Modeling and Simulation of Large-Scale Wind Power Base Output Considering the Clustering Characteristics and Correlation of Wind Farms

The rapid development of renewable energy improves the requirements of renewable energy output simulation. The clustering characteristics and correlation of renewable energy would improve the accuracy of power output simulation. To clarify the typical power output process of a large-scale wind power base, a novel method is proposed for wind power output scene simulation in this paper. Firstly, the genetic algorithm (GA) Kmeans is used to divide the wind farm clusters. The wind power output of each cluster is calculated by the wind turbine model. Then, the Copula principle is used to describe the correlation characteristic of wind farm clusters. Finally, the power output scenes are simulated by the Markov chain Monte Carlo (MCMC) method. To verify the effectiveness of proposed method, the wind power base in the downstream Yalong River basin is taken as the case study. The results show that the 65 wind farms should be divided into 6 clusters. The five typical power output scenes in winter-spring and summer-autumn seasons are simulated respectively based on the clustering characteristics and correlation of wind farms. This study provides a valuable reference for other large-scale renewable power bases all over the world.