A MILP–GIS Siting-and-Sizing Framework for Renewable-Powered Battery Swapping Station Networks with Hourly Feasibility Screening

Battery swapping stations (BSSs) can help shorten the refueling time for electric vehicles (EVs), but their widespread deployment will need decisions on station placement, battery availability, the number of chargers, photovoltaic (PV) capacity, grid import, accessibility, and emissions. This study proposes a decomposed mixed-integer linear programming and geographic information system (MILP–GIS) framework to optimize the planning of BSSs using renewable energy in a Shenzhen GIS–traffic planning case. Each year, the MILP decides on the location and size of the BSS, the PV capacity, battery packs, charging power, aggregate state of charge, reserve inventory, and grid import; a representative post-siting hourly feasibility layer then verifies the PV availability, charging power, aggregate state of charge, reserve inventory, and grid import. The case has 16 demand zones, 24 screened candidate sites and 24 hourly demand slices derived from Shenzhen GIS–traffic inputs that were aggregated into publicly releasable demand-zone and candidate-site tables. A PV investment cost is annualized once, and charging/discharging losses are taken as an electricity charging-input on the grid electricity cost side. The results demonstrate that the six-station R40 portfolio can cut the annualized cost from 49.07 to 46.78 million CNY/year compared to the case of the grid only, and grid-related CO2 emissions decrease from 22.05 to 9.92 kt/year as the renewable-energy target increases from 0% to 55%, under the base-case techno-economic assumptions. The levelized service cost (LSC) is dominated by the cost of the grid, the cost of the battery, PV capital expenditure (CAPEX), and the demand level, as revealed by the cost audit, station-level, ablation, and sensitivity results. When compared to EV infrastructure planning using benchmarking with GIS-score, p-median and energy-only baselines, the proposed framework provides a better cost–coverage–carbon trade-off in dense urban EV infrastructure planning.