Convergent approaches to smart energy management in grids enhancing grid stability and efficiency through integrated solutions

The increasing need for sustainable and efficient energy systems throughout the globe has led to considerable advancements in smart grid technology. This paper proposes a novel approach titled Convergent Approaches to Smart Energy Management in Grids: Enhancing Grid Stability and Efficiency through Integrated Solutions. The proposed method integrates the Interpretable Generalized Additive Neural Network with the Portia Spider Optimization Algorithm (PSOA), collectively known as the IGANN-PSOA technique. The main goal of this approach is to improve the efficiency of the smart grid system while minimizing power losses. The IGANN is employed for accurate prediction of load demand, and the PSOA technique is applied to optimize the grid's monitoring system. In this work, renewable energy sources like photovoltaic panels, wind turbines and battery storage systems are considered to ensure efficient energy generation, storage, and utilization. The proposed approach is evaluated using the MATLAB platform and contrasted with other methods, such as the Fire Hawk Optimizer, Diagonally Masked Fusion Network (DMFN), and Flying Foxes Optimization (FFO). The experimental outcome demonstrates that the proposed IGANN-PSOA approach achieves a power loss of 1.2 W and an efficiency value of 98.2 %, outperforming the existing methods and showcasing its effectiveness in enhancing smart grid energy management.