Optimizing hosting capacity and spatial deployment of EV charging stations for V2G integration in zonal distribution networks

Establishing a robust network of electric vehicle charging stations (EVCSs) is a critical step in enabling convenient and reliable access to charging infrastructure, thereby accelerating the transition to sustainable transportation systems. However, the rapid expansion of EVCSs imposes additional loads on distribution networks, necessitating a thorough assessment of the hosting capacity that can be accommodated by the network without reducing its efficiency. This paper proposes a genetic algorithm-based multi-objective function to determine the optimal hosting capacity and spatial placement of EVCSs, alongside the spatial and temporal distribution and operation of V2G and their optimal power factors. By selecting optimal power factors, the proposed framework minimizes active and reactive power losses, while enhancing the system's voltage profile. The optimization objectives include minimizing total energy losses and maximizing power quality indices. Managing uncertainties in load demand and V2G operations represents significant challenges in maintaining power system stability and efficiency. Therefore, these uncertainties, along with system constraints, are carefully accounted for in the optimization process to ensure system stability and efficiency. The proposed framework is validated on benchmark distribution systems using MATLAB/Script, with regions divided into districts to simulate electric vehicles (EVs) distribution. The obtained results demonstrate the algorithm's robustness and reliability under various operational scenarios.