计及 DG 功率不确定性的配电网多目标无功优化

Distributed generation (DG) connects to the electricity grid, changes the trend of the distribution of the original system, and the output of the distributed generation has a strong randomness, this adversely affects system losses and node voltages, so the research of DG distribution network reactive power optimization to improve power quality and economy has important practical significance. The multi-objective probabilistic reactive power optimization model of distribution network is established in view of the influence of the output randomness of distributed power supply and load uncertainty on the system network loss and node voltage. In order to deal with the influence of the uncertain factors in the established output model on the reactive power optimization results, the probabilistic power flow is calculated by the two-point estimation method. At the same time, in order to overcome the defect of particle swarm optimization (PSO), the improved particle swarm optimization (IPSO) is used to solve the reactive power optimization model. Finally, the improved IEEE33 node system is simulated and tested. The results show that the proposed model can reduce the loss, improve the quality of node voltage, it also verifies the feasibility and fast effectiveness of IPSO algorithm.