Literature DB >> 16948688

Using graph models to analyze the vulnerability of electric power networks.

Ake J Holmgren1.   

Abstract

In this article, we model electric power delivery networks as graphs, and conduct studies of two power transmission grids, i.e., the Nordic and the western states (U.S.) transmission grid. We calculate values of topological (structural) characteristics of the networks and compare their error and attack tolerance (structural vulnerability), i.e., their performance when vertices are removed, with two frequently used theoretical reference networks (the Erdös-Rényi random graph and the Barabási-Albert scale-free network). Further, we perform a structural vulnerability analysis of a fictitious electric power network with simple structure. In this analysis, different strategies to decrease the vulnerability of the system are evaluated. Finally, we present a discussion on the practical applicability of graph modeling.

Year:  2006        PMID: 16948688     DOI: 10.1111/j.1539-6924.2006.00791.x

Source DB:  PubMed          Journal:  Risk Anal        ISSN: 0272-4332            Impact factor:   4.000


  7 in total

1.  Trade-offs between efficiency and robustness in bacterial metabolic networks are associated with niche breadth.

Authors:  Melissa J Morine; Hong Gu; Ransom A Myers; Joseph P Bielawski
Journal:  J Mol Evol       Date:  2009-04-14       Impact factor: 2.395

2.  Robust component: a robustness measure that incorporates access to critical facilities under disruptions.

Authors:  Shangjia Dong; Haizhong Wang; Ali Mostafavi; Jianxi Gao
Journal:  J R Soc Interface       Date:  2019-08-07       Impact factor: 4.118

3.  Dynamics of Complex Systems Built as Coupled Physical, Communication and Decision Layers.

Authors:  Florian Kühnlenz; Pedro H J Nardelli
Journal:  PLoS One       Date:  2016-01-05       Impact factor: 3.240

4.  Topological Vulnerability Evaluation Model Based on Fractal Dimension of Complex Networks.

Authors:  Li Gou; Bo Wei; Rehan Sadiq; Yong Sadiq; Yong Deng
Journal:  PLoS One       Date:  2016-01-11       Impact factor: 3.240

5.  Reducing Cascading Failure Risk by Increasing Infrastructure Network Interdependence.

Authors:  Mert Korkali; Jason G Veneman; Brian F Tivnan; James P Bagrow; Paul D H Hines
Journal:  Sci Rep       Date:  2017-03-20       Impact factor: 4.379

6.  Searching for small-world and scale-free behaviour in long-term historical data of a real-world power grid.

Authors:  Bálint Hartmann; Viktória Sugár
Journal:  Sci Rep       Date:  2021-03-22       Impact factor: 4.379

7.  Supermodal Decomposition of the Linear Swing Equation for Multilayer Networks.

Authors:  Kshitij Bhatta; Amirhossein Nazerian; Francesco Sorrentino
Journal:  IEEE Access       Date:  2022-07-04       Impact factor: 3.476

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.