A combined approach to evaluate power quality and grid dependency by solar photovoltaic based electric vehicle charging station using hybrid optimization

This work aims to improve the power quality of the distribution system by reducing the negative impacts of electric vehicle (EV) load, such as power losses, voltage deviations and voltage unbalance factors. The voltage deviation index (VDI), voltage unbalance factor (VUF) and grid dependency for charging the EV load are minimized simultaneously for three different cases by employing a fire hawk optimization (FHO) and hybridized whale particle swarm optimization (HWPSO) algorithm. The proposed problem is tested on an unbalanced IEEE 123-bus system using MATLAB software. The uncertainty with solar power generation and EVs state of charge is modelled using the Monte Carlo simulation. Solar energy is used as the primary supply for EV charging stations (EVCSs) and relies on the grid only when the power supply from the solar photovoltaic (PV) is insufficient. The voltage stability range and critical point of the system are found by sensitivity analysis. EVCSs are optimally placed on a distribution network based on the voltage sensitivity factor. A charging-discharging algorithm is employed to reduce the quantity of energy taken from the grid by using EVs as temporary storage devices. In this work, the solar PV, distribution static synchronous compensator (DSTATCOM) is optimally placed along with EVCSs to improve the voltage profile of the distribution network. The results are compared with the previous studies, and it is found that the proposed operational approach is promising in optimizing the objective functions.