Scenario Based Stochastic Optimization of Probabilistic EV Charging Scheduling

Abstract

High penetration and intermittency of solar resources drive the development of distributed energy storage in microgrid. The adoption of electric vehicles (EV) encourages solar self consumption and carries a significant potential owning to cost and environmental considerations. Previous deterministic valley filling algorithms for EV charging scheduling are extended to consider the stochastic nature of EV schedules. The uncertainties in EV availabilities are studied through sampling coupled with ${k}$- means clustering methods formulated specifically for a real- world EV database. Then a scenario-based approach is proposed for modeling probabilistic EV charge events. The stochastic objective function is realized by considering uncertain EV charging in form of scenarios as true, which is known as deterministic equivalent problem. The resulting net load profiles are transformed into quantile distribution considering their probabilities of occurrence. The final output mustrates probabilistic EV charging strategies and associated grid net load profiles to assist operators in proactively managing grid load.