Optimized Renewable Energy Integration: Advanced Modeling, Control and Design of A Standalone Microgrid

Abstract

The increasing environmental impacts and limited nature of fossil fuels have accelerated the growth of renewable energy sources (RESS), such as solar, wind, and tidal energy. However, the intermittent nature of these sources poses significant challenges to reliability and cost-effectiveness, particularly in standalone hybrid microgrids. This study focuses on designing, modeling, and optimizing an off-grid hybrid RESS integrating solar, wind, and tidal power along with battery energy storage systems (BESS). A key challenge that this work addresses is battery degradation, which significantly impacts the long-term feasibility of BESS. Conventional studies often overlook degradation effects; however, this thesis contains degradation constraints, considering characteristics such as state of charge (SOC) to ensure an optimized sizing strategy. A hybrid optimization method combining the Firefly Algorithm (FA) and Particle Swarm Optimization (PSO) (FA-PSO) is proposed to achieve minimum net present cost (NPC) while maintaining system reliability, measured with Loss of Power Supply Probability (LPSP). The optimization model is tested on a standalone microgrid in Halifax, Nova Scotia. The results reveal that the proposed FA-PSO hybrid technique outperforms traditional commercial optimization tools (HOMER) and standalone heuristic techniques (GA, ACO, FA, and PSO). Furthermore, a comparative analysis of Li-ion and Lead-acid batteries is conducted, evaluating their impact on NPC, degradation, and system efficiency. This study contributes to sustainable energy management by providing an optimization framework that considers renewable integration challenges, battery degradation, and cost minimization. In this study, tidal energy plays an important role in hybrid renewable microgrids, as it is more predictable and reliable than wind and solar power alone. By using heuristic algorithms, this research offers a practical and scalable approach for optimizing renewable energy systems, addressing economic and technical challenges in microgrids.