Holistic optimization of electric vehicle charging stations in radial power systems with V2G and DG integration considering fault repairing periods

Adopting electric vehicles is pivotal for mitigating environmental impacts and enhancing energy sustainability by reducing emissions and fossil fuel reliance in transportation systems. The integration of electric vehicles significantly impacts power grids and increases complexity but offers opportunities for grid stabilization through vehicle-to-grid (V2G) technology. Grid performance can be improved by integrating renewable energy sources (RESs) and reactive power compensation devices. This paper presents a proposed multi-objective function solved using parallel search real-coded genetic algorithm (PSRCGA) for obtaining the optimal placement of electric vehicle charging stations (EVCSs), V2G sharing points, photovoltaic systems (PV), wind farms, and capacitor banks. The multi-objective function aims to minimize the cost function and total energy and maximize power quality indices. The power quality indices are improved within constraints including the voltage stability and voltage deviation index. The constraints are merged with the multi-objective function within a fitness function, implemented considering fault repairing periods (FRP). Load flow analysis is applied on the IEEE 33-bus radial test system and the algorithm is verified on a real system. The system is divided into various zones to depict streets/regions/population distribution. The results affirm the algorithm's reliability across different scenarios and system configurations. Integrating RESs of 40 % in the IEEE 33-bus system decreases the annual active energy loss to 575.40 MWh with simultaneously inserting capacitor banks of 43.24 % reduces the reactive energy loss to 415.10 Mvarh. The system minimum voltage is maintained at 0.903 pu during system reconfiguration in FRP, which is within the allowable voltage limits