Optimal Siting and Sizing of EV Charging Stations Using Fuzzy Logic and Multi-Objective Optimization

Abstract

This paper presents an innovative approach that combines fuzzy logic with a multi-objective mixed-integer linear programming (MILP) model to address the challenges of optimal planning for electric vehicle charging stations (EVCS). The proposed method integrates fuzzy logic to evaluate potential locations based on various geospatial, economic, and griddependence factors. This allows for a more flexible and accurate ranking of candidate sites, ensuring robust decisions for the initial deployment of EVCSs. The MILP model then optimizes multiple objectives, including minimizing installation and maintenance costs, reducing the distance between EVCSs, and aligning with electric vehicle user preferences. Additionally, the model incorporates constraints on user waiting times, ensuring an efficient and functional charging experience. By simultaneously considering these aspects, the approach offers a comprehensive solution that enhances decision-making processes for urban electric mobility planning. The method is scalable and adaptable, making it suitable for dynamic and evolving urban environments. This approach not only improves operational efficiency but also contributes to reducing costs and supporting the widespread adoption of electric vehicles. The proposed solution is expected to provide valuable insights for policymakers, urban planners, and other stakeholders involved in developing sustainable and efficient EVCS network.