Software-Defined Network-Based Proactive Routing Strategy in Smart Power Grids Using Graph Neural Network and Reinforcement Learning

Abstract

Smart power grid relies on sensors and actuators to provide continuous monitoring and precise control functions. Two types of data and command packets are associated with such field devices, namely, periodic fixed scheduling (FS) and emergency-related event-driven (ED) packets, which require different levels of quality-of-service (QoS) support. However, existing routing strategies in smart power grids are not adaptive to network conditions and cannot guarantee differentiated QoS support. To overcome this limitation, we propose a software-defined network (SDN) proactive routing framework in smart grids that takes into account the current and future state of the network while making the routing decisions. The proposed framework offers the following features: (a) It sets up separate queues for ED and FS packets, with higher priority for the ED queue; (b) It predicts the future traffic condition at each switch within the network (congested or not congested) using a graph-neural-network (GNN) model that provides an accurate prediction of the traffic condition despite the sparsity of the ED events; (c) It adopts a reinforcement learning (RL) strategy that establishes an ideal route and updates the queue service rate for each switch along the route following the network’s current and predicted future congestion condition. The proposed framework is tested on the cyber layers of the IEEE 14-bus and 39-bus test systems, and compared to two benchmarks. Our results indicate the superiority of our proposed framework compared with the benchmarks.