Metaheuristic algorithm for optimal allocation of electric vehicles and photovoltaics in distribution grid

Abstract

Since electric vehicles (EVs) emit less carbon dioxide, their number is rapidly increasing. As the number of EVs grow, these added loads strain the distribution grid, introducing new challenges. Key concerns for network operators include voltage fluctuations and increased power losses. Properly deploying throughout the grid, photovoltaic (PV) systems and electric vehicle charging stations (EVCS) can assist in lowering power losses and improving the bus voltage profile. A MATLAB implementation of the metaheuristic algorithm called Harris Hawk optimization (HHO) algorithm is developed to select the best locations for integrating EVCSs and PVs, with the goals of enhancing the voltage profile and reducing power losses across buses. IEEE 12-bus and 14-bus systems and real-time distribution grid data were used to test the method. For the 26-bus real-time system, the results demonstrated a notable 24% decrease in overall power loss as compared to the base case and improved voltage regulation, as indicated by a lower average voltage deviation index (AVDI) value of 0.0929. A comparative analysis was performed between optimized and random placements of EVCSs and PVs, as well as against the grey wolf optimization (GWO) algorithm. The results provide a framework for implementing solar-powered EV charging infrastructure. This can reduce costs, enhance energy reliability, and contribute to a cleaner environment.