Optimal location selection for electric vehicle charging stations using an enhanced Analytic Hierarchy Process

Optimal site selection for electric vehicle (EV) charging stations is critical for promoting EV adoption but remains challenging, particularly in densely populated urban settings. This study proposes an enhanced Analytic Hierarchy Process (AHP)-based approach integrating geographic and socioeconomic factors, addressing the limitations of traditional methods that often neglect practical constraints and data scarcity. Using real-world geographic (latitude and longitude) data and urban rental market insights from Chengdu, China, we systematically identify candidate locations. Our results show Xiangyue Lake as the optimal site, validated through load simulations confirming effective local power supply. This research provides a practical and robust decision-support framework for urban planners aiming to strategically deploy EV infrastructure.