A Virtual Power Plant Load Curve Clustering Method Based on Improved K-means Algorithm and Its Application. Issue 1 (December 2020)

• Record Type: Journal Article
• Authors: Li, Hui; Zhao, Lang; Peng, Dong; Wang, Zhidong; Wang, Xueying; Ai, Xin
• Is Part Of: IOP conference series. Volume 619:Issue 1(2020)
• Publication Date: 2020-12
• Subjects: Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/619/1/012055
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: In view of how virtual power plants can effectively participate in power grid operation, a method of load curve clustering of virtual power plants based on principal component analysis reduction and aggregation level clustering and k-means clustering is proposed, and the application of clustering results is studied. Firstly, combined with the data obtained from the information physical network, the principal component analysis method is adopted to analyze the characteristics of different loads participating in the virtual power plant aggregation, so as to standardize the data and reduce the dimension. Then, the algorithm combining aggregation hierarchical clustering and k-means clustering is used to cluster all load output curves participating in the aggregation, to obtain load curve clusters of the same class and find out the clustering center. Finally, the clustering results are analyzed, and the corresponding evaluation system is established. Through comprehensive evaluation, appropriate load combinations are selected to participate in the virtual power plant aggregation.