A Resilient Mod Osprey Optimization Algorithm (MOOA) Based Effective Demand Side Management for Solar PV tied Smart Grid Networks

Background: This paper addresses the challenges of managing demands on solar PV-tied smart grid networks by implementing a specialized optimization technique. The primary aim is to enhance the effectiveness of demand-side management (DSM), thereby optimizing energy use, reducing peak load periods, and achieving a balanced load profile across the smart grid. Methods: The proposed method utilizes a Multi-Objective Optimization Algorithm (MOOA) specifically designed to optimize DSM within smart grid networks. MOOA enhances energy management by planning demand schedules more efficiently, shifting loads to off-peak times, and increasing the use of renewable energy. This optimization technique is capable of adjusting to real-time changes and uncertainties in energy supply and usage, thereby enhancing the resilience and stability of the grid. Results: Simulation analyses reveal that MOOA significantly optimizes energy use, reduces peak load times, and balances the load profile across the smart grid. The integration of solar PV systems contributes to sustainability efforts by decreasing dependence on non-renewable fuels and boosting the use of renewable energy sources. Economic benefits are achieved through cost savings from more efficient demand scheduling and greater utilization of renewable energy. Concluding Remarks: This paper underscores the transformative potential of integrating MOOA-linked DSM and renewable energies into smart grid networks. The findings demonstrate that MOOA effectively enhances DSM, optimizes energy use, reduced costs, and increases grid resilience and stability. The proposed approach offers significant benefits for smart grid networks, including improved efficiency, sustainability, and robustness.