The intermittent nature of solar energy results in a generation versus load curve, thus becoming a significant challenge for reliable utilization. The use of a battery energy storage system, on the other hand, plays a vital role in uninterrupted power supply; however, it requires advanced control techniques for smooth functionality. The use of conventional techniques, such as a classical Maximum Power Point Tracking scheme based on Constant Current/Constant Voltage, becomes inefficient for dealing with nonlinear dynamics associated with system functionality, thereby reducing battery life. The proposed composite approach incorporates fuzzy logic decision-making, Genetic Algorithm optimization, and adaptive Extended Kalman Filter estimation for quantifying real-time battery state. A comprehensive PV-BESS model has been developed in the environment of a MATLAB/Simulink toolbox, using actual load as well as actual solar irradiance. The results based on the proposed approach indicate improvement in energy efficiency of 20% along with 5% accuracy in battery state estimation while extending battery life to 18-25% compared to conventional battery energy storage systems. The proposed approach provides a robust, optimizing, as well as a viable solution set for the PV-BESS system, thereby paving the way for future micro-grid development.
Keywords: Photovoltaic system, Battery Energy Storage System (BESS), Fuzzy logic control, Genetic Algorithm optimization, Extended Kalman Filter (EKF)
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