虚拟电厂调节能力评估与典型运行模式用户画像方法OA

The regulation capability of virtual power plants(VPPs)exhibits stochastic and challenging-to-quantify due to their aggregation of diverse flexible resources.The data-driven approaches for assessing the adjustable potential of VPPs have become increasingly critical.Based on real-world engineering data,this study first conducts clustering analysis using the improved Canopy and DTW-Kmeans++algorithms,thereby identifying the typical operational modes of VPPs.Secondly,to characterize the regulation performance and typical operational patterns of VPPs,an evaluation framework is established across three dimensions:intrinsic performance metrics,grid interaction parameters,and user behavioral characteristics.Finally,to assess the regulatory potential of newly accessed users,a user profiling methodology based on collaborative filtering is proposed.It employs silhouette coefficients and weighted Pearson correlation coefficients to achieve a precise characterization of regulation performance for new users.It aims to address practical challenges,such as the lack of historical operational records for new users in real-world business scenarios including VPP grid-connection testing and evaluation.A case study demonstrates the feasibility of applying this method to evaluate practical engineering data,effectively constructing multi-type and differentiated user profiles for VPPs.