Critical Nodes Identification of Power Grids Based on Network Efficiency

Abstract

Critical nodes identification is of great significance in protecting power grids. Network efficiency can be used as an evaluation index to identify the critical nodes and is an indicator to quantify how efficiently a network exchanges information and transmits energy. Since power grid is a heterogeneous network and can be decomposed into small functionallyindependent grids, the concept of the Giant Component does not apply to power grids. In this paper, we first model the power grid as the directed graph and define the Giant Efficiency sub-Graph (GEsG). The GEsG is the functionally-independent unit of the network where electric energy can be transmitted from a generation node (i.e., power plants) to some demand nodes (i.e., transmission stations and distribution stations) via the shortest path. Secondly, we propose an algorithm to evaluate the importance of nodes by calculating their critical degree, results of which can be used to identify critical nodes in heterogeneous networks. Thirdly, we define node efficiency loss to verify the accuracy of critical nodes identification (CNI) algorithm and compare the results that GEsG and Giant Component are separately used as assessment criteria for computing the node efficiency loss. Experiments prove the accuracy and efficiency of our CNI algorithm and show that the GEsG can better reflect heterogeneous characteristics and power transmission of power grids than the Giant Component. Our investigation leads to a counterintuitive finding that the most important critical nodes may not be the generation nodes but some demand nodes.

Cyber—Physical Power System (CPPS): A review on measures and optimization methods of system resilience

“…The influential abilities of a node involve the topological structure based on the electric betweenness and operating status based on the entropy of power flow. Kang et al (2018) modeled the power network as a directed graph and presented the concept of the giant efficiency subgraph for the power network. On this basis, they developed an algorithm based on four factors, including the node sharing degree, the cumulative distance between a node and all of the generation nodes, and in-degree and out-degree of nodes, to calculate the critical degree of nodes.…”