# A Congestion Control Strategy for Power Scale-Free Communication Network
## 1 Introduction  
The power communication network is a private communication network that meets the effective operation and management of the power system [1]. The reliability of the power communication network, which is the supporting network for the smart grid, has been the prerequisite for economical and safe operation of the smart gird [2; 3]. The power communication network is different from the public communication network because of multiple service types, which have different influences on the reliable operation of the power system. With the amount of data in the power communication network increasing explosively, the bottleneck nodes of the network are prone to congestion, resulting in that it is difficult to process the critical data in a timely manner, which endangers the safe operation of the power system [4; 5; 6]. Therefore, how to reduce the congestion in the power communication network becomes the key problem of the smart grid. Since power grid status monitoring is moving towards Internet protocols and services, the existing Internet technologies should be modified with new protocols to fit into the real-time service requirements. There are many studies about the problem of communication network congestion. The work in [7] proposed a congestion avoidance algorithm based on traffic priority for an electric power dispatching digital network, which guaranteed the QoS of high priority services. The work in [8] proposed a low risk routing method based on the fact that different types of traffic have different service importance levels to reduce the risk of the power communication network. In [9], when the number of nodes in the power communication network increases to a certain extent, the scale-free characteristic is shown in the network. In [10], a routing algorithm based on a complex network was proposed, and the relationship between node congestion level and betweenness centrality was studied. The work in [11] proposed a protocol to work with a free-scale peer-to-peer network, by using theinformation about the network's architecture and by representing message dependencies at the bit level. In [12], the node betweenness is used to compute edge weights and then routed by the shortest path. In [13], the next-hop node was selected by studying the gravitational effect of the node in the packet transmission process, which takes the path length and the waiting time into account. In [14; 15], a global dynamic routing strategy was proposed, which took the queue length of each node in the path as the route cost. In [16], the optimal path was found by predicting the queues of neighbor nodes and combining the shortest path routing. In [17], a heuristic algorithm for the optimization of transport was proposed to enhance traffic efficiency in complex networks. However, the above existing studies for the communication network do not take the scale-free characteristic of the power communication network topology and power communication service level into consideration when doing research on local congestion. The scale-free topology is characterized by a power-law distribution of the node degrees, which shows that there is a small number of highly connected nodes in the network called hub nodes. Therefore, the characteristic of scale-free networks easily causes local network congestion at hub nodes. In order to solve the local congestion problem of the power communication network, the topology model of the power communication network is established, and the delivery capacity and cache capacity of nodes are allocated according to node betweenness. Communication services are divided into two levels based on the importance of the power communication services, including key operational services of the I-level and affairs management services of the II-level. An integrated routing strategy composed of shortest path routing and global dynamic routing is proposed.