Techno-Economic Optimization of Grid Connected Electric Vehicle Charging Station: A Case Study of Riyadh, Saudi Arabia

This research evaluates the integration of renewable energy sources with grid-connected systems to power electric vehicle charging stations (EVCSs) in Riyadh, Kingdom of Saudi Arabia (KSA), underscoring its importance for sustainable development. An eco-friendly EVCS model is proposed and apply a novel metaheuristic optimization technique, the Enhanced Bald Eagle Search Algorithm (EBESA), to determine the optimal sizing of system components. Our objective is to minimize the total net present cost (TNPC) and the levelized cost of energy (LCOE) while ensuring reliability as quantified by the loss of power supply probability (LPSP). The performance of the EBESA is compared with alternative algorithms such as the Bald Eagle Search Algorithm (BESA), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO), assessing their efficacy in system component sizing. Our findings indicate that the optimal EVCS configuration is a solar photovoltaic/wind turbine (PV/WT) grid-tied system, achieving a LCOE of $0.0796/kWh, a TNPC of $102,042. our study aims to guide policy-making and investment in the expansion of EV charging infrastructure, with broader implications for developing countries.