Do topological models provide good information about electricity infrastructure vulnerability?

By Paul D. H. Hines, Eduardo Cotilla-Sánchez, S. Blumsack. Chaos, 2010

342 citations recorded by the source corpus.

In order to identify the extent to which results from topological graph models are useful for modeling vulnerability in electricity infrastructure, we measure the susceptibility of power networks to random failures and directed attacks using three measures of vulnerability: characteristic path lengths, connectivity loss, and blackout sizes. The first two are purely topological metrics. The blackout size calculation results from a model of cascading failure in power networks. Testing the response of 40 areas within the Eastern U.S. power grid and a standard IEEE test case to a variety of attack/failure vectors indicates that directed attacks result in larger failures using all three vulnerability measures, but the attack-vectors that appear to cause the most damage depend on the measure chosen. While the topological metrics and the power grid model show some similar trends, the vulnerability metrics for individual simulations show only a mild correlation. We conclude that evaluating vulnerability in power networks using purely topological metrics can be misleading.

Subfields: Communication, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/ae6414c2a8cae34627c5134af1ba7695f7746848

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Communication

1. Ouyang Min

In Statistical and Nonlinear Physics

1. Marc Timme
2. Dirk Witthaut
3. Benjamin Schäfer
4. Ouyang Min
5. Zhezhe Pan
6. Lijing Zhao
7. Liu Hong
8. Vito Latora

Citations in the corpus

• Correlation analysis of different vulnerability metrics on power grids. (2014, methodology): “This indicates that topologybased vulnerability metrics could not well approach the flow-based vulnerability, which is also found in some other studies [9].”
• Dynamically induced cascading failures in power grids (2017, background): “In particular, we show that our indicator is capable of identifying the critical links of the network much better than other measures purely based on the topology or steady state of the network, such as the edge betweenness [11, 27, 28].”