Evaluating Disturbance Ride-Through Capability of Fast Electric Vehicle Charging Stations Using DVshave

Reliable power quality is crucial for electric vehicle charging stations (EVCS), but persistent voltage quality issues in distribution networks pose a significant challenge. This study proposes DVShave, a novel system integrating a dynamic voltage restorer (DVR) with peak-shaving functionality to significantly enhance EVCS resilience. Designed, modelled and tested in MATLAB/Simulink and validated through OPAL-RT real-time studies, DVShave features a DVR supplied by a 700 V energy storage system (ESS) and controlled by an artificial neural network (ANN) using a synchronous reference frame strategy. The system's performance was evaluated under severe symmetrical voltage dips (30%, 60% and 80%), swells, unsymmetrical faults and non-linear voltage conditions. DVShave effectively prevents charging interruptions and maintains stable charging rates during these grid disturbances, notably reducing supply voltage total harmonic distortion from 19.60% to 4.84%. The ANN-based controller demonstrated a small but notable improvement in harmonic distortion elimination compared to PI-based DVRs. Concurrently, its integrated peak-shaving feature leverages the DVR's ESS, using a rule-based control technique to lower the peak-to-average ratio from 1.9 to 1.49 daily over 4.5 h for a charging station with 50 EV chargers.