Efficient allocation of capacitors and vehicle-to-grid integration with electric vehicle charging stations in radial distribution networks

Electric vehicles offer zero tailpipe emissions and effectively reduce the release of harmful pollutants, which are significant contributors to air pollution and climate changes. However, the increased adoption of electric vehicles presents challenges to the power grid and could create a surge in demand characterized by fast-absorbing electrical energy. This surge can affect voltage profiles and escalate energy losses within distribution lines. This study introduces an optimization framework leveraging parallel search real-coded genetic algorithms (PSRCGA) for the efficient allocation and sizing of fast charging stations, vehicle-to-grid integration, and capacitors utilization. The optimization aims to enhance grid profitability by minimizing capacitor costs and optimizing power quality metrics through a multi-objective function. Various constraints such as power balance, bus voltage, current flow limits, and capacitor bank specifications are considered in the optimization process. By employing the PSRCGA, the study proposes a fitness function that effectively incorporates the objective function with the constraints. The proposed framework is validated through diverse scenarios applied to IEEE 33-bus and 69-bus systems, showcasing its efficacy in optimizing system performance.