Multi-time scale optimal scheduling strategy for microgrids considering electric vehicle aggregations and energy storage lifetime

To address the challenges posed by the increasing penetration of renewable energy and electric vehicles (EVs)—such as output fluctuations, time-varying electricity prices, and battery degradation—this study proposes a multi-timescale optimal scheduling method for microgrids that incorporates vehicle–grid interaction-based flexibility regulation, price-based demand response, and energy storage lifetime considerations. First, EVs are classified into three behavioral categories according to their charging/discharging characteristics and dispatchability. The bidirectional vehicle-to-grid (V2G) category, which is the focus of this study, is further divided into four representative classes (A–D) according to their technical parameters, and the aggregate feasible operating boundaries of the four V2G classes are derived using the Minkowski sum approach. Subsequently, a comprehensive economic model for energy storage is established, integrating charging/discharging costs, lifetime degradation costs, and residual value. By introducing a degradation term based on state-of-charge variation, the health status of the battery is explicitly incorporated into the optimization process. On this basis, a multi-timescale scheduling framework combining day-ahead optimization and real-time model predictive control is constructed to dynamically respond to uncertainties in renewable generation, EV behavior, and load fluctuations. Simulation results demonstrate that the proposed method effectively guides EV clusters to charge during low-price periods and discharge during high-price periods, significantly smoothing power fluctuations. Meanwhile, it reduces frequent deep cycling of energy storage systems, thereby slowing battery aging and improving overall economic performance. The findings confirm that the proposed method substantially enhances the flexibility, economic efficiency, and sustainability of microgrid operation under uncertain conditions.