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

To enable a set of advanced functionalities, the smart power grid relies on pervasive sensors and actuators for continuous monitoring and control. These field devices handle both regular and event-driven (emergency) data packets, which require different levels of quality-of-service (QoS) support (latency requirements). Unfortunately, the existing routing strategies in smart power grids do not guarantee differentiated QoS support that adapts to the network dynamics. To address this limitation, we propose a QoS-aware routing strategy in smart grids based on a software-defined network (SDN). The proposed framework establishes separate queues for event-driven and fixed-scheduling packets. Then, the routing strategy is designed based on reinforcement learning to enable adaptive QoS support. In this regard, updating the routing tables at the OpenFlow switches for each incoming packet is not practical and incurs additional delays that would violate the target QoS. Instead, the proposed routing strategy relies on two Q-learning agents that fix an optimal route for each source-destination pair and update the queue service rate for each switch along the route depending on the network condition (packet arrival rate). Our simulation results demonstrate that the proposed adaptive strategy offers effective QoS support compared with an SDN benchmark routing strategy based on Bellman-Ford. For instance, the average percentage of packets that violate their latency requirement is only 3% compared with at least 30% for the benchmark routing strategy. 

Software-Defined Routing Strategy Based on Reinforcement Learning in Smart Power Grid

The high proportion of distributed new energy access and large-scale power electronics applications make the new power system more complex, leading the power grid operation facing greater challenges. Therefore, there is an urgent need for heterogeneous communication technologies that meet the needs of massive, distributed object access. Based on the network model, energy consumption model and link quality model, this paper proposes a reliable transmission routing algorithm by integrating the inter-node trustworthiness, link quality, inter-node distance and energy consumption, improving the shortcomings of the original routing measurement. Besides, this paper adopts the strategy of optimal single-hop distance and the timer coordinated forwarding mechanism based on the priority order of candidate forwarding nodes, which effectively reduces the data forwarding delay, balances the energy consumption of network nodes and improves the reliability of data transmission.

A Reliable Transmission Routing Algorithm for Massive Distributed Object Access Data in New Power Systems

Future communication networks of smart girds will consist of a mixture legacy of wireless and PLC links using heterogeneous technologies. In this paper we investigate the feasibility of Opportunistic Routing (OR) use to deliver packets in such a heterogeneous network and propose a custom OR for it which uses static geographical information. For doing this, we first introduce Dijkstra's algorithm to select a single link between the source and the destination for routing unicast packets. Then the forward sets for each nodes on the link is chosen according to the hops away from the source node. At last, we make a comparison between OR and sequential routing with respect to the number of transmissions and end-to-end delay.

Opportunistic routing for heterogeneous micro-power network in smart grids

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Opportunistic routing for heterogeneous micro-power network in smart grids