Multi-Objective Genetic Algorithm for Charging Station Capacity and Location Optimization

This paper presents a multi-objective optimization model for determining charging station capacity and location, aiming to maximize revenue, minimize waiting probability, and reduce construction and maintenance costs. It uses a genetic algorithm to balance these objectives, ensuring practicality and efficiency. The model also incorporates maximizing the coverage area for location simulation. Applied to Shanghai's Pudong New Area, the region is divided into sub-districts, and necessary parameters are determined. The proposed algorithm plans and sites charging and battery-swapping stations, determining their layout and quantity. This provides practical references for planning and constructing new energy vehicle charging infrastructure, thereby enhancing the accessibility and efficiency of charging facilities. The research provides a scientific foundation for optimizing electric vehicle charging infrastructure, promoting the sustainable development of the electric vehicle ecosystem, and facilitating the widespread adoption of electric vehicles.