考虑馈线负荷与多类型资源协同的配电网多目标调峰优化OA

Aiming at the problem of peak-valley difference increase and voltage violation caused by large-scale fluctuation of network power in multi-time sections under high-proportion photovoltaic distribution network,a peak-shaving optimization strategy based on feeder load power control and multi-type resources was proposed.Firstly,the K-means clustering algorithm combined with KL(Kullback-Leibler)divergence was used to generate photovoltaic output scenarios and probability distribution uncertainty sets.On this basis,considering the active response of feeder load and multi-type resources to power and voltage and the demand side management of flexible load,the multi-objective optimization model of peak regulation of distribution network was constructed with the overall operation cost of distribution network,voltage deviation rate and the minimum peak-valley difference of lower network power as indexes.Finally,the improved particle swarm optimization algorithm combined with the technique for order preference by similarity to an ideal solution method(TOPSIS)was used to solve the model.The simulation results verify that the proposed strategy can effectively achieve peak load shifting and improve the system voltage distribution,and significantly improve the economy of the distribution network.