Comparison of Optimization Techniques for Energy Conversion and Management in Sustainable Smart Energy Systems

The transition toward sustainable energy systems necessitates efficient energy conversion and management strategies to optimize renewable energy utilization, reduce dependence on fossil fuels, and enhance grid stability. This study evaluates and compares three metaheuristic optimization algorithms-Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Chimp Optimization Algorithm (ChOA)-for optimizing energy dispatch and management in smart energy systems. The optimization framework considers renewable energy sources (solar and wind), battery energy storage systems (BESS), and grid interactions to minimize energy losses and maximize renewable energy penetration. A MATLAB/SIMULINK-based simulation is conducted to analyse the performance of these algorithms in real-time energy scheduling scenarios. The results indicate that ChOA outperforms PSO and GWO in terms of convergence speed, renewable energy utilization, and energy loss reduction, making it a promising technique for smart energy system optimization. The study highlights the significance of advanced optimization techniques in achieving a sustainable, resilient, and efficient energy infrastructure.