Contribution to the Optimization of Energy Management in a Microgrid Integrating Conversion and Hybrid Storage Elements

Abstract

The growing global demand for sustainable energy has accelerated the shift toward renewable sources, with photovoltaic (PV) and wind systems emerging as leading technologies for clean power generation. Despite their potential, these systems suffer from intermittency and variability caused by changing weather conditions, which often compromise their efficiency and stability. To address these challenges, advanced control and optimization approaches are required to ensure reliable operation and optimal energy utilization. This thesis focuses on the performance of renewable energy system performance through intelligent power management and optimization strategies. First, various Maximum Power Point Tracking (MPPT) algorithms are investigated and compared to identify the most effective methods for improving PV energy extraction. Building on this, a hybrid renewable energy system (HRES) combining photovoltaic panels, wind turbines, batteries, and supercapacitors is developed to increase overall reliability and energy storage efficiency. A novel Fuzzy Logic–based Energy Management Strategy (FL-EMS) is then proposed to coordinate power distribution among the different sources and storage elements, ensuring smooth operation under varying environmental and load conditions. Simulation results validate the effectiveness of the proposed approaches. The optimized MPPT algorithms achieve higher conversion efficiency, while the FL-EMS demonstrates superior performance in maintaining power balance, minimizing voltage fluctuations, and extending battery lifespan. Overall, this research presents a comprehensive framework for improving the stability, efficiency, and reliability of hybrid renewable energy systems, advancing their integration into future smart microgrid