Behaviour Analysis of Renewable Energy Sources in Microgrid

Abstract

The integration of renewable energy sources (RES) into microgrids has revolutionized modern power systems by enhancing sustainability, reliability, and energy autonomy. However, the intermittent and stochastic nature of RES, such as solar and wind, introduces significant challenges in microgrid operation, including voltage fluctuations, frequency instability, and energy management complexities. This paper presents a comprehensive analysis of the behavioral dynamics of RES within microgrids, focusing on their operational characteristics, control strategies, and the impact on overall system performance. Advanced control methodologies, including artificial intelligence (AI) and machine learning (ML) techniques, are explored for optimizing energy dispatch, forecasting generation, and enhancing system resilience. Through simulation studies and real-world case analyses, the research delineates the critical factors influencing RES behavior in microgrids and proposes strategies for effective integration and management. The findings aim to contribute to the development of robust, efficient, and intelligent microgrid systems capable of accommodating high penetration levels of renewable energy.​