Coordinated Scheduling of EV Charging Service and Energy Arbitrage for Truck Mobile Charging Stations

作者：Kecheng He, Hongjie Jia, Yunfei Mu, Xiaodan Yu, Yue Zhou, Wei Gan, Jianzhong Wu · 发表于：IEEE Transactions on Smart Grid · 年份：2025 · DOI：10.1109/tsg.2025.3571790 · 被引用次数：10 · 研究领域：Electric Vehicles and Infrastructure、Advanced Battery Technologies Research、Electric and Hybrid Vehicle Technologies

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 twostage 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.