IoT-based Real-Time Energy Routing Algorithm in The Energy Internet

Abstract

Modern smart grids leverage Internet of Things technologies to revolutionize power distribution through intelligent network connectivity across generation units, energy storage facilities, and transmission infrastructure. These complex interconnected systems require sophisticated routing mechanisms to orchestrate optimal power flow control and ensure seamless energy delivery across heterogeneous network topologies. However, conventional path-finding mechanisms exhibit limitations such as excessive processing demands, suboptimal load distribution, and inflexible response capabilities, leading to elevated power losses and degraded performance. This work introduces a dual-phase adaptive routing framework that employs predictive scheduling algorithms for initial path determination, coupled with dynamic IoT-driven reconfiguration for responsive energy management. The proposed approach utilizes distributed edge computing nodes and impedance-based network analysis to achieve superior power flow optimization with reduced transmission losses. Experimental validation using cloud-based monitoring platforms confirms that adaptive routing mechanisms minimize supply-demand misalignments, strengthen grid stability, and enhance operational efficiency, highlighting IoT's transformative impact on contemporary energy infrastructure.