Charge and discharge scheduling method for large-scale electric vehicles in V2G mode via MLGCSO

This paper addresses the challenge of charging and discharging scheduling for large-scale electric vehicles (EVs) in the Vehicle-to-Grid (V2G) mode by proposing a user-oriented scheduling algorithm. First, a large-scale EV charging and discharging scheduling model grounded in the V2G mode is developed, where the objective function mainly focuses on the load variance at the user side and the charging and discharging costs for EV owners, and constraints such as the available time of EVs, charging and discharging power limits, available state of charge values, and upper and lower bounds of real-time prices are incorporated to make the model more applicable to practical engineering scenarios. Based on this model, a multi-level grouping based competitive swarm optimizer (MLGCSO) is put forward. Compared with traditional methods, the diversity and convergence of particle swarm learning are enhanced, and the optimization performance is improved. Simulation results indicate that when compared with three state-of-the-art optimizers, the optimization accuracy of the proposed algorithm is increased by at least 34% and the total cost is reduced by 3.14% and 1.62% respectively, demonstrating that the MLGCSO exhibits high optimization performance and remarkable optimization effects.