Multi-Objective Optimization of Electric Vehicle Charging and Discharging in Smart Grid-based Vehicle-to-Grid Systems

This paper proposes a multi-objective optimization framework for electric vehicle (EV) charging and discharging scheduling in a smart grid-based vehicle-to-grid (V2G) system. The scheduling problem is formulated to simultaneously minimize electricity cost and battery degradation while satisfying all operational and state-of-charge constraints. A modified Artificial Bee Colony (mABC) algorithm is proposed to address the problem, enhancing both convergence speed and solution quality. The performance of the proposed method is evaluated over a one-year assessment horizon using a representative 24-hour daily profile. It is compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and standard ABC algorithms. Numerical results demonstrate that the mABC algorithm achieves lower annual electricity costs, smoother battery state-of-charge (SoC) profiles, and faster convergence than the benchmark algorithms. These findings confirm the effectiveness and robustness of the proposed approach for practical EV energy management in smart grid-based V2G systems.