EV Charging Station Location and Capacity Planning Scheme Based on Voronoi Diagram and Catfish Particle Swarm Optimization

To address the lagging construction and irrational spatial distribution of current electric vehicle (EV) charging infrastructure, scientific location and capacity planning has emerged as a critical research focus in transportation electrification. Through a systematic review of domestic and international literature, this paper analyzes the evolution of charging station planning models from single economic indicators to multi-objective frameworks incorporating grid constraints, carbon emission benefits, and user behavior. Research indicates that while geometric spatial partitioning and swarm intelligence algorithms are widely utilized, existing methods face limitations in handling iterative spatial service area matching and overcoming the premature convergence of standard Particle Swarm Optimization (PSO). Consequently, this study proposes an integrated technical route utilizing Voronoi diagrams to adaptively partition service areas based on demand density, and constructing a comprehensive model encompassing construction and maintenance costs, environmental costs, and generalized user costs. To solve this highly complex spatial allocation problem, a Catfish Particle Swarm Optimization (CPSO) algorithm is employed as an efficient computational tool. Ultimately, this approach aims to provide practical, quantitative decision support for urban EV charging network planning by balancing the conflicting interests of operators, users, and the power grid within a comprehensive ‘Total Social Cost’ framework.