Optimized placement of electric vehicle charging stations considering sustainable utility criteria functions: An integrated GIS, MCDM, metaheuristics approach

Electric Vehicle Charging Stations (EVCS) are essential for promoting the adoption of electric vehicles (EVs) by providing infrastructure that reduces transportation-related carbon emissions. However, optimizing EVCS placement is challenging due to the need for a balanced approach considering geographic, social, and economic factors influencing user convenience, operational efficiency, and infrastructure sustainability. Existing approaches wholly or partially lack comprehensive real-world spatial parameter integration (due to significant assumptions), leading to suboptimal results. This study addresses these gaps by developing a comprehensive optimization model incorporating diverse, sustainable, spatial utility criteria—proximity to public utilities, feasibility, land use, connectivity, grid access, accessibility, and land cost—using customized utility functions and network analysis. The model is formulated as a non-linear optimization problem and employs multi-criteria decision-making (MCDM) methods CRiteria Importance Through Intercriteria Correlation and Entropy (CRITIC and Entropy), with a multi-swarm particle swarm optimization algorithm to optimize EVCS placement. Applied to a smart city case study in Bhubaneswar, India, the optimized locations demonstrate a 15–22 % improvement in utility scores over existing sites. CRITIC weighting yields a balanced distribution of high-utility locations, and Entropy favors central locations. This study underscores the value of integrating sustainable utility criteria, network analysis, MCDM, and metaheuristic optimization to support data-driven, sustainable EVCS placement for accelerating EV adoption.