Integrated-grid energy system for supplying reliable electricity to health sectors using grey wolf optimizer algorithms - Olumuyiwa Ajibola Awoniyi; Evans Chinemezu Ashigwuike; Timothy Oluwaseun Araoye (Bulletin of Electrical Engineering and Informatics, 2026)
This study evaluates the techno-economic and environmental performance of various hybrid renewable energy system (HRES) optimized using the gray wolf optimization (GWO) algorithm, aimed at supplying reliable and sustainable energy to a health centre. Multiple system configurations were analyzed, including combinations of photovoltaic (PV), wind, diesel generator (DG), battery storage, and the electrical grid. Key performance metrics assessed include the cost of energy (COE), net present cost (NPC), payback period, annual utility bill savings, carbon dioxide (CO2) emissions, and annual fuel consumption. Results indicate that all hybrid systems significantly outperform the conventional grid-only base case across all evaluated parameters. The base case exhibits the highest COE ($0.159/kWh) and CO2 emissions (8,549 tons/year), underscoring the economic and environmental drawbacks of sole grid dependency. In contrast, the PV/wind/diesel/grid configuration achieved the lowest COE ($0.013676/kWh) and substantial reductions in emissions (2,349 tons/year), with a favorable payback period of 4.5 years. The optimization results highlight the effectiveness of GWO in balancing economic viability with environmental sustainability. Among the configurations, the PV/wind/grid and PV/wind/diesel/grid systems emerged as the most cost-effective and environmentally beneficial solutions. These findings highlight the potential of intelligently optimized hybrid renewable systems to deliver reliable, low-cost, and low-emission energy to critical infrastructure, such as healthcare centers.