Optimizing Green Energy Expansion: A Ladybug Beetle-Inspired Approach for Renewables

In response to the growing global demand for sustainable energy, this paper introduces a novel optimization approach for renewable energy expansion planning using a bio-inspired algorithm, the Ladybug Beetle Optimization (LBO). Traditional energy sources are being progressively replaced by renewable options such as wind, solar, and biomass due to concerns over fossil fuel limitations and environmental impacts. However, optimizing the cost, capacity, and sustainability of renewable energy systems presents complex challenges. Our proposed LBO algorithm addresses these challenges by minimizing the total cost, which includes investment, generation, and operation and maintenance expenses, while ensuring reliable energy output to meet future demand projections. We have considered Iran as our case study, and the results have been analyzed and discussed. Comparative analysis against Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) demonstrates the superior performance of LBO in terms of convergence speed, cost-effectiveness, and adaptability to increasing energy demands. Simulation results show that LBO consistently yields optimized energy configurations that balance investment and operational efficiency. Moreover, a sensitivity analysis is carried out in this paper. This work underscores the potential of LBO as a viable tool for scalable and economically feasible renewable energy planning.