Optimal Reactive Optimization of Distribution Network with Wind Turbines Based on Improved NSGA-II

Abstract

Considering the uncertainties of wind turbines output and the random fluctuations of load, and the correlation of multi-wind turbine output and load correlation, this paper first uses Latin hypercube sampling to generate multiple scenarios, and then uses K-means clustering to generate several typical scenarios. For K-means clustering, it is not possible to determine the optimal number of clusters based on the characteristics of wind power output data and load data distribution. The clustering validity index is used to determine the optimal number of clusters. The improved non-dominated sorting genetic algorithm based on local differential method was used to solve the model, and selected the best compromise solution from the Pareto optimal solution set according to fuzzy membership degree. and finally the simulation was performed in the improved IEEE 33-bus power distribution network. The results prove that the reactive power optimization can effectively improve the voltage level of the distribution network and reduce the network loss.