SMART ENERGY MANAGEMENT FOR A HYBRID DC MICROGRID ELECTRIC VEHICLE CHARGING STATION

Abstract

Electric Vehicles (EVs) are increasing in popularity due to their environment-friendly, lower-cost operation and technology elevation. With these advancements and new technologies come more significant challenges and opportunities. The increasing power demand and emerging EV usage reflect enhanced renewable energies such as PV power along with smart storage devices. Nevertheless, an EV charging station for a residential building or a parking lot powered through grid-connected local PV generation has specific uncertainty issues and energy management problems. Some of the main areas to investigate are selecting Energy Storage devices with adequate capacity, grid-PV integration, and energy management for maintaining constant EV charging station requirements based on the EV’s State of Charge (SOC). This study proposes an intelligent, coordinated energy management strategy between the PV power station, the grid, the ESS, and the EV charging station. Here, a smart Energy Management system (EMS) based on Convolution Neural Network – Long Short Term Memory (CNN-LSTM) is proposed for the real-time changes in solar irradiance and State of Charge (SOC) of the ESS to manage grid power and local PV to maintain EV charging station requirements. Moreover, the proposed method prioritizes the usage of Renewable PV sources for the EV charging station, making this eco-friendly and sustainable. Simulation results illustrate the effective integration of the proposed EMS.