A Metaheuristic Approach for EV Charging Station Planning in Distribution Networks: Performance Comparison of PSO, GWO, and ALO

Abstract

The optimal planning of Electric Vehicle Charging Stations (EVCS) is crucial for the stable and efficient operation of power distribution networks. This study presents a comparative analysis of meta-heuristic algorithms for solving the EVCS placement and sizing problem on a 30-bus distribution system. The objective is to minimize a composite cost function that balances initial investment costs against technical performance indicators, including active power loss, reactive power loss, and voltage profile improvement. Three algorithms—Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Ant Lion Optimizer (ALO)—are evaluated and compared across nine different weight combinations for the objective function. Results demonstrate that the GWO algorithm significantly outperforms both PSO and ALO, achieving the lowest overall cost of 1,017,050 with a strategically conservative deployment of only 14 chargers. In contrast, PSO and ALO converged on higher-cost solutions (1,678,877 and 1,915,035 respectively) with excessive charger deployments (340 and 270 chargers). A sensitivity analysis on the weight parameters confirms that solutions prioritizing power loss reduction (higher w_2) yield the most favorable techno-economic outcomes. The findings conclusively establish GWO as a superior and more efficient algorithm for the EVCS planning optimization problem, offering a solution that minimizes cost while maintaining grid performance.