基于改进粒子群优化算法的分布式电源集群划分方法

Cluster Partition Method of Distributed Power Supply Based on Improved Particle Swarm Optimization Algorithm

随着新型电力系统建设进程的推进,分布式电源并网比重逐渐增加,为解决新型配电网分布式电源调控困难的问题,采用改进粒子群优化算法对大规模分布式电源进行集群划分。首先,在模块度划分标准基础上,引入群内源荷有功匹配度和无功匹配度,提出由三者加权组合的综合性能指标,构建基于综合指标体系的集群划分优化模型;其次,采用惯性权重动态递减策略改进二进制粒子群优化算法,使其惯性权重动态变化,优化粒子位置与速度的更新进程,提高粒子群优化算法的寻优效率;最后,采用改进二进制粒子群优化算法对基于综合指标体系的集群划分优化模型进行寻优,基于此对IEEE33节点、某10 kV实际配网馈线系统进行集群划分。结果表明:基于综合指标体系的集群划分方法在保障划分结果模块度基本不变的基础上,可将有功匹配度和无功匹配度分别提高30%左右;而改进粒子群优化算法对提高划分结果的各个指标值均具有明显效果。

With the development of new power system construction process continued to advance,the proportion of grid-connected distributed power gradually increased.In order to solve the difficult problem of the regulation of distributed power in new distribution network,this paper adopted the improved particle swarm optimization algorithm was proposed to cluster the large-scale distributed power.Firstly,on the basis of the module degree modularity division standard,the active power matching degree and reactive power matching degree of the group internal load were introduced,and the comprehensive performance indexes weighted by the three were proposed to construct the cluster division optimization model based on the comprehensive index system.Then the inertia weight dynamic decline strategy was used to improve the binary particle swarm optimization algorithm to make the inertia weight change dynamically,optimizing the updating process of particle position and velocity,and improving the optimization efficiency of particle swarm optimization algorithm.Finally,the improved binary particle swarm optimization algorithm was used to optimize the clustering optimization model based on the comprehensive index system.Based on this,cluster division of IEEE33 nodes and a 10 kV actual distribution network feeder system was carried out.The results showed that the cluster partitioning method based on the comprehensive index system could improve the active power matching degree and reactive power matching degree by about 30%,respectively,on the basis of keeping the module degree modularity of the partitioning result basically unchanged.The improved particle swarm optimization algorithm had obvious effect on improving each index value of partitioning results.