Simulation of V2G to Home Power Interface using ANN Voltage Control Technology

The integration of Electric Vehicles (EVs) into the electrical grid poses a number of challenges and opportunities for the effective use of renewable energy sources and load management. In a wireless vehicle-to-grid (V2G) to home power interface, the primary focus of this research is a novel voltage control technique based on artificial neural networks (ANNs). Through the utilization of bidirectional power flow among EVs, the grid, and residential dwellings, the suggested system maximizes energy exchange while upholding grid stability and home power cleanliness. A directional power flow interface is primarily required for Electric Vehicles (EVs) to use the Vehicle to Grid (V2G) concept to supply grid services. The proposed wireless vehicle-grid-home power interface (WVGH-PI) enhances power quality by accounting for the harmonic and reactive power of nonlinear household loads. In order to enable two-way energy flow, the grid side low frequency to DC converter is typically limited to use in wireless bidirectional chargers. Furthermore, an Artificial Neural Network Controller is proposed to improve the overall effectiveness of the interface. To evaluate the functionality of the proposed wireless interface, a variety of modes and dynamic scenarios are looked at. To substantiate the validity of the proposed concept, simulations and experiments from a 1 kW prototype are presented along with comparisons to conventional control.