Dynamic penetration allocation for distributed generators based on PSO initialized with K-means cluster

Abstract

Grid connection of distributed generator (DG) is highly increasing in power distribution system. Network energy loss (NEL) reducing for multiple DGs is very significant. To minimize the NEL, we propose a methodology based on the particle swarm optimization (PSO) initialized with K-means cluster to dynamically allocate the penetration for multiple DGs. The random initialization of particles in the PSO can lead to a premature convergence to a local solution. In order to avoid this problem, we employ the K-means clustering method to initialize particle with the designed formulas. We divide the nodes into the clusters with various NEL objects by K-means clustering method. The NEL objects include annual NEL, probabilistic annual NEL and hourly NEL. Based on the statistical probability of hourly NEL cluster, we obtain the time-varying cluster. Since nodes have similar characteristic in the same cluster, cluster with less centre distance of NEL and the node with less NEL are allocated more penetration by the designed formulas. We utilize this penetration to initialize the particle in PSO algorithm. Because the variation of power generated from DG and load demand can impact on NEL, we utilize the PSO based on the K-means clustering initialization to allocate the penetration at each hour with the historical data. The PSO algorithm and proposed method are applied to a 38 nodes power distribution system. We carry on the simulation in various types of time period. We also present load growth to compare the proposed method with PSO algorithm. The results indicate that the proposed allocation strategy performs better than PSO in reducing NEL.