Grid-Connected PV-Wind System with Energy Storage and Electric Vehicle Integration

Abstract

The addition of renewable energy sources into power systems is essential to meet growing energy demands while reducing environmental impact. This study presents an innovative approach to enhancing power generation and optimizing power quality in a grid-connected hybrid system comprising photovoltaic (PV), wind energy, energy storage, and electric vehicle (EV) integration. An Adaptive Neuro-Fuzzy Inference System (ANFIS)-based Maximum Power Point Tracking (MPPT) algorithm is employed to maximize energy harvesting from the PV and wind subsystems under varying environmental conditions. The proposed system also incorporates energy storage to ensure reliability and stability by mitigating fluctuations in renewable energy output. Additionally, the bidirectional interaction with EVs enables energy balancing during peak and off-peak hours. Simulation results demonstrate the system's ability to achieve efficient power management, with reduced Total Harmonic Distortion (THD) of 3.08 % and improved voltage regulation during grid disturbances. The ANFIS-MPPT algorithm exhibits superior performance providing efficiency of 99.5%, ensuring rapid convergence to the optimal operating point. This comprehensive approach highlights the potential of hybrid renewable systems with advanced MPPT techniques and energy storage in delivering enhanced power quality and system reliability, paving the way for sustainable and efficient energy utilization in modern grids.