Performance improvement and control optimization in grid-integrated PV source with energy storage systems

Abstract

Photovoltaic (PV) systems integrated with the grid and energy storage face significant challenges in maintaining power quality, especially under fluctuating temperature and irradiance conditions. Traditional Maximum Power Point Tracking (MPPT) techniques often struggle to optimize efficiency in such variable environments. This research introduces an advanced MPPT approach that combines Incremental Conductance (INC) with a Function-Fitting Neural Network (FFNN). This hybrid method enhances PV system performance, improving tracking accuracy, system stability, and energy conversion efficiency under dynamic conditions. Also, the traditional converter and inverter control strategies fail to manage grid stability and minimize Total Harmonic Distortion (THD) during fluctuations, which this research resolves by utilizing an Adaptive Voltage Source Inverter (VSI) control that combines voltage, current, and droop control to maintain grid stability at 300 V and 12 A, even under varying PV input conditions. Moreover, the performance limitations of conventional PID-controlled Energy Storage Systems (ESS), characterized by slow response times and frequent manual tuning, are addressed in this research by optimizing the PID controller through Ant Colony Optimization (ACO), enhancing system responsiveness and robustness to improve ESS efficiency and power quality. The proposed methodology significantly reduces Total Harmonic Distortion up to 0.02 %, improving power quality and system efficiency by up to 97.8 % by integrating intelligent MPPT with neural network capabilities, advanced grid, and storage control strategies, offering a significant contribution to improving power quality and operational stability in renewable energy integration, making it a vital advancement in the field.