基于改进k-means算法的VPP负荷曲线聚类方法及应用

能源互联网的建设,将物联网、人工智能、云计算等技术融入电网。虚拟电厂作为能源互联网的基本单元,其聚合、运行方式也将迎来改变。针对虚拟电厂如何有效参与电网运行,提出一种基于主成分分析降维和凝聚层次聚类与k-means聚类相结合的虚拟电厂负荷曲线聚类方法,并对聚类结果的应用进行了研究。首先,结合信息物理网络所获数据,采用主成分分析方法对参与虚拟电厂聚合的不同负荷的特征进行分析,对数据进行标准化处理并降低维度;然后,利用凝聚层次聚类和k-means聚类相结合的算法,对所有参与聚合的负荷出力曲线进行聚类,得到同类别的负荷曲线簇并找出聚类中心;最后,分析聚类结果,建立与之匹配的评价体系,通过综合评价选取合适的负荷组合参与虚拟电厂聚合。

A Load Curve Clustering Method Based on Improved K-means Algorithm for Virtual Power Plant and Its Application

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.