Date of Award

1-1-2013

Document Type

Open Access Dissertation

Department

Geography

First Advisor

Michael E Hodgson

Abstract

Vulnerability of critical infrastructure systems is of the utmost importance to a nation's national security interests, especially the electric grid. Despite the importance of these systems, disruptions continue to occur at an alarming rate, thus indicating that there is a fundamental flaw in the way critical infrastructure systems are analyzed for vulnerability.

Critical infrastructure systems are typically analyzed using mathematical approaches such as graph theory, which strip systems of their important geographic information, and only look at their connections to each other. While these relationships and metrics provide useful information, they cannot provide the entire picture. As such, this research seeks to develop a new, geographic framework that not only takes into account the information uncovered by graph metrics, but information about the unique geography of the area that can impact these systems. Using Southeast Asia as a study region, this research seeks to answer the following broad questions:

1. What are differences that arise from analyzing energy network vulnerability using the new geographic framework versus graph theory alone?

2. What types of evaluation methods are applicable for determining if the proposed framework is more effective than graph theory?

To answer these questions, this research developed a field-based model utilizing service areas as the unit of analysis. The factors of betweenness, degree, closeness,

land use, service area population, other critical infrastructure frequency, natural hazard frequency, and temperature extremes. These factors were ranked from one to five, one indicating the least vulnerability and five indicating the highest vulnerability. These factors were then weighted, using the Analytic Hierarchy Process to determine the weights, and summed to determine an overall vulnerability ranking. The higher the score, the more vulnerable a particular substation is.

The results indicate that many of these variables provide little insight into the vulnerability of the electric grid, when validated against real-world data from the 2012 Indian Blackout. The most important variables were betweenness, land use, natural hazard frequency, and temperature extremes. Basic metrics of percentage of substations identified versus substations affected in a real-world scenario provided the basis for effective evaluation of each model.

Included in

Geography Commons

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