龚迪阳,唐雅洁,高为举,汪湘晋,蒋国栋,付明
GONG Diyang,TANG Yajie,GAO Weiju,WANG Xiangjin,JIANG Guodong,FU Ming

• 1:国网浙江省电力有限公司电力科学研究院
• 2:国电南瑞南京控制系统有限公司

摘要(Abstract)：

针对分布式光伏电源出力的间歇性和不确定性引发的配电网电压越限问题，提出基于Q学习和PCA（牵制一致性算法）的分布式光伏集群优化调度策略。首先，构建融合节点电气距离与电压灵敏度的综合指标，并应用DTW（动态时间弯曲）算法实现分布式光伏集群的合理划分。其次，建立考虑集群间功率交互的双层优化调度模型，采用逃生优化算法对模型进行求解。然后，将Q学习与PCA相结合，实现集群内的协同控制。最后，基于某地区电网进行仿真分析，结果表明：所提策略能够增强分布式光伏集群对电网的支撑能力，改善配电网运行水平，提高模型的求解效率，验证了该策略的有效性和优越性。
To address voltage violations in distribution networks caused by the intermittency and uncertainty of distributed photovoltaic(PV) generation, this paper proposes an optimal cluster dispatch strategy integrating Qlearning and pinning consensus algorithm(PCA). First, a composite index merging nodal electrical distance and voltage sensitivity is developed, and the dynamic time warping(DTW) is applied for rational PV cluster partitioning. Second, a bi-level optimal dispatch model accounting for inter-cluster power exchange is established, with the escape optimization algorithm employed to solve the model. Third, Q-learning and PCA enable cooperative intracluster control. Finally, simulation on a regional power grid demonstrate enhanced grid-support capability of PV clusters, improved operational performance of distribution network, higher solution efficiency, and superior effectiveness versus conventional methods.

关键词(KeyWords)： 分布式光伏集群;双层优化调度;Q学习;牵制一致性算法
distributed PV cluster;bi-level optimal dispatch;Q-learning;PCA

Abstract:

Keywords:

基金项目(Foundation): 国网浙江省电力有限公司科技项目（5211DS230003）

作者(Author): 龚迪阳,唐雅洁,高为举,汪湘晋,蒋国栋,付明
GONG Diyang,TANG Yajie,GAO Weiju,WANG Xiangjin,JIANG Guodong,FU Ming

DOI: 10.19585/j.zjdl.202511007

参考文献(References)：

• [1]杨悦，陈宇航，成龙，等.考虑节点功率储备与GIN中心性的主动配电网动态集群电压控制[J].电网技术，2024,48(2):618-632.YANG Yue,CHEN Yuhang,CHENG Long,et al.Power reserve and GIN centrality of buses considered dynamic cluster voltage control of active distribution networks[J].Power System Technology,2024,48(2):618-632.
• [2] DALL’ANESE E,DHOPLE S V,GIANNAKIS G B.Optimal dispatch of photovoltaic inverters in residential distribution systems[J].IEEE Transactions on Sustainable Energy,2014,5(2):487-497.
• [3] LI Z W,CHENG Z P,SI J K,et al. Distributed eventtriggered hierarchical control of PV inverters to provide multi-time scale frequency response for AC microgrid[J].IEEE Transactions on Power Systems,2023,38(2):1529-1542.
• [4] GUO F H,WANG L,WEN C Y,et al.Distributed voltage restoration and current sharing control in islanded DC microgrid systems without continuous communication[J].IEEE Transactions on Industrial Electronics,2020,67(4):3043-3053.
• [5] XIAO C L,DING M,SUN L,et al. Network partitionbased two-layer optimal scheduling for active distribution networks with multiple stakeholders[J]. IEEE Transactions on Industrial Informatics,2021,17(9):5948-5960.
• [6] DA SILVA E L,LIMA A M N,CORRêA M B R,et al.A new centralized active and reactive power control strategy for voltage regulation in power distribution networks with high penetration of photovoltaic generation[C]//2016 17th International Conference on Harmonics and Quality of Power(ICHQP). October 16-19,2016,Belo Horizonte,Brazil.IEEE,2016:823-828.
• [7] KHAN Z A,RAHIMI T,CARDENAS-BARRERA J L,et al.Optimal smart inverter volt-watt and volt-var settings to maximize fair contribution in voltage regulation[C]//2024 IEEE 12th International Conference on Smart Energy Grid Engineering(SEGE).August 18-20,2024,Oshawa,ON,Canada.IEEE,2024:31-36.
• [8] MANSOOR V M A M,NGUYEN P H,KLING W L.An integrated control for overvoltage mitigation in the distribution network[C]//IEEE PES Innovative Smart Grid Technologies,Europe.October 12-15,2014,Istanbul,Turkey.IEEE,2014:1-6.
• [9]赵振民，程静，王维庆，等.基于MMC-PET的直流微电网综合控制策略[J].太阳能学报，2022,43(10):458-464.ZHAO Zhenmin,CHENG Jing,WANG Weiqing,et al.Research on DC microgrid lomprehensive control strategy based on mmc-pet[J].Acta Energiae Solaris Sinica,2022,43(10):458-464.
• [10]马庆，邓长虹.基于单/多智能体简化强化学习的电力系统无功电压控制[J].电工技术学报，2024,39(5):1300-1312.MA Qing,DENG Changhong.Single/multi agent simplified deep reinforcement learning based volt-var control of power system[J]. Transactions of China Electrotechnical Society,2024,39(5):1300-1312.
• [11]张剑，崔明建，何怡刚.结合数据驱动与物理模型的主动配电网双时间尺度电压协调优化控制[J].电工技术学报，2024,39(5):1327-1339.ZHANG Jian,CUI Mingjian,HE Yigang.Dual timescales coordinated and optimal voltages control in distribution systems using data-driven and physical optimization[J].Transactions of China Electrotechnical Society,2024,39(5):1327-1339.
• [12]葛津铭，刘英儒，庞丹，等.含高渗透率光伏配电网的集群划分电压控制策略[J].高电压技术，2024,50(1):74-82.GE Jinming,LIU Yingru,PANG Dan,et al.Cluster division voltage control strategy of photovoltaic distribution network with high permeability[J]. High Voltage Engineering,2024,50(1):74-82.
• [13]李金雨，宋福龙，马俊杰，等.基于5G基站可调度潜力与配电网集群划分的储能选址定容方法[J].电力系统自动化，2023,47(18):151-160.LI Jinyu,SONG Fulong,MA Junjie,et al.Siting and sizing method for energy storage based on dispatchable potential of 5G base station and cluster partition of distribution network[J].Automation of Electric Power Systems,2023,47(18):151-160.
• [14]李炜祺，窦晓波，张科鑫，等.数据驱动的配电网电压灵敏度感知方法[J].电网技术，2023,47(11):4711-4718.LI Weiqi,DOU Xiaobo,ZHANG Kexin,et al.Data-driven voltage sensitivity sensing method for distribution network[J].Power System Technology,2023,47(11):4711-4718.
• [15]王文倬，谢丁，谢醉冰，等.考虑集群划分的分布式光伏无功电压控制策略[J].浙江电力，2024,43(7):64-75.WANG Wenzhuo,XIE Ding,XIE Zuibing,et al.A reactive voltage control strategy for distributed PV based on cluster segmentation[J].Zhejiang Electric Power,2024,43(7):64-75.
• [16]周金辉，颜剑峰，王子凌，等.基于模糊动态时间弯曲算法的主动配电网电压运行状态评估[J].电力自动化设备，2021,41(1):62-73.ZHOU Jinhui,YAN Jianfeng,WANG Ziling,et al.Voltage operating state assessment of active distribution network based on fuzzy dynamic time warping algorithm[J].Electric Power Automation Equipment,2021,41(1):62-73.
• [17] OUYANG K C,FU S W,CHEN Y,et al.Escape:an optimization method based on crowd evacuation behaviors[J].Artificial Intelligence Review,2024,58(1):19.
• [18]张天海，于国强，李阳，等.考虑风速和负荷变化场景的风电机组下垂控制设计[J].南京理工大学学报，2020,44(5):550-559.ZHANG Tianhai,YU Guoqiang,LI Yang,et al.Wind turbines droop control design considering wind speed and load change scenarios[J].Journal of Nanjing University of Science and Technology,2020,44(5):550-559.
• [19]茆美琴，奚媛媛，张榴晨，等.基于Q学习的微网二次频率在线自适应控制[J].电力系统自动化，2015,39(20):26-31.MAO Meiqin,XI Yuanyuan,ZHANG Liuchen,et al. Qlearning algorithm based secondary frequency adaptive online control in real-time operation for microgrids[J].Automation of Electric Power Systems,2015,39(20):26-31.
• [20]周烨，汪可友，李国杰，等.基于多智能体一致性算法的微电网分布式分层控制策略[J].电力系统自动化，2017,41(11):142-149.ZHOU Ye,WANG Keyou,LI Guojie,et al.Distributed hierarchical control for microgrid based on multi-agent consensus algorithm[J]. Automation of Electric Power Systems,2017,41(11):142-149.
• [21]孟晓凡.基于牵制控制的多智能体系统的固定时间一致性[D].哈尔滨：哈尔滨工程大学，2023.MENG Xiaofan.Fixed-time consensus for multi-agent systems via pinning control[D].Harbin:Harbin Engineering University,2023.
• [22]姜昊，赵建国，冯李.基于Fast-Newman算法的网络社团分区在传送网汇聚区划分中的应用[J].信息通信，2019,32(1):212-214.JIANG Hao,ZHAO Jianguo,FENG Li. Application of network community partition based on Fast-Newman algorithm in the division of transport network convergence area[J]. Information&Communications,2019,32(1):212-214.