Efficient Real-Time EV Charging Scheduling via Ordinal Optimization
Teng Long, Qing‐Shan Jia, Gongming Wang, Yu Yang
Abstract
The surge of plug-in electric vehicles (PEV) on the roads poses the issue to handle the substantial charging demand. Particularly, the operation of charging stations requires an efficient and scalable real-time scheduling method to accommodate the dramatic charging requests in an economical (i.e., to best utilize the local renewable generation) and friendly (i.e., to reduce the impact on the electric grid) manner. This paper fulfills the objectives and makes the following main contributions. First, we develop a parameterized aggregated PEV charging model using the energy boundaries to express the charging flexibility. We propose to parameterize the aggregated charging policy by the incomplete Beta function based on the problem structures. The proposed model can scale down the decision variables from O(NH) to O(2) where N is the number of PEVs and H is the number of prediction horizon. Second, we develop an ordinal optimization (OO) based method (denoted as OO-P) to search for good enough charging policies within seconds while still providing probabilistic performance guarantee. Third, we demonstrate the performance of the proposed OO-P via simulations. The numerical results show that the solution of OO-P is only 4% worse than the optima. However, OO-P shows high computation efficiency and scalability. Compared with the existing real-time PEV charging scheduling method, OO-P can reduce 6% of the operation cost for the charging station. OO-P is also shown to outperform the existing heuristic rule.