Optimal electric vehicle charging and discharging scheduling using metaheuristic algorithms: V2G approach for cost reduction and grid support

The adoption of Electric Vehicles (EVs) in the transportation sector is expected to grow significantly in the coming few years. While EVs offer numerous benefits, including being environmentally friendly, energy-efficient, low-noise, and can intelligently interact with smart grids through Vehicle-to-Grid (V2G) technology, their widespread adoption will increase energy demand and present challenges to grid load management. Furthermore, EV users face issues such as charging costs, charging time, access to public charging infrastructure, and more. In this article, we propose an approach utilizing metaheuristic algorithms to schedule the charging and discharging activities of EVs while parking, leveraging V2G technology with the goal of reducing the daily costs of EV users and addressing energy demand management challenges in smart grids. Four metaheuristic algorithms inspired by evolutionary and swarm concepts are applied, including Differential Evolution (DE), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Grey Wolf Optimizer (GWO). The results obtained from the proposed approach demonstrate the feasibility of scheduling EVs charging and discharging activities to minimize EV user costs through V2G integration. This, in turn, contributes to enhancing the overall EV user experience and addressing energy demand management issues. Additionally, the results show that WOA outperformed the other algorithms in terms of convergence. This work can be further developed to create an integrated algorithm to balance the interests of both EV users and parking facility operators.