<![CDATA[This study addresses the growing need for efficient and sustainable energy systems through the development of Hybrid Renewable Energy Systems (HRES), which integrate multiple renewable sources with advanced storage technologies to overcome intermittency and reliability issues. The research employs a quantitative modeling and simulation approach, utilizing secondary data on renewable resources, load demand, and component specifications, combined with simulation tools such as HOMER Pro and MATLAB. A multi-objective optimization framework based on metaheuristic algorithms, including Particle Swarm Optimization (PSO) and Non-dominated Sorting Genetic Algorithm II (NSGA-II), is applied to determine optimal system configurations. The results indicate that hydrogen-based configurations provide the highest reliability and lowest emissions, while biomass-based systems offer lower costs but higher environmental impact. Sensitivity analysis reveals that fuel price, load demand, and renewable resource availability significantly influence system performance. The discussion highlights the importance of integrating diverse storage technologies and adopting holistic optimization approaches that consider techno-economic, environmental, and resilience factors. In conclusion, the proposed framework effectively enhances HRES design by producing optimal and realistic solutions, thereby contributing to the advancement of sustainable and resilient energy systems.]]>
Copyrights © 2026
