Photovoltaic-integrated battery swapping stations: An optimization tool for operational scheduling

Electric vehicles (EVs) are essential for decarbonization but still face barriers such as long charging times and risks of grid overload. In this context, Battery Swapping Stations (BSSs) offer a faster alternative to conventional charging by replacing discharged batteries with fully charged ones, thus providing both rapid charging and flexibility for demand-side management. However, managing BSS operations, particularly when integrated with renewable energy sources such as solar power, requires advanced optimization. The charging process must be scheduled considering both the electricity price curve and the availability of renewable resources, while renewable generation itself can be managed to increase overall station revenues. This paper presents a novel Mixed-Integer Linear Programming (MILP) algorithm designed to optimize the operation of a BSS integrated with a photovoltaic (PV) power plant. The algorithm simulates yearly operations, accounting for hourly energy prices and grid constraints, with the objective of maximizing revenues by optimally scheduling post-swap battery charging and efficiently utilizing renewable energy. The tool is applied to a real-world case study in northern Italy, analysing different PV system sizes and electricity price scenarios. Results demonstrate substantial economic benefits, including up to 69 % reduction in power costs, enhanced PV self-consumption, and reduced peak grid demand. The findings highlight the practical and economic advantages of integrating PV with BSSs, offering a scalable framework for more efficient and grid-friendly EV infrastructure.