Robust EV Scheduling in Charging Stations Under Uncertain Demands and Deadlines - Sone, Su Pyae; Lehtomaki, Janne J.; Khan, Zaheer; Umebayashi, Kenta; Kim, Kwang Soon (IEEE transactions on intelligent transportation systems, 2024)
To enable widespread use of electric vehicles (EVs), large-scale public charging stations with fast chargers are being planned in places such as shopping malls and office car parks. Operators of public charging stations need to utilize EV scheduling algorithms that can satisfy charging demands with a minimum number of simultaneous charging sessions. In this paper, we propose EV charging scheduling algorithms that meet the charging demands and deadlines of EV users while minimizing the number of simultaneous charging sessions. An uncertainty-aware deep learning (DL) framework is also used to predict EV arrivals at a charging station. The predicted EV arrivals in turn are used to help the charging operator estimate how many charging sessions to order from the grid. Our DL model not only predicts the mean EV arrival rates but also the upper limits of EV arrivals, which enhances robustness against uncertainty in EV arrivals and helps estimate the maximum charging demand for a given interval. Moreover, to overcome the challenge of insufficient EV charging data for DL models, we construct a synthetic data model that takes into account multiple factors influencing EV arrivals, such as weather, events, weekdays, and weekends. Both online and offline approaches in the design of EV scheduling algorithms are utilized. The performances of the proposed algorithms are evaluated in terms of active charging sessions used to serve EV users. We also compare their performance with a baseline algorithm which is an offline optimal algorithm based on a mixed integer linear problem formulation.