A novel forecasting approach to schedule aggregated electric vehicle charging

作     者：Nico Brinkel Lennard Visser Wilfried van Sark Tarek AlSkaif 

作者机构：Copernicus Institute of Sustainable DevelopmentUtrecht UniversityPrincetonlaan 8aUtrecht3584 CBThe Netherlands Information Technology Group(INF)Wageningen University and Research(WUR)Hollandseweg 1Wageningen6706 KNThe Netherlands 

出 版 物：《Energy and AI》 (能源与人工智能（英文）)

年 卷 期：2023年第14卷第4期

页      面：522-535页

核心收录：

学科分类：08[工学] 0807[工学-动力工程及工程热物理] 

基　　金：Ministerie van Economische Zaken en Klimaat, EZK Ministerie van Binnenlandse Zaken en Koninkrijksrelaties, BZK, (MOOI32014) Ministerie van Binnenlandse Zaken en Koninkrijksrelaties, BZK 

主　　题：Forecasting Electric vehicle smart charging Electric vehicle aggregation Virtual battery method 

摘      要：To be able to schedule the charging demand of an electric vehicle fleet using smart charging,insight is required into different charging session characteristics of the considered fleet,including the number of charging sessions,their charging demand and arrival and departure *** use of forecasting techniques can reduce the uncertainty about these charging session characteristics,but since these characteristics are interrelated,this is not ***,forecasting frameworks that cover all required characteristics to schedule the charging of an electric vehicle fleet are absent in scientific *** cover this gap,this study proposes a novel approach for forecasting the charging requirements of an electric vehicle fleet,which can be used as input to schedule their aggregated charging *** the first step of this approach,the charging session characteristics of an electric vehicle fleet are translated to three parameter values that describe a virtual ***,optimal predictor variable and hyperparameter sets are *** serve as input for the last step,in which the virtual battery parameter values are *** approach has been tested on a real-world case study of public charging stations,considering a high number of predictor variables and different forecasting models(Multivariate Linear Regression,Random Forest,Artificial Neural Network and k-Nearest Neighbors).The results show that the different virtual battery parameters can be forecasted with high accuracy,reaching R^(2) scores up to 0.98 when considering 400 charging *** addition,the results indicate that the forecasting performance of all considered models is somehow similar and that only a low number of predictor variables are required to adequately forecast aggregated electric vehicle charging characteristics.