Edge-Enabled Resource Optimization for Smart Grids: A Load Balancing Approach

Power grids integrated with advanced networking technologies and edge computing are rapidly evolving to meet modern energy demands. However, the growing reliance on IoT devices for real-time monitoring poses significant challenges in network congestion, load balancing, and efficient data routing. This research presents an edge-enabled IoT framework that dynamically integrates load balancing, resource allocation, and communication optimization in smart grids. The solution provided here incorporates the introduction of edge computing in the smart grids. Firstly, the edge devices are set up in the physical and the cyber layer to process the data, followed by introducing the load balancing and resource management framework in the edge devices to improve the resource management and load balancing of the smart grids. In addition, the proposed framework significantly enhances network efficiency by integrating edge computing to process energy data locally, thereby minimizing latency, reducing network congestion, and optimizing real-time power distribution. Experimental results demonstrate significant improvements, including increased throughput (11.8 Mbps), reduced delay (0.002%), and 15-20% lower energy consumption compared to existing methods. These findings establish a resilient, communication-driven model for future smart grids.