Optimizing day-ahead EV scheduling across multiple charging stations with an interrupted-charging scheme

Globally, the expansion of charging infrastructure has lagged behind the rapid adoption of electric vehicles (EVs), causing inconvenience to users due to limited charging station availability. To address this issue, this study proposes a day-ahead scheduling strategy for charging service providers (CSPs) managing multiple stations within their jurisdictions. The strategy optimally assigns charging demands to stations and establishes charging schedules while considering battery degradation and existing infrastructure through an interrupted-charging scheme. We formulate this problem as a mixed-integer programming model to minimize operational costs consisting of charging, shortage, and assignment costs. To enhance scalability, a decomposition method based on column generation is developed to obtain high-quality solutions efficiently. A case study inspired by the regional characteristics of South Korea validates the strategy’s effectiveness. Compared with benchmark strategies, the proposed approach achieves lower operational costs while maintaining high charging completion rates in both day-ahead scheduling and even in real-time operation where actual demand deviates from day-ahead estimates. Furthermore, the results demonstrated the effectiveness of the proposed decomposition method for large-scale instances.