Charge Scheduling of Electric Vehicles in Smart Parking-Lot Under Future Demands Uncertainty

In this study, a public parking-lot is assumed to schedule the charging of Electric Vehicles (EVs). Each EV owner upon arriving gives the energy demand as well as departure time to the system and immediately receives feedback; fulfilling or adjusting the request. The system designed in this study decides based on the previously admitted requests and the uncertain future demands in both Admission Control (AC) and Charge Scheduling (CS) mechanisms. We formulate a multi-stage stochastic programming model to minimize the expected total energy costs over the finite time horizon. Next, we approximate the model using a finite scenario tree. However, this model is computationally intractable, even for a moderate number of stages. Therefore, we customize a well-known decomposition procedure, Stochastic Dual Dynamic Programming (SDDP), to be matched the time-dependent charging conditions. Since the procedure takes several hours to obtain a high-quality solution, we run it once in the offline mode and employing the results for the online mode. The simulation results indicate that the proposed method outperforms the myopic approach, and obtains a close solution to the theoretical optimal value in terms of total costs and rejected demands.