Open AccessA Virtual Power Plant Load Curve Clustering Method Based on Improved K-means Algorithm and Its Application
Author(s) -
Hui Li,
Luanxiao Zhao,
Peng Dong,
Zhidong Wang,
Xueying Wang,
Xin Ai
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/619/1/012055
Subject(s) - cluster analysis , principal component analysis , computer science , data mining , virtual power plant , hierarchical clustering , correlation clustering , fuzzy clustering , cluster (spacecraft) , cure data clustering algorithm , power (physics) , algorithm , artificial intelligence , distributed generation , physics , quantum mechanics , programming language
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.