Critical Nodes Identification of Power Grids Based on Network Efficiency

WenJie KANG  PeiDong ZHU  JieXin ZHANG  JunYang ZHANG  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.11   pp.2762-2772
Publication Date: 2018/11/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018EDP7042
Type of Manuscript: PAPER
Category: Information Network
Keyword: 
network efficiency,  giant efficiency sub-graph,  the algorithm of critical nodes identification,  critical degree,  node efficiency loss,  

Full Text: PDF(1.3MB)
>>Buy this Article


Summary: 
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 functionally-independent 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.