控制，2024,52(18):76-86.Zhang Zixiao,Cui Mingjian,Zhang Chengbin,et al.Active and reactive power coordinated optimal voltage control of a distribution network based on counterfactual multi-agent reinforcement learning[J]. Power System Protection and Control,2024,52(18):76-86.
• [16]欧杰宇，张逸，辛荣，等.基于多智能体深度强化学习的低压配电网电能质量分散式协同调控策略[J].中国电机工程学报，2025,45(21):8308-8322.Ou Jieyu,Zhang Yi,Xin Rong,et al.Decentralized coordinated control strategy for power quality in low voltage distribution networks based on multi-agent deep reinforcement learning[J]. Proceedings of the CSEE,2025,45(21):8308-8322.
• [17]胡丹尔，彭勇刚，韦巍，等.多时间尺度的配电网深度强化学习无功优化策略[J].中国电机工程学报，2022,42(14):5034-5045.Hu Daner,Peng Yonggang,Wei Wei,et al. Multitimescale deep reinforcement learning for reactive power optimization of distribution network[J].Proceedings of the CSEE,2022,42(14):5034-5045.
• [18]樊会丛，段志国，陈志永，等.基于多智能体深度策略梯度的离网型微电网双层优化调度[J].中国电力，2025,58(5):11-20.Fan Huicong,Duan Zhiguo,Chen Zhiyong,et al. Twolayer optimization scheduling for off-grid microgrids based on multi-agent deep policy gradient[J]. Electric Power,2025,58(5):11-20.
• [19]朱东方，朱丹丹，周前，等.基于多智能体深度强化学习的新型配电系统多时间尺度无功电压分层分区优化研究[J/OL].华北电力大学学报（自然科学版），2024:1-13[2026-01-29]. https：//link. cnki. net/urlid/13.1212. tm.20241030.1426.002.Zhu Dongfang,Zhu Dandan,Zhou Qian,et al.Research on multi-time scale hierarchical partition optimization of reactive power and voltage for novel power distribution systems based on multi-agent deep reinforcement learning[J/OL]. Journal of North China Electric Power University,2024:1-13[2026-01-29]. https：//link. cnki. net/urlid/13.1212.tm.20241030.1426.002.
• [20]范培潇，杨军，温裕鑫，等.考虑电动汽车与微电网参与的配电网双层协调控制策略[J].电力系统自动化，2024,48(19):60-68.Fan Peixiao,Yang Jun,Wen Yuxin,et al.Bi-layer coordinated control strategy of distribution network considering participation of electric vehicles and microgrid[J].Automation of Electric Power Systems,2024,48(19):60-68.
• [21]张嘉蕾，宣文举，杨栋，等.微网内双重需求响应和微网间P2P合作的协同调度策略[J].电力工程技术，2025,44(6):103-113.Zhang Jialei,Xuan Wenju,Yang Dong,et al.Co-scheduling strategies for intra-microgrid dual demand response and inter-microgrid P2P cooperation[J].Electric Power Engineering Technology,2025,44(6):103-113.
• [22]Rte. Voltage control by a consumer site in areas under stress[EB/OL].https：//www.services-rte.com/en/news/voltage-regulation-in-areas-constrained-by-a-consumersite.htm,2024.
• [23]Meeus L,Saguan M. The clean energy package and demand response:setting correct incentives[J]. Energies,2020,13(21),5672. Ofgem.Network Innovation Allowance(RIIO-2).Office of Gas and Electricity Markets[EB/OL]. https：//www. ofgem. gov. uk/network-innovationallowance-riio-2,2020.