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Robust charging Demand Prediction and charging Network Planning for Heterogeneous behavior of Electric Vehicles

Journal of Shanghai Jiaotong university(Science) 2023年第1期28卷 136-149页

作者：张轶伦徐思坤徐捷曾学奇李铮谢驰 Department of Industrial Engineering and Management Shanghai Jiao Tong UniversityShanghai 200240China Olin Business School Washington University in St.LouisSt.LouisMO 63130USA Urban Mobility Institute Tongji UniversityShanghai 201804China Antai College of Economics and Management Shanghai Jiao Tong UniversityShanghai 200030China College of Transportation Engineering Tongji UniversityShanghai 201804China

This study addresses a new charging station network planning problem for smart connected electric vehicles.We embed a charging station choice model into a charging network planning model that explicitly considers the heterogeneity of the charging behavior in a data-driven manner.To cope with the deficiencies from a small size and sparse behavioral data,we propose a robust charging demand prediction method that can significantly reduce the impact of sample errors and missing data.On the basis of these two building blocks,we form and solve a new optimal charging station location and capacity problem by minimizing the construction and charging costs while considering the charging service level,construction budget,and limit to the number of chargers.We use a case study of planning charging stations in Shanghai to validate our contributions and provide managerial insight in this area.

关键词： electric vehicle charging network planning charging behavior robust demand prediction

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Operation-area-constrained Adaptive Primary Frequency Support Strategy for Electric Vehicle Clusters

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Journal of Modern Power Systems and Clean Energy 2023年第6期11卷 1982-1994页

作者： Tianqi Liu Pengyu Wang Qiao Peng Min Zhang Tengxin Wang Jinhao Meng College of Electrical Engineering Sichuan UniversityChengdu 610065China Electric Power Research Institute State Grid Shanxi Electric Power CompanyTaiyuan 030001China

Due to their fast response and strong short-term power throughput capacity, electric vehicles(EVs) are promising for providing primary frequency support to power grids. However, due to the complicated charging demands of drivers, it is challenging to efficiently utilize the regulation capacity of EV clusters for providing stable primary frequency support to the power grid. Accordingly, this paper proposes an adaptive primary frequency support strategy for EV clusters constrained by the charging-behavior-defined operation area. First, the forced charging boundary of the EV is determined according to the driver's charging behavior, and based on this, the operation area is defined. This ensures full utilization of the available frequency support capacity of the EV. An adaptive primary frequency support strategy of EV clusters is then proposed. The output power of EV is adaptively regulated according to the real-time distance from the EV operating point to the forced charging boundary. With the proposed strategy, when the EV approaches the forced charging boundary, its output power is gradually reduced to zero. Then, the rapid state-of-charge declines of EVs and sudden output power reductions in EV clusters caused by forced charging to meet the driver's charging demands can be effectively avoided. EV clusters can then provide sustainable frequency support to the power grid without violating the driver's charging demands. Simulation results validate the proposed operation-area-constrained adaptive primary frequency support strategy, which outperforms the average strategy in terms of stable output maintenance and the optimal utilization of regulation capacities of EV clusters.

关键词： Primary frequency control frequency support electric vehicle vehicle-to-grid(V2G) operation area charging behavior

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