Multi-objective optimization framework for strategic placement of electric vehicle charging stations and shunt capacitors in a distribution network considering traffic flow

B.Vinod Kumar and Aneesa Farhan M.A.

Applied Energy, 2025, vol. 397, issue C, No S0306261925010141

Abstract: This study aims to develop a comprehensive multi-objective planning framework for the optimal placement of Electric Vehicle Charging Stations (EVCS) and Shunt Capacitors (SCs) in the distribution network (DN) while accounting for transportation network (TN) constraints. The integration of EVCS into DN, though essential for promoting electric vehicle (EV) adoption, can result in voltage deviations and power losses. To address these challenges, a Traffic Flow Capturing model is employed to maximize EV traffic flow and minimize active power loss (APL) and voltage deviation (VD). A novel hybrid metaheuristic algorithm (HGPC), combining Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Cuckoo Search Optimization (CO), is proposed to solve the multi-objective problem. The framework is validated on a 33-node DN and 25-node TN across three planning scenarios: DN-focused, TN-focused, and integrated TN-DN. In the DN-focused scenario, the objective is to minimize power loss, whereas in the TN-focused scenario, the goal is to maximize EV flow. In the integrated planning case, with battery constraints, the model achieves 155.05 kW APL and 41.54 % EV flow; without constraints, it improves to 148.14 kW APL and 57.52 % EV flow. These results confirm the effectiveness of the proposed HGPC algorithm, suggesting greater flexibility in handling conflicting multi-objective functions and highlighting the benefits of integrated planning for sustainable EV infrastructure.

Keywords: Distribution network; Electric vehicle charging station; Multi-objective optimization; Normalization; Traffic flow capturing; Transportation network (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.126284

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