The rapid growth in energy demand, along with environmental concerns and depletion of fossil fuels, has accelerated the adoption of renewable energy sources such as solar and wind. Although these sources are clean and sustainable, their intermittent and unpredictable nature poses challenges to power system stability and reliability. Microgrids have emerged as an effective solution for integrating distributed energy resources (DERs), offering improved control, flexibility, and resilience in both grid-connected and islanded modes. This thesis focuses on optimizing renewable energy integration in microgrids using a Hybrid Energy Storage System (HESS), which combines batteries and supercapacitors. Batteries provide high energy density for long-duration support, while supercapacitors offer high power density for handling transient fluctuations and sudden load variations. A smart energy management system is developed to efficiently coordinate power sharing between these storage components. Advanced optimization techniques such as Particle Swarm Optimization (PSO) and Artificial Intelligence (AI)-based control are implemented to enhance real-time energy flow management, improve voltage and frequency stability, and reduce operational costs. Simulation studies conducted using MATLAB / Simulink demonstrate improved power quality, better load balancing, and optimal utilization of renewable resources, making the proposed system a reliable and scalable solution for future smart grids.

Keywords: Microgrid; Hybrid Energy Storage System (HESS); Renewable Energy Integration; Particle Swarm Optimization (PSO); Artificial Intelligence (AI) Control.