Genetic Algorithm-Based Optimal EV Charging Station and V2G Allocation with Power Factor Control

The worldwide shift toward electric vehicles (EVs) aims to mitigate climate change and reduce pollution. Advancements in battery technology have significantly boosted the EV industry, accelerating the transition from hybrid to fully electric vehicles. To sustain this growth, the widespread availability of EV charging stations (EVCS) is essential. However, integrating EVCS into the distribution network introduces challenges, including potential power system constraints and quality standards violations. This study proposes an optimal approach for integrating EVCS into distribution systems while adhering to power quality constraints, such as minimizing energy losses, enhancing voltage stability, and improving the system power factor. A Genetic Algorithm is employed to define the optimal locations for EVCS, Vehicle-to-Grid (V2G) allocation, and V2G power factor. Simulations are conducted using the radial system IEEE-33 bus, along with load curves and EV charging/discharging profiles, demonstrating the proposed approach's effectiveness in improving voltage stability and reducing energy losses.