Meta-heuristic Approaches to Optimal Placement of Electric Vehicle Charging Station

Abstract

Electric vehicles (EVs) have been developed to reduce the emissions of carbon dioxide (CO2) produced by vehicles with internal combustion engines. As the adoption of EVs increases, the development of a robust and efficient electric vehicle charging station (EVCS) is essential for the widespread adoption of EVs, providing the necessary infrastructure to recharge vehicle batteries. The strategic placement of an EVCS is critical, because it directly affects the efficiency and reliability of the distribution system. Properly located charging stations can enhance grid stability, optimize energy distribution, and reduce peak load pressures. Conversely, a poorly planned EVCS placement can lead to grid congestion, increased operational costs, and potential reliability issues. Strategy for maximizing EV utilization through EVCSs in the Radial Distribution Network System (RDNS) by considering factors such as load voltage deviation and line losses. In this study, the RDNS is segmented into zones, followed by the application of various optimization algorithms, including Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). This approach identifies the optimal locations for the EVCSs in the network for different load growth rates. This was followed by Forward Backward Sweep (FBS) power flow analysis to determine the active and reactive power losses, voltage deviations, and voltage profiles. Extensive simulations using IEEE 33-node test feeders validated the proposed techniques using the MATLAB tool.