Coordinated scheduling of EV charging service and energy arbitrage for truck mobile charging stations

He, Kecheng, Jia, Hongjie, Mu, Yunfei, Yu, Xiaodan, Zhou, Yue, Gan, Wei and Wu, Jianzhong ORCID: https://orcid.org/0000-0001-7928-3602 2026. Coordinated scheduling of EV charging service and energy arbitrage for truck mobile charging stations. Presented at: 2026 IEEE PES International Meeting (PES IM), Hong Kong, Hong Kong, 18-21 January 2026. Proceedings of 2026 IEEE PES International Meeting (PES IM). IEEE, 10.1109/PESIM67009.2026.11438985

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Abstract

Truck mobile charging stations (TMCS) have emerged as a promising complement to fixed charging stations (FCS), sparking increased interest in recent years. However, the potential applications of TMCS have yet to be fully explored, which impedes its economic feasibility and widespread adoption. Therefore, this paper aims to address this gap by proposing a two-stage scheduling model for TMCS, focusing on coordinating its operation between electric vehicle (EV) charging services and energy arbitrage. In this model, an EV charging demand generation model is formulated to capture the charging behavior of EV users between FCS and TMCS. Additionally, an extended graph model is developed to depict the dynamic characteristics between different service provisions of TMCS. To account for the impact of EV charging demand uncertainty on operator profit, a two-stage optimal scheduling model based on lookahead rolling horizon-value function approximation (LRH-VFA) is established. By offline learning from historical data, the impact of current decisions on future periods is considered. Subsequently, using short-term forecast data and real-time information, the online optimization results are rolling updated and outputted. Numerical studies demonstrate that the proposed method can effectively enhance the utilization and economy of TMCS.

Item Type:	Conference or Workshop Item - published (Paper)
Date Type:	Published Online
Status:	In Press
Schools:	Schools > Engineering
Publisher:	IEEE
ISBN:	9798331566456
Last Modified:	30 Mar 2026 11:30
URI:	https://orca.cardiff.ac.uk/id/eprint/186079

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