Scheduling the charging and discharging events of electric vehicles for quasi dynamic load flow calculations of a low-voltage distribution grid with regard to stochastic behavior and grid requirements

In this study, a novel bidirectional Vehicle-to-Grid (V2G) scheduling application is presented to predict Electric Vehicle (EV) charging/discharging based on the user's stochastic behavior and wishes, as well as the grid operator's demand. The proposed application takes into account factors such as the initial battery State of Charge (SOC), user's arrival time, user's desired minimum and maximum SOC, EV unplugged time, peak-hour period, and the option of applying the grid operator control over EV charging start time. The effectiveness of the proposed application is then evaluated using various case studies in a single residential feeder of the low-voltage (LV) distribution network example in DIgSILENT PowerFactory. The simulation results indicate that appropriate management of EV charging/discharging through V2G technology can mitigate their negative effects on the LV distribution network, resulting in improved load profiles, voltage profiles, distribution lines loading, and power losses.