Multiobjective optimization for sizing and placing electric vehicle charging stations considering comprehensive uncertainties

Abstract

Abstract The rapid growth of electric vehicles (EVs) demands a robust and efficient charging infrastructure. To address this, we propose a particle swarm optimization algorithm designed for optimal placement and sizing of EV charging stations. This study hypothesizes that comprehensive consideration of uncertainties in vehicle types, user behaviors, road dynamics, and environmental impacts will enhance infrastructure effectiveness. Our method integrates data from road networks, driver patterns, station owners, and EV manufacturers to meet diverse charging requirements. Results indicate that 14 fast charging stations are needed along the studied freeway, with a total installation cost of $289,820 and annual operational costs of $4,223,050, leading to annual CO2 emissions of 1,843,572.57 kg. This strategic approach balances technical, environmental, and economic criteria, providing an essential tool for policymakers and urban planners in establishing sustainable EV charging networks.

Keywords

• Charging station planning
• Particle swarm optimization
• Environmental impact
• Real-world uncertainties
• Sustainable mobility