International Journal of Science, Engineering and TechnologyAuthors: Satyam Kumar Upadhyay, Shamsher kumar Bharati
Abstract: This study presents a comprehensive approach to enhance power quality and efficiency in a grid-connected photovoltaic (PV) and wind energy (WE) hybrid system integrated with energy storage systems (ESS) and electric vehicles (EVs). By leveraging advanced Adaptive Neuro-Fuzzy Inference System (ANFIS) based Maximum Power Point Tracking (MPPT) techniques, the system dynamically maximizes the power output from PV arrays under varying environmental conditions, thereby optimizing energy utilization. The integration of ESS stabilizes the power flow, compensates for renewable intermittency, and supports grid reliability. Additionally, an electric vehicle aggregator (EVA) framework is designed to enable bidirectional power flow—vehicle-to-grid (V2G) and grid-to-vehicle (G2V)—facilitating uninterruptible power supply and efficient load demand management. Various power quality enhancement techniques, including advanced controllers and power electronic devices, are incorporated to mitigate voltage fluctuations, harmonics, and other disturbances inherent in renewable energy integration. Simulation results demonstrate significant improvements in voltage stability, reduced power losses, and harmonic distortion mitigation. The proposed integrated system offers a robust solution for sustainable, efficient, and reliable renewable energy integration into the grid, advancing the role of EVs as active participants in energy management and promoting optimal utilization of renewable resources through ANFIS MPPT-based control strategies.
