Optimal Non-Anticipative All-Scenario-Feasible Scheduling Strategy for Community Microgrid With Vehicle-to-Grid Energy Storages

The rapid proliferation of electric vehicles (EVs), empowered by vehicle-to-grid (V2G) technology, provides a novel and scalable form of flexible energy storage. Integrating V2G-enabled EVs into community microgrid (C-MG) presents a promising solution to mitigate supply-demand imbalances induced by renewable energy sources. However, the optimal scheduling of C-MG with integrated V2G energy storage remains significant challenges, primarily due to modeling complexities arising from large system scale and stochastic user behaviors, as well as the non-anticipativity and all-scenario-feasibility requirements for optimal decision-making. To address these challenges, this paper proposes a multi-stage robust optimal method to obtain the non-anticipative all-scenario-feasible scheduling decisions for C-MG with V2G energy storage. Specifically, an EV aggregation model that incorporates uncertainties in charging energy demand and large-scale heterogeneous charging behaviors is given first. Then an exact aggregated robust feasible region (ARFR) for scheduling decisions is developed, which guarantees non-anticipativity and all-scenario-feasibility. Based on this ARFR, the optimal charging/discharging profiles for the charging station are determined by solving an equivalent mixed integer linear programming (MILP) problem aimed at minimizing operational costs. Comparative case studies across different methods, EV types and scaling cases confirm that the proposed method enhances stable microgrid operation under various uncertainties.