A scenario-based stochastic optimization model for charging scheduling of electric vehicles under uncertainties of vehicle availability and charging demand. (1st May 2020) - Wang, Zongfei; Jochem, Patrick; Fichtner, Wolf
Abstract: The integration of electric vehicles (EVs) into the electricity systems comprises both threats and chances. A successful control strategy of EV charging processes is beneficial for both EVs and electricity grid. This paper proposes a scenario-based two-stage stochastic linear programming model for scheduling EV charging processes for different grid requirements in real time using a rolling window approach. The model considers the uncertainties in EV availability (i.e. arrival time and departure time) and electricity demand upon arrival (i.e. initial and target state of charge of the battery). Monte Carlo simulation shows how different input parameters may affect the results. Inhomogeneous Markov Chains are used for EV usage pattern simulation and for scenario generation. For reducing computing time, the amount of scenarios is again reduced by scenario reduction technique. The proposed model is applicable for various grid purposes. We demonstrate the applicability of our model by three example cases: Load flattening (only EV charging load), load leveling (together with conventional household load) and demand response (for wind energy integration or ancillary service).