Impact of Optimal Electric Vehicle Charging Station Placement on Distribution Network Performance: A Comparative Study

The integration of Electric Vehicle Charging Stations (EVCSs) into distribution networks presents significant challenges, including increased power losses and voltage instability. This paper presents an optimization framework for the optimal placement of EVCSs in radial distribution systems with existing distributed generation. The objective is to minimize total active power loss over a 24-hour period while adhering to operational constraints, including bus voltage limits and line thermal capacities. A deterministic Non-Linear Programming (NLP) model is formulated and solved using the Interior Point Optimizer (IPOPT). The methodology is validated on the IEEE 33-bus and 69-bus networks, which incorporate fixed photovoltaic and biomass generation units. The proposed deterministic approach successfully identified optimal EVCS locations that significantly reduce system-wide power losses and improve voltage stability. To validate its reliability, the proposed method is benchmarked against three popular metaheuristic algorithms: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Grey Wolf Optimizer (GWO). The results demonstrate that while the deterministic NLP framework entails a higher computational cost, it consistently outperforms all three metaheuristic methods in solution quality. This advantage is particularly pronounced in the larger 69-bus network, where the NLP model achieved a 47.15% reduction in power losses, whereas the metaheuristic algorithms failed to exceed 7.1% due to premature convergence. These findings indicate that, for standard radial distribution systems, the deterministic optimization framework offers a superior trade-off between solution optimality and reliability compared to stochastic metaheuristics, albeit at a higher computational cost. While the NLP model guarantees convergence to a high-quality solution, the study highlights the importance of selecting the appropriate optimization strategy based on the specific network size and available computational resources.