Disturbance Observer-Based Model Predictive Voltage Control for Electric-Vehicle Charging Station in Distribution Networks

This paper proposes a disturbance observer (DOB)-based model predictive voltage control (MPVC) method to improve the power quality of electric vehicle charging stations (EVCSs) with battery energy storage systems (BESSs) in distribution networks. As the volume of EVCS increases, we face challenges related to transformer overloading and power quality issues. In particular, voltage fluctuations in local EVCS become the most critical problem due to the highly unpredictable EV charging loads and renewable energy production. In this study, the DOB estimates the EV charging loads and PV generation power to ensure that the MPVC can compensate for them effectively and minimize the voltage fluctuation of the EVCS. The proposed MPVC with DOB does not require communication system and is obtained by solving a linear matrix inequality (LMI)-based optimization problem. Furthermore, the parameter uncertainties, caused by the inherent tolerances and aging degradation of circuit components, are considered. The effectiveness of the proposed control scheme is demonstrated based on simulations and experiments using a 10 kVA EVCS simulator.

Automated summary

This paper proposes a disturbance observer (DOB)-based model predictive voltage control (MPVC) method to improve the power quality of electric vehicle charging stations (EVCSs) with battery energy storage systems (BESSs) in distribution networks. The DOB estimates the EV charging loads and PV generation power to minimize voltage fluctuation. The proposed MPVC with DOB does not require communication system and considers parameter uncertainties.