A multi-timescale low-carbon economic dispatch of microgrid considering V2G integration and battery residual value

Recently, the uncertainty of renewable energy output and the randomness of electric vehicle charging loads have posed challenges to the stable operation of microgrids. In this study, multi-type electric vehicle clusters (EVCs), distributed generation, distributed renewable energy, and energy storage devices are integrated, and a multi-timescale rolling optimization model considering Vehicle-to-Grid (V2G) and battery degradation costs is proposed. First, EVCs are classified by analyzing the travel characteristics, and a V2G incentive mechanism is introduced to guide the orderly charging and discharging of EVCs. Second, an energy storage resale value model is established to quantify the degradation cost of energy storage batteries. Furthermore, a dynamic interaction mechanism between carbon trading and green certificates is proposed to improve the absorption rate of renewable energy in the system. Finally, a multi-timescale optimization model is established. The day-ahead stage formulates a 24-h dispatching plan based on wind–solar output and load forecasts, while the intra-day stage employs model predictive control for 15-min rolling optimization. The simulation results show that the proposed optimization strategy can reduce the wind power curtailment rate to less than 5%, reduce carbon emissions by 28.8%, and reduce the system operation cost by 9.6%.