Multi-Objective Sizing Method for PV-BESS Integration with EV Charging Stations and Analysis Across Different Parking Scenarios

The growing adoption of electric vehicles (EVs) is driving the expansion of charging networks. Local photovoltaic (PV) systems combined with battery energy storage systems (BESS) have emerged as promising solutions to mitigate the impact of charging demand on the grid and reduce the environmental impact of EV charging. In this context, proper sizing of PV-BESS systems is crucial to maximize their integration with charging hubs (CHs) and ensure optimal performance. This paper proposes a multi-objective sizing method to optimize the energy and economic performance of PV-BESS systems in EV charging hubs. Sizing optimization is performed using a Non-Dominated Sorting Genetic Algorithm-II. The method is applied to four CH scenarios characterized by variations in energy demand, user behavior, and location. Results indicate that while optimal PV size remains relatively consistent across scenarios, the ideal BESS configuration varies with each scenario’s characteristics. Optimized PV-BESS integration significantly improves energy performance, increasing system self-sufficiency by up to +72%. From an economic point of view, results show that in some cases, smaller BESS capacities are more advantageous due to lower capital costs, while in others, larger BESS sizes reduce overall costs by up to −50%, significantly cutting utility expenses despite higher initial investment.