Real-time stochastic optimal scheduling of large-scale electric vehicles: A multidimensional approximate dynamic programming approach

作者：Pan, Z. N.; Yu, T.^*; Chen, L. P.; Yang, B.; Wang, B.; Guo, W. X.

来源：INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2020, 116: 105542.

DOI：10.1016/j.ijepes.2019.105542

• Electric vehicle
• Charging optimization
• Real-time optimization
• Stochastic optimization
• Approximate dynamic programming

This paper studies the real-time charging optimization (RTCO) of large-scale electric vehicles (EVs), which is a multistage and multidimensional stochastic resource allocation problem. In order to handle the complex RTCO of large-scale EVs, a multidimensional approximate dynamic programming (ADP-RTCO) is designed for the sequent optimal decision makings. The hierarchy of ADP-RTCO contains two layers. In the upper layer, RTCO of large-scale EVs is formulated as a multidimensional energy storage problem by classifying EVs into several virtual EV clusters (EVCs). Then, temporal difference learning based policy iteration method is used to obtain the value function approximation for each EVC. In the lower layer, priority based reallocation algorithm is employed to obtain the detailed charging power for connected EVs. ADP-RTCO is designed with on-line learning ability to enhance its adaptability and robustness to ambiguous environment. Comprehensive simulation results demonstrate the optimality and robustness of ADP-RTCO in both cost-saving and load-flattening problems. Furthermore, ADP-RTCO can obtain higher-quality solution compared with other algorithms and is applicable for RTCO of large-scale EVs under uncertainties due to its high computation efficiency.