Enhanced grey wolf optimization for maximum power point tracking in photovoltaic systems with hybrid battery-supercapacitor storage

This paper proposes an enhanced Grey Wolf Optimization algorithm integrated with a stochastic Cauchy-Gaussian mutation to improve maximum power point tracking in standalone photovoltaic systems. The system incorporates a hybrid energy storage solution combining a lithium-ion battery and a supercapacitor to address energy fluctuations and ensure stable power management. The proposed method was compared with conventional and metaheuristic optimisation techniques, including Particle Swarm Optimization, Ant Colony Optimization, and the standard Grey Wolf Optimization algorithm. Simulation results demonstrate faster convergence, higher tracking efficiency, and improved stability under varying solar and load conditions. The enhanced approach significantly improves energy extraction and reduces tracking time compared to traditional methods. Additionally, the hybrid storage system ensures a balanced and stable operation between the battery and supercapacitor, extending the lifespan of the energy storage components. These findings underscore the algorithm's reliability and effectiveness in optimising and enhancing system performance, making it suitable for real-world applications.