Spatio-Temporal Coordinated Mobile Electric Vehicle Charging in Integrated Transportation and Distribution Systems

With the electrification of the automobile system, the overload problem is being incurred by the increased charging demands from the electric vehicles (EVs). To avoid overloading in the power grid, microgrids (MGs) can be integrated to assist the power balancing. In addition, a coordinated charging strategy among EVs can mitigate the overload problem based on spatially and temporally varying distribution of vehicle traffic in transportation. However, few works have been done on the integration of power system and transportation system in large-scale realistic EV networks. In this paper, both the power distribution and transportation systems are integrated in the high-fidelity and at-scale co-simulation models. Specifically, an LSTM-based prediction model of vehicle traffic distribution is first built and trained over realistic vehicle trace files. The predicted vehicle traffic distribution is exploited to forecast the future EV charging demand. The distribution system is then simulated to describe how the loads (e.g., controllable loads and EVs) and supplies (e.g., distributed generations and energy storages in MGs) impact a power system across the region at scale. Based on the forecast EV loads and co-simulation results from the integrated system, a spatio-temporal coordinated fast EV charging strategy is developed and executed in a distributed way to improve the reliability and resilience of the power systems. Numerical results demonstrate that our proposed strategy can improve the total EV charging performance in the power system while maintaining the power balance of the networked MGs.