Economic and Technical Aspects of Power Grids with Electric Vehicle Charge Stations, Sustainable Energies, and Compensators

The study applies the black kite algorithm (BKA), equilibrium optimizer (EO), and secretary bird optimization algorithm (SBOA) to optimize the placement of electric vehicle charge stations (EVCSs), wind turbine stations (WTSs), photovoltaic units (PVUs), and capacitor banks (CAPBs) in the IEEE 69-node distribution power grid. Three single objectives, including power loss minimization, grid power minimization, and total voltage deviation improvement, are considered. For each objective function, five scenarios are simulated under one single operation hour, including (1) place-only EVCSs; (2) place EVCSs and PVUs; (3) place EVCSs, PVUs, and CAPBs; (4) EVCSs and WTSs; and (5) EVCSs, PVUs, WTSs, and CAPBs. The results indicate that the EO can find the best solutions for the five scenarios. The results indicate that the EO and SBOA are the two powerful algorithms that can find optimal solutions for simulation cases. For one operating day, the total grid energy that is supplied to base loads and charge stations is 80,153.1 kWh, and many nodes at high load factors violate the lower limit of 0.95 pu. As for installing more renewable power sources, the energy that the base loads and charge stations need to supply from the grid is 39,713.4 kWh. As more capacitor banks are installed, the energy demand continues to be reduced to 39,578.9 kWh. The energy reduction is greater than 50% of the demand of all base loads and charge stations. Furthermore, the voltage can be significantly improved up to higher than 0.95 pu, and a few nodes at a few hours fall into the lowest range. Thus, the study concludes that the economic and technical aspects can be guaranteed for DPGs with additional installation of EVCSs.