Particle Swarm Optimization Based on Fuzzy C-means Cluster for Day-ahead Allocation Plan of Multiple Photovoltaic Distributed Generation

Distribution power system connecting with multiple photovoltaic distributed generation (PV-DG) will bring a huge effect to the economical benefit and stable operation of the distribution power system. The scene of Distribution power system connecting with multiple PV-DGs is designed in this paper, considering with short-time economical benefit and stable operation, economical benefit cost index, voltage error index and voltage stable index of optimal objective, the output of PV-DG is multiple-objective optimizing; In order to improve the optimizing performance of multiple objective particle swarm optimization, the PSO based on fuzzy-C means cluster algorithm is proposed, the initialization of PSO is clarified with fuzzy-C means cluster, the Pareto solving method is used to analyze on the optimizing solution set of the optimization. Considering with uncertainty of PV-DG output and time-variation of load demand, the auto regressive moving average (ARMA) model is used to predict the PV-DG output and load demand, the forecasting data is used to optimize the short-time economical benefit and improve the stability of operation. Based on the illumination and load demand in the history, the simulation is carried on the matlab platform, the simulation results indicate that PSO based on fuzzy-C means cluster optimizing algorithm can optimize the economical benefit cost and stable operation in the same time.