V2G-enhanced operation optimization strategy for EV charging station with photovoltaic and energy storage integration

• A day-ahead and intra-day integrated optimization framework for the operation of the charging station is provided. • TDRO-based planning achieves within 0.1% economic loss, balancing economy and uncertainty better than TSRO/TSO. • MPC real-time scheduling controls intra-day randomness while tracking the day-ahead plan with errors constrained within 5%. • V2G-enhanced optimization yields an 23.81 % reduction in peak load, nearly double the ES-only performance. The integration of renewable energy and energy storage in electric vehicle (EV) charging stations offers broad application prospects. With the development of Vehicle-to-Grid (V2G), designing an economic, efficient, and safe operation strategy for such integrated microgrids has become a key issue in promoting their sustainable development. Considering the uncertainty of photovoltaic (PV) generation and the randomness of intra-day load fluctuations, this study proposes an optimal day-ahead and intra-day operation framework for the charging station with integrated energy sources, realizing economic operation while reducing load peaks and smoothing power fluctuations. First, the microgrid model and optimization architecture of the integrated energy system are introduced, including a method for estimating PV output uncertainty. Second, considering economic objectives and PV forecast errors, a day-ahead power purchase planning model based on Two-stage Distributionally Robust Optimization (TDRO) is developed. To handle intra-day randomness, a real-time intra-day optimization scheduling method for the charging station based on Model Predictive Control (MPC) is established. Through rolling optimization and correction, this approach tracks the day-ahead power plan and optimizes the dispatch for energy storage and V2G in real-time. Finally, case studies based on an actual EV charging station located in Shanghai validate the effectiveness of the proposed methodology.