Abstract: On the basis of considering uncertainties, a multi-time scale energy management strategy including gas turbine, energy storage, battery charging and discharging equipment, wind power, and photovoltaic is proposed for a novel battery charging and swapping station. Firstly, the battery is modeled based on the SOC (state of charge) interval, and its charging and discharging priorities are embedded in the optimization. Then, the day-ahead distributionally robust optimization, intraday rolling optimization and intraday real-time optimization models are established. On the basis of considering the prediction errors of wind power, photovoltaic and load, the day-ahead optimization optimizes the start-stop state of the gas turbine, the power of the battery charging and discharging equipment and the energy reference value of energy storage at the minimum expected total cost, and is transformed into a mixed integer programming based on the linear decision rule and duality theory for solving; the intraday optimization re-optimizes the power of gas turbines and energy storage with the lowest total operating cost based on the reference values of the previous optimization and the latest forecast information; the real-time optimization re-optimizes the power of gas turbines and energy storage again with the lowest total operating cost based on the reference values of intraday rolling optimization and real-time forecast information, and issues instructions for the first control period. Finally, the numerical results demonstrate that the proposed multi-time scale energy management strategy can effectively take into account the economy and robustness of the novel battery charging and swapping station.