Optimal sizing of smart hybrid renewable energy system using Lotus Effect Optimization Algorithm

The increasing demand for sustainable and cost-effective energy solutions has prompted the integration of Hybrid Renewable Energy Systems (HRES), which combine solar, wind, and storage technologies. This study proposes an optimized HRES sizing framework utilizing the Lotus Effect Optimization Algorithm (LEOA), a novel nature-inspired metaheuristic approach known for its robust performance in solving multiobjective nonlinear problems. Research focuses on minimizing the Levelized Cost of Energy (LCOE), enhancing system reliability, and reducing environmental impact. A real-world case study from Qassim, Saudi Arabia, is presented to validate the proposed method. The results show that LEOA outperforms conventional algorithms, including PSO, GA, SA, and MOPSO, in terms of convergence speed, solution accuracy, and computational efficiency. The proposed algorithm achieved the lowest LCOE ($0.275/kWh), the highest penetration of renewable energy (85%) and the maximum reduction of $C{O}_2$ emissions (40%). These findings highlight the effectiveness of the proposed algorithm in the design of cost-effective, reliable, and environmentally sustainable HRES configurations, making it a promising tool for future smart grid applications.