Multi-timescale stochastic optimization for enhanced dispatching and operational efficiency of electric vehicle photovoltaic charging stations

• Proposed a multi-time scale stochastic strategy for PV-EV charging station dispatch. • Integrates day-ahead and real-time optimization to handle forecast uncertainties. • Minimizes operational costs by dynamic scheduling of energy storage systems. • Enhances resilience and reliability of PV-powered EV charging infrastructures. • Demonstrates reduced forecast error and improved cost-efficiency via simulation. Addressing the integration of global day-ahead dispatching and the necessity for real-time dispatch precision, this study proposes a novel multi-timescale stochastic dispatch strategy for photovoltaic (PV) charging stations equipped with energy storage systems. Initially, the dispatch center optimizes the energy storage system’s charging status using reduced scenario forecast data to minimize operational costs, considering uncertainties in PV power generation and charging demand. As the day progresses, this strategy dynamically updates forecasts for PV power and charging loads based on real-time data, enabling ongoing optimization of the storage system to reduce operational costs. The method strategically schedules charging and discharging activities, effectively diminishing daily operational expenses. Simulation results show that the proposed method reduces forecast errors, lowers operational costs, enhances resilience, and reliably meets electric vehicle charging demand, presenting a robust solution for future energy dispatch challenges.