Enhancing Energy Management in Photovoltaic Systems with Battery Storage through Artificial Rabbits Optimization in Connected and Disconnected Modes

Effective Energy Management (EM) in Photovoltaic (PV) systems with battery storage enhances the utilization of Renewable Energy Sources (RES), ensuring reliability in both connected and disconnected modes. Nonetheless, there is a challenge in synchronizing battery storage system with the PV systems due to increased costs when trying to manage the flow of energy during supply and demand conditions. The ARO technique is applied to the identification of optimum EM in PV system with storage batteries in this paper. The main focus for the control design is to balance the energy exchange with the grid, batteries and photovoltaic system in connected and disconnected modes. The suggested approach aims to minimize the Mean Squared Error (MSE) in the EM system and lower the cost of electricity. ARO is used to optimize the EM system's control parameters, such as the battery's charging and discharging thresholds, which improves energy efficiency and improves the PV system's performance in a variety of scenarios. At that point, the suggested approach is put into practice on the MATLAB platform and contrasted with a number of current approaches, including Spider Monkey Optimization (SMO), Manta Ray Foraging Optimization Algorithm (MMRFO), and Honey Badger Algorithm (HBA). The proposed ARO method achieves an MSE of 0.0692, demonstrating a significant improvement in accuracy over other methods in EM for PV systems with battery storage.