An Enhanced Distribution System Performance with Optimization Techniques for Location of Electrical Vehicle Charging Stations

This study introduces a novel strategy for strategically placing of Electric Vehicle Charging Stations (EVCS) within a distribution power network towards minimizing power losses. The proposed methodology leverages the Grey Wolf Optimization (GWO) metaheuristic algorithm, enthused by the grey wolves hunting, to identify the most strategic locations for EVCSs. The effectiveness of the GWO-based approach is rigorously evaluated using the IEEE-33 bus system, a standard benchmark in distribution system analysis. The GWO algorithm's performance is evaluated in comparison to that of the prevalent Particle Swarm Optimization (PSO) methodology. The study analyzes system voltage levels in relation to the nominal voltage, ensuring compliance with operational limits. The results demonstrate the GWO algorithm's superiority in minimizing power losses and enhancing voltage profiles across the distribution network. Furthermore, a probabilistic assessment is conducted to evaluate the robustness of the optimal EVCS placement under uncertain EV charging patterns. This analysis considers the stochastic nature of EV charging behavior, providing insights into the strategy's resilience to real-world variations. The proposed GWO-based optimization strategy offers a significant contribution to make power distribution infrastructure more dependable and efficient in order to handle the growing number of EVs. By minimizing power losses and optimizing voltage profiles, the strategy reduces operational costs and improves the overall stability of the grid. The probabilistic assessment further strengthens the strategy's practicality by accounting for the inherent uncertainties in EV charging demands. This research provides valuable insights for distribution system operators and planners seeking to integrate EVCSs effectively while maintaining system performance.