Robust Online EV Charging Scheduling with Statistical Feasibility

With the worldwide adoption of electric vehicles (EVs), charging stations are becoming the bottleneck in delivering high-quality charging service to EVs. Compared to conventional fuel vehicles, EVs require more time to charge at charging stations until their energy requirements are fulfilled. Furthermore, the distribution network capacities frequently limit charging resources at a charging station. As a result, charging station operators must optimize EVs' charging scheduling and allocate the limited charging resources efficiently. Due to the high uncertainty of future EVs' arrival and charging demands, station operators typically schedule the arrived EVs' charging solely based on the charging requirements of these EVs, while disregarding future arrivals. Such a scheduling policy is simple to implement, but it may result in high service drop rate, particularly for charging stations with high occupancy levels. To that end, we develop an EV charging schedule model that includes a reserved charging rate, as well as a robust sample-based approach that incorporates the concept of statistical feasibility to help minimize the service drop rate. Numerical studies further verify the effectiveness of our suggested method.