@article{article_1662971, title={Design and Optimization of a Hybrid Renewable Energy System for Sustainable Power Generation}, journal={Journal of Energy Trends}, volume={2}, pages={1–12}, year={2025}, DOI={10.5281/zenodo.16415542}, author={Olodu, Dıckson Davıd and Erameh, Andrew and Ihenyen, Osagie Imevbore and Inegbedion, Francis}, keywords={Hybrid Energy System, Optimization, Sustainable Power, Solar-Wind Integration, HOMER, Energy Efficiency}, abstract={The growing demand for sustainable energy solutions necessitates the integration of renewable energy sources into hybrid systems. This study presents the design and optimization of a hybrid renewable energy system combining solar, wind, and diesel power to ensure reliable and cost-effective electricity generation. A detailed numerical analysis evaluates the system’s technical, economic, and environmental performance. Solar resource assessment indicates an average daily radiation of 5.1 kWh/m², while wind speeds range between 4.3 m/s and 5.4 m/s, supporting complementary energy generation. The optimized system achieves an 85% renewable energy fraction, with solar contributing 45.2%, wind 38.7%, and diesel 16.1%. Economic evaluation reveals an investment cost of ₦7,400,000, with a levelized cost of energy (LCOE) of ₦55.2/kWh and a payback period of 6.5 years. Sensitivity analysis confirms financial feasibility under varying input parameters. The system reduces carbon emissions by 73.3% compared to diesel-only alternatives, enhancing environmental sustainability. Reliability assessments show 97.2% system availability with a low Loss of Power Supply Probability (LPSP) of 2.8%.
This research demonstrates the viability of hybrid renewable energy systems as a sustainable power solution, contributing to global decarbonization efforts. The findings underscore the importance of optimizing energy storage and implementing advanced control strategies to enhance efficiency and economic viability. Future studies should explore the integration of artificial intelligence-based predictive models to further improve system performance and reliability.}, number={1}, publisher={Ataturk University}