Day-ahead electric vehicle charging behavior forecasting and schedulable capacity calculation for electric vehicle parking lot

Xu, Maosheng, Gao, Shan, Zheng, Junyi, Huang, Xueliang and Wu, Chuanshen 2024. Day-ahead electric vehicle charging behavior forecasting and schedulable capacity calculation for electric vehicle parking lot. Energy 309 , 133090. 10.1016/j.energy.2024.133090

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Abstract

The Electric Vehicle Parking Lot (EVPL) aims to address the growing demand for EV charging infrastructure. EVs connected to EVPLs have extended access times compared to those at public charging stations. Consequently, EVPL owners can aggregate the schedulable capacity of connected EVs to participate in day-ahead and ancillary service markets, thereby gaining economic benefits. However, achieving this objective is hindered by the lack of accurate day-ahead forecasts of EV charging behavior. To address this issue, this paper introduces a novel day-ahead forecasting method for EV charging behavior at EVPLs, alongside a strategy for calculating EV schedulable capacity based on these forecasts. Unlike existing methods, this paper presents a day-ahead time-of-use clustering forecasting strategy, which provides more detailed and accurate predictions of EV charging behavior, eliminating the need for numerous assumptions during schedulable capacity calculations. The study demonstrates that the proposed method, validated using actual historical data, enables precise forecasting of EV charging behavior. Furthermore, the proposed day-ahead schedulable capacity calculation strategy is shown to be both effective and practical.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by-nc-nd/4.0/, Start Date: 2026-09-12
Publisher:	Elsevier
ISSN:	0360-5442
Date of Acceptance:	2 September 2024
Last Modified:	16 Sep 2024 15:45
URI:	https://orca.cardiff.ac.uk/id/eprint/172146

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