AI-Powered Smart Grids for Dynamic Load Balancing in Electrical Networks

Abstract

Modern electrical grids need improved dynamic load balancing and distributed energy resource (DER) management strategies because they are integrating renewable energy sources. This research introduces an artificial intelligence system that optimizes the coordination between distributed energy resources through federated learning and combination of graph neural networks with swarm intelligence algorithms. The proposed method enables time-sensitive power distribution through distributed AI models running directly on hardware devices to optimize network allocation and decrease energy losses across the system. The federated learning approach enables protected optimization of the power grid through distributed node collaboration that avoids sending sensible data to central storage. The implementation of GNNs allows for the prediction of energy flow patterns in complex network topologies while particles in PSO and ants in ACO methods ensure dynamic energy storage and distribution strategy management. The AI framework delivers improved microgrid combination alongside more accurate load predictions and adaptable demand response capabilities which results in sustainable grid resiliency. The experimental data shows that the AI-based distribution system operator management technology decreases peak power usage while improving energy efficiency through sustainable deployment in modern smart grids.