Optimal Scheduling Strategy for Mobile Charging Stations Considering Long and Short-term Uncertainties

Trunk Mobile charging stations (TMCS) have garnered significant attention domestically and internationally due to their excellent mobility and flexible scheduling capabilities. However, their potential application value has not been fully explored. This paper proposes a two-stage collaborative optimization scheduling strategy for TMCS to coordinate their operation in providing charging services for electric vehicles (EVs) and engaging in energy arbitrage with the power grid. In the day-ahead optimization phase, the growth curve function simulates the future development trend of EVs, and a stochastic optimization method determines the collaborative operation plan of TMCS. In the real-time scheduling phase, the chance-constrained method adjusts and updates the TMCS schedule based on real-time traffic and power operation data, resulting in an optimized TMCS scheduling outcome considering short-term uncertainty. Case analysis shows that the proposed method effectively addresses the impact of long- and short-term uncertainties on the TMCS scheduling plan, ensuring the quality of charging services while guaranteeing the profitability of charging facility operators.