翟苏巍, 李银银, 杜凡, 李文云, 潘凯岩, 梁峻恺. 考虑海量分布式能源接入的配电网分布式无功控制策略[J]. 中国电力, 2024, 57(8): 138-144. DOI: 10.11930/j.issn.1004-9649.202403107
引用本文: 翟苏巍, 李银银, 杜凡, 李文云, 潘凯岩, 梁峻恺. 考虑海量分布式能源接入的配电网分布式无功控制策略[J]. 中国电力, 2024, 57(8): 138-144. DOI: 10.11930/j.issn.1004-9649.202403107
ZHAI Suwei, LI Yinyin, DU Fan, LI Wenyun, PAN Kaiyan, LIANG Junkai. Distributed Reactive Power Control Strategy of Distribution Network Considering Massive Distributed Energy Access[J]. Electric Power, 2024, 57(8): 138-144. DOI: 10.11930/j.issn.1004-9649.202403107
Citation: ZHAI Suwei, LI Yinyin, DU Fan, LI Wenyun, PAN Kaiyan, LIANG Junkai. Distributed Reactive Power Control Strategy of Distribution Network Considering Massive Distributed Energy Access[J]. Electric Power, 2024, 57(8): 138-144. DOI: 10.11930/j.issn.1004-9649.202403107

考虑海量分布式能源接入的配电网分布式无功控制策略

Distributed Reactive Power Control Strategy of Distribution Network Considering Massive Distributed Energy Access

  • 摘要: 针对非常规安全风险对新型电力系统造成的影响,提出了考虑源荷不确定性的新型电力系统双层协同无功控制策略。以配电网网络损耗最小为目标,考虑多种调节设备约束,建立配电网无功优化模型。构建配电网分布式无功优化求解框架,外层采用自适应超松弛惩罚参数交替方向乘子法(alternating direction method of multipliers,ADMM)进行全局更新迭代求解,内层采用列与约束生成算法(column-and-constraint generation,C&CG)对各区域两阶段分布鲁棒无功优化模型求解。所提策略能有效改进分布式无功优化模型求解效率,降低网络损耗,提高新型电力系统的稳定性。

     

    Abstract: In view of the influence of unconventional safety risks on new power systems, a two-layer cooperative reactive power control strategy considering the uncertainty of source load was proposed. The reactive power optimization model of distribution network was established with the aim of minimizing the loss of distribution network and considering various regulation equipment constraints. The distribution network distributed reactive power optimization solution framework was constructed. The outer layer adopted the adaptive overrelaxation penalty parameter alternating direction method of multipliers (ADMM) for global update iterative solution. In the inner layer, the column-and-constraint generation (C&CG) algorithm was used to solve the two-stage distributionally robust reactive power optimization model for each region. The proposed strategy can effectively improve the solving efficiency of distributed reactive power optimization model, reduce network losses, and improve the stability of the new power system.

     

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