Hybrid optimization approach for power scheduling with PV-battery system in smart grids

This manuscript proposes a hybrid method for the smart-grid (SG) optimization, which combines automatic demand-response (DR) shedding with load classification. The integration of the Mexican Axolotl Optimization (MAO) and Honey Badger Algorithm (HBA) constitutes the proposed hybrid approach. The HBA method improves the axolotls' updating behavior. It is commonly referred to as the Enhanced MAO (EMAO) approach. The proposed energy-management framework optimizes customer power consumption patterns to minimize carbon emissions, electricity-costs, and peak-power-consumption. By integrating utility generation, PV-battery systems, and dynamic price signals using the EMAO approach, it reduces power consumption costs, minimizes peak fluctuations, and lowers carbon emissions. The EMAO control-topology is rigorously evaluated through MAT LAB simulations, demonstrating superior performance compared to existing optimization methods such as HGPO, PSO, and GA. The results showcase the EMAO algorithm consistently achieving the lowest cost at 310 cents, minimizing carbon emissions to 1.8 pounds, and achieving a high load classification accuracy of 98.2 %. With a moderate performance-to-cost ratio of 1.7, the EMAO algorithm excels in energy management, effectively balancing cost considerations, environmental impact, and load classification objectives. The proposed hybrid method effectively integrates DR shedding and load classification to optimize SG-operation, achieving significant improvements in cost, emissions, and load-classification accuracy compared to traditional methods.