A GIS MILP framework for electric vehicle charging station placement and optimization: A Case Study in Eindhoven, Netherlands

Electric vehicle charging station planning requires decisions that account not only for spatial accessibility, but also for investment costs, charging demand, renewable energy availability, grid energy use, and storage requirements. Existing EV charging station location studies often address these components separately, limiting their ability to evaluate trade-offs between spatial suitability and system-level operation. This study develops an integrated GIS-MILP framework for electric vehicle charging station planning. The framework combines GIS-based spatial suitability analysis with a multi-objective mixed-integer linear programming model to optimize charging station location, energy flows, travel time, investment costs, grid energy use, battery storage, and vehicle-to-grid interactions. The approach is applied to Eindhoven, the Netherlands, using spatial indicators, seasonal solar generation profiles, charging demand, candidate locations, and infrastructure cost parameters. Results show that integrating spatial suitability with operational optimization supports the identification of accessible charging station locations while accounting for renewable variability, storage capacity, and grid energy requirements. The inclusion of PV, battery storage, and V2G enables the model to evaluate how local renewable generation and bidirectional energy flows may contribute to charging infrastructure planning, although their contribution depends on modeled assumptions and system conditions. The proposed framework provides a transferable decision-support approach for cities seeking to plan EV charging infrastructure under spatial, economic, and energy-system constraints. • Dense roads and competing land uses reduce suitability for EV charging station placement. • A 4% increase in investment yields notable reductions in user travel time. • Cost and time runs yield similar spatial patterns but vary in station density. • The optimized network adds most stations within 200 m of existing sites.