Abstract: The construction of energy internet integrates the Internet of Things， artificial intelligence， cloud computing and other technologies into the power grid. As the basic unit of energy internet， virtual power plant (VPP) will change its aggregation and operation mode. In view of how virtual power plants can effectively participate in power grid operation， this paper proposes a VPP load curve clustering method based on principal-component dimension-reduced analysis， aggregation level clustering and k-means clustering， and studies the application of the clustering results. 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 VPP 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 VPP aggregation.

Key words: virtual power plant, load curve , principal component analysis, k-means algorithm, hierarchical clustering, comprehensive assessment