Before revision:  Compared with the SAA approach, the computational complexity of the proposed algorithm is drastically reduced from O(\epsilon^T) to O( T^3) per time stage , where T is the total number of the time stagesand \epsilon is a constant related to the accuracy of the solution. More importantly, we show that the proposed algorithm can be made scalable when the random process describing the arrival of charging demands is first-order periodic .
After revision:  Compared with the SAA approach, the computational complexity of the proposed algorithm is drastically reduced from O(\epsilon^T) to O( T^3) per time stage , where T is the total number of time stages. We rigorously analyze the performance gap between the near-optimal solution of the MPC-based approach and the optimal solution for any distributions of exogenous random variables. Furthermore, our rigorous analysis shows that when the random process describing the arrival of charging demands is first-order periodic .