Research on Output of a Wind Farm Considering Wind Resources

Abstract

The output of renewable power generation and its resources and environment are characterized by complex correlation in respect to time and space. The paper analyzes the spatiotemporal correlation of renewable energy under different resources and environmental conditions and builds differentiated output models of renewable energy power generation. Firstly， principal component analysis （PCA） is used to determine the principal components of the renewable energy sources in new energy stations. It combines with the natural output characteristic indexes that characterize the operating characteristics of new energy stations to generate their characteristics data. The improved K-means algorithm clusters the new energy stations to obtain the new energy generation clusters with the spatiotemporal correlation of resources. Secondly， based on the historical output data of each new energy station in the cluster， a new energy differential output model for different environmental conditions is constructed by cluster analysis. Finally， a simulation analysis is conducted using wind farms' actual environmental and output monitoring data in southeast China. The results demonstrate that the proposed method has improved the correlation between the output characteristics of new energy generation clusters and the error level of the constructed differentiated output model is higher than that of the typical daily method.

Keywords

• renewable energy power generation
• resources correlation
• principal component analysis
• clustering algorithm
• output characteristics