An Improved Energy Management Strategy for Hybrid Power Systems using Dual Predator Optimization

In this research work, the proposed Dual Predator Optimization algorithm is inspired by the hybridization of the very well-known Whale Optimization Algorithm and Grey Wolf Optimization. This algorithm integrates with a hybrid micro-grid to optimize the use of renewable resources, reduce reliance on fossil fuel, and increase the cost-effectiveness of using excess energy by adjusting these parameters over time. The Dual Predator Optimization is flexible and more suitable for hybrid energy management. The findings indicate that Dual Predator Optimization effectively manages hybrid systems by substantially lowering electricity expenses and diminishing the likelihood of supply interruptions. It was determined that in comparison to the hybridization algorithms, the Cost of Energy of the proposed Dual Predator Optimization technique is reduced to an average of 20%, however, the Loss of Power Supply Probability rises to an average of 7.5%. it offers zero load shedding within the hybrid system with 100% renewable-satisfied energy production for the microgrid. Moreover, the proposed Dual Predator Optimization outperformed others in terms of producing hydrogen by 21.10% and 17.60% respectively. The findings indicate that Dual Predator Optimization is a superior method for addressing energy reliability and environmental sustainability issues.