Abstract

The penetration of distributed photovoltaics (PV) in the distribution network is increasing. Due to the output of distributed PVs has strong fluctuation and poor stability, large-scale access of distributed PVs will bring challenges to the operation of distribution network. Cluster partitioning can reduce the adverse impact of large-scale distributed PVs connected to the grid. First, the particle swarm optimization (PSO), genetic algorithm (GA) and K-means clustering algorithm are used to divide clusters in this paper. And then, an encoding method of the intelligent algorithm based on the connection state matrix is proposed, which can reduce the calculation amount of solving the cluster partition problem using the PSO and GA. Case studies compare and analyze the characteristics of these three mainstream clustering methods on the modified IEEE 33-bus feeder system. The results show that the GA has stronger global search ability and is more suitable for cluster partition of distribution network with large-scale distributed PVs. At the same time, the effectiveness of the proposed encoding method is verified.

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