Event-based electric vehicle scheduling considering random user behaviors

Uncontrolled Electric Vehicle (EV) and Plug-in Hybrid Electric Vehicle (PHEV) charging within a local distribution grid may cause unexpected high load, which further results in power quality degradation. However, coordinating charging behaviors of a number of EVs is a challenging task, which involves not only the deterministic schedule computing but also nondeterministic EV driver behaviors with random arrival time and energy demands. Previous researches in this area rarely consider these random behaviors for real EV users. In this paper, an implementable event-based cost optimal scheduling algorithm (ECSA) is developed, which solves EV scheduling problem by dynamically estimating the stay duration and energy demand for each participating EV user. Datasets, including users´ historical charging records and time series meter data collected from Electric Vehicle Supply Equipments (EVSEs) in UCLA campus, are utilized for feature extraction. Based on that, proper inference technique is employed to determine parameters within each charging session. In addition, solar generation integration into EVSEs is also considered in our problem formulation. The proposed approaches are tested and validated by real EV charging schedules of users in UCLA campus. The results from simulation experiment demonstrate that the proposed algorithm has a better performance in cost minimization and load shifting compared to existing equal-sharing scheduling algorithm (ESSA).