1.Research Center for Photovoltaic Systems Engineering of Ministry of Education, Hefei University of Technology, Hefei 230009, China;2.State Grid Anhui Electric Power Co., Ltd., Hefei 230000, China;3.State Grid Anhui Electric Vehicle Service Co., Ltd., Hefei 230000, China;4.National Technical University of Athens, Athens 15780, Greece

The aggregation schedulable capacity (ASC) of large-scale electric vehicles (EVs) is one of the important technical indicators for virtual power plant containing EVs to participate in multi-level and multi-scenario power balance auxiliary services. However,the existing ASC model of EVs is difficult to adapt to the interactive scenario between large-scale EVs and provincial power dispatching center. Therefore,from the new perspective of multi-level and multi-scenario regulation for peak-shaving, frequency regutalion, and voltage regulation of power systems,this paper proposes a dual-layer clustering modeling method for ASC of EVs based on data-driven and machine learning. By constructing the individual schedulable capacity model of generalized EV-charging pile energy storage unit,this method combines the density space-based clustering algorithm and the improved self-organizing map deep clustering algorithm,which effectively integrates the time distribution of electric quantity of EVs and the spatial distribution characteristics of charging piles,and constructs the ASC model for multi-scenario regulation of peak shaving,frequency regulation and voltage regulation. The proposed method is verified by actual charging records in a province of China, and various charging profiles such as “morning type”, “noon type” and “evening type” are obtained. The self-aggregation of generalized energy storage system with different spatial and temporal distributions of EVs is realized, and the potential evaluation of provincial-level EVs participating in different auxiliary services of power grid is realized. The data foundation is laid for the prediction of ASC.