面向农村配电网电压优化控制的自适应动态分区方法  

Adaptive Dynamic Partitioning Method for Voltage Optimization Control in Rural Distribution Networks

作　　者：易姝娴 王晶 梁伟宸 李江 马鑫晟 黄炎 YI Shuxian;WANG Jing;LIANG Weichen;LI Jiang;MA Xinsheng;HUANG Yan(Electric Power Research Institute,State Grid Jibei Electric Power Co.,Ltd.,Beijing 100045,China;Sichuan Energy Internet Research Institute,Tsinghua University,Chengdu 610042,China)

机构地区：[1]国网冀北电力有限公司电力科学研究院,北京100045 [2]清华四川能源互联网研究院,成都610042

出　　处：《电力系统及其自动化学报》2025年第3期110-119,共10页Proceedings of the CSU-EPSA

基　　金：国家电网有限公司科技项目(国网冀北电科院2023年农村中压配电网优化控制技术研究,B3018K23000F)。

摘　　要：大量异构分布式资源分散无序地接入农村配网,为源荷功率平衡及节点电压调节等带来了巨大挑战。本文针对农村配电网电压分布式控制分区难的问题,提出一种基于自适应学习粒子群优化算法改进K-means的集成异构分布式资源的农村配电网电压优化控制动态分区方法。首先,建立包含模块度、电压调节能力和节点隶属度的综合分区指标体系;其次,通过非线性减小惯性权值和自适应学习因子改进粒子群优化算法,解决传统粒子群优化易陷入局部最优的问题;最后,在聚类分区算法基础上,利用改进粒子群优化算法优化K-means聚类中心,配合触发机制以实现配电网动态分区。仿真结果表明,该方法能够有效均衡分区规模,提高电压调节能力,与传统粒子群优化的K-means方法相比,速度提升14.8%,精度提升4.3%。A large number of heterogeneous distributed resources are integrated into rural distribution networks in a distributed and disordered way,which poses significant challenges to the power balance between sources and loads,as well as the voltage regulation at various nodes.In this paper,aimed at the difficulty in partitioning for distributed volt-age control in rural distribution networks,a dynamic partitioning method for voltage optimization control in rural distri-bution networks is proposed by integrating heterogeneous distributed resources,which is based on an improved Kmeans algorithm using an adaptive learning particle swarm optimization(PSO)algorithm.First,a comprehensive partitioning index system is established,including modularity,voltage regulation capability and node affiliation.Then,the PSO algorithm is improved through nonlinear inertia weight reduction and adaptive learning factors,thus avoid easily falling into local optima,which is the problem in traditional PSO algorithms.Finally,based on the cluster partitioning algo-rithm,the improved PSO algorithm is used to optimize the Kmeans cluster centers,thereby achieving the dynamic partitioning of distribution networks with the combination of a triggering mechanism.Simulation results show that the pro-posed method can effectively balance the partition sizes and improve the voltage regulation capability,with a 14.8%in-crease in speed and a 4.3%improvement in accuracy compared with those of the traditional PSO-optimized Kmeans method.

关 键 词：农村配电网 动态分区 自适应学习粒子群优化算法 电压控制 分布式控制 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]