Multiobjective Optimization for Sizing and Placing Electric Vehicle Charging Stations Considering Comprehensive Uncertainties

Abstract

<title>Abstract</title> The dynamic integration of electric vehicles (EVs) necessitates efficient charging infrastructure. This study introduces an algorithm using particle swarm optimization for the optimal placement and sizing of charging stations, addressing uncertainties in vehicle specifics, road and user factors, station owner considerations, and environmental impacts. Integrating a comprehensive dataset from road networks, driver behaviors, station owners, and EV manufacturers, the algorithm guides station placement to accommodate individualized charging needs. By optimizing charg er types for four distinct EV battery types, the method ensures balanced and efficient infrastructure. The cost‒benefit objective function aligns technical and environmental criteria, offering a sustainable solution for charging station planning. The results indicate that 14 fast charging stations (FCSs) are required along the studied freeway, with a total installation cost of $289,820 and an annual operation cost of $4,223,050. This infrastructure leads to annual CO₂ emissions of 1,843,572.57 kg. Stations are strategically placed to optimize coverage and minimize waiting times, with the highest number of chargers at high-traffic nodes. Forecasting individual daily load profiles demonstrates the ability of the algorithm to manage power system behavior, ensure balanced load distribution and prevent grid overload. This approach enhances user satisfaction and economic viability and reduces environmental impact, making it a valuable tool for policymakers, urban planners, and stakeholders in developing sustainable EV charging networks.