Distributionally Robust Model Predictive Control for Smart Electric Vehicle Charging Station With V2G/V2V Capability

Abstract

This paper proposes a distributionally robust model predictive control (DRMPC) for energy management of a vehicle-to-grid (V2G)/vehicle-to-vehicle (V2V)-enabled smart electric vehicle charging station (EVCS) with a photovoltaic (PV) system and an energy storage system. The proposed DRMPC method aims to reduce the operational cost of the EVCS while ensuring the desired charging demands of electric vehicle (EV) users under uncertainties in electricity buying/selling prices, PV generation outputs, and future EV charging demands. To cope with these uncertainties, the proposed method includes the following three features: i) tractable reformulation of the worst-case expected buying cost and selling revenue using a Wasserstein metric and duality theory, ii) determination of a distributionally robust bound on the random PV generation output using its support information, and iii) a scenario-based approach to predicting the future EV charging demand. To improve computational efficiency, a penalty method is proposed to relax the complementarity constraints, while still ensuring nonsimultaneous charging and discharging of EVs under the derived sufficient conditions. Numerical examples using a real-world operational dataset of the EVCS are provided to demonstrate the effectiveness of the proposed DRMPC method under uncertain environments in terms of the EVCS cost saving via V2G/V2V capability, data utilization, and computational complexity.