Topology Optimization and Flow Control Algorithm of Energy Internet Based on Graph Convolution Network

Traditional energy sources such as fossil fuels pose serious challenges to the future energy needs of humanity, making the full utilization of clean and renewable energy an urgent need for social growth. With the deep integration of emerging grid technologies, such as smart grid and microgrid, with Internet technology, the concept of energy Internet came into being. It provides a new way to improve energy utilization rate, realize energy complementarity, promote the transformation of energy utilization mode, and promote social sustainable growth. This paper proposes a graph convolutional network (GCN) based topology optimization and flow control algorithm for energy Internet. This algorithm uses GCN to learn the characteristics of nodes and edges by constructing the topology diagram of the energy Internet, and then carries out topology optimization and flow control on the energy Internet. Topology optimization aims to find the optimal network structure to improve energy transmission efficiency, reduce costs, and enhance network robustness; And flow control achieves efficient and stable energy transmission by predicting and scheduling the energy demand and supply of each node. The experimental results show that the algorithm has achieved significant results in improving energy utilization efficiency, optimizing network structure, and achieving traffic balance.