Empirical Data and Regression Analysis for Estimation of Infrastructure Resilience with Application to Electric Power Outages

MacKenzie, Cameron A.; Barker, Kash

doi:10.1061/(asce)is.1943-555x.0000103

Cited by 48 publications

(19 citation statements)

References 39 publications

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“…Oklahoma experienced a much smaller electrical outage in 2007 due to a winter storm. The losses from this outage are estimated at $104 million with $27.5 million of these coming within the first hour [44].…”

Section: Other Disruptionsmentioning

confidence: 99%

The economic costs of natural disasters, terrorist attacks, and other calamities: An analysis of economic models that quantify the losses caused by disruptions

Kazimi

MacKenzie

2016

2016 IEEE Systems and Information Engineering Design Symposium (SIEDS)

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Over the past decade, numerous studies have estimated the economic impacts of a variety of disruptions. Most of these studies are based on macroeconomic models that quantify the direct and indirect economic losses from a disruption. Direct economic losses occur due to damaged facilities or when consumers change their purchasing behavior because of the disruption. Indirect economic losses occur when directly impacted businesses consequently reduce their orders to their suppliers. Indirect economic losses are often larger than direct economic losses. This paper compiles the results from these economic models in order to compare the costs of different disruptions and help decision makers prioritize among disruptions. We compare the direct and indirect economic losses from a variety of disruptions, including earthquakes, hurricanes, terrorist attacks, pandemic diseases, and port closures. Some studies model hypothetical scenarios, but other studies quantify the economic losses from historical events such as the September 11 attacks and the 2011 Japanese tsunami. This paper provides a useful benchmark to understand the consequences from disruptions and highlight areas that public officials could address in planning for future disruptions.

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Section: Other Disruptionsmentioning

confidence: 99%

The economic costs of natural disasters, terrorist attacks, and other calamities: An analysis of economic models that quantify the losses caused by disruptions

Kazimi

MacKenzie

2016

2016 IEEE Systems and Information Engineering Design Symposium (SIEDS)

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“…This basic idea has been extended in a number of ways, including explicitly modeling the tradeoffs between loss and recovery time, (11,12) modeling interconnected infrastructure resilience, (13,14) capturing multi-dimensionality, (4,15) and tracking the disaster recovery process. (16) Most of these research efforts ultimately tend to concentrate on the characterization of resilience so as to better understand system behavior.…”

Section: [Insert Fig 1]mentioning

confidence: 99%

“…Simulation, for example, is often used to compare a system's response to a disruptive event under alternative control policies, (11,17) and techniques like regression are used to try to uncover the theoretical relationship between decisions and outcomes. (13) A related viewpoint depicts resilience as a time-dependent metric defined as the ratio of recovery at a given time to the loss in performance. (18) Such a metric can be used to identify the most important components in a network, such as a transportation or power network, with the goal of prioritizing investments to protect the most influential components.…”

Section: [Insert Fig 1]mentioning

confidence: 99%

Allocating Resources to Enhance Resilience, with Application to Superstorm Sandy and an Electric Utility

MacKenzie

Zobel

2015

Risk Analysis

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This article constructs a framework to help a decisionmaker allocate resources to increase his or her organization's resilience to a system disruption, where resilience is measured as a function of the average loss per unit time and the time needed to recover full functionality. Enhancing resilience prior to a disruption involves allocating resources from a fixed budget to reduce the value of one or both of these characteristics. We first look at characterizing the optimal resource allocations associated with several standard allocation functions. Because the resources are being allocated before the disruption, however, the initial loss and recovery time may not be known with certainty. We thus also apply the optimal resource allocation model for resilience to three models of uncertain disruptions: (1) independent probabilities, (2) dependent probabilities, and (3) ABSTRACTThis paper constructs a framework to help a decision maker allocate resources to increase his or her organization's resilience to a system disruption, where resilience is measured as a function of the average loss per unit time and the time needed to recover full functionality. Enhancing resilience prior to a disruption involves allocating resources from a fixed budget to reduce the value of one or both of these characteristics. We first look at characterizing the optimal resource allocations associated with several standard allocation functions. Because the resources are being allocated before the disruption, however, the initial loss and recovery time may not be known with certainty. We thus also apply the optimal resource allocation model for resilience to three models of uncertain disruptions: (1) independent probabilities, (2) dependent probabilities, and (3) unknown probabilities. The optimization model is applied to an example of increasing the resilience of an electric power network following Superstorm Sandy.

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“…The assumption of model linearity is overshadowed by the amount of real data describing the interdependencies among infrastructure sectors, as well as between infrastructures and industries, and among industries, from the Bureau of Economic Analysis (BEA) for the US, as well as by similar agencies for over 60 other countries worldwide. The IIM has been previously deployed to measure the interdependent economic impact experienced across industries due to an underlying infrastructure disruption, including inland waterway closures [35], [36], and electric power outages [37], [38].…”

Section: Acronymsmentioning

confidence: 99%

“…Then the generic -level of a bound for the fuzzy number is given by (37) Moreover, if , and are triangular fuzzy numbers, then a bound for is given by (38) Proof:…”

Section: Appendixmentioning

confidence: 99%

Fuzzy Importance Measures for Ranking Key Interdependent Sectors Under Uncertainty

2014

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In the field of reliability engineering, several approaches have been developed to identify those components that are important to the operation of the larger interconnected system. We extend the concept of component importance measures to the study of industry criticality in a larger system of economically interdependent industry sectors that are perturbed when underlying infrastructures are disrupted. We provide measures of (i) those industries that are most vulnerable to disruptions and (ii) those industries that are most influential to cause interdependent disruptions. However, difficulties arise in the identification of critical industries when uncertainties exist in describing the relationships among sectors. This work adopts fuzzy measures to develop criticality indices, and we offer an approach to rank industries according to these fuzzy indices. Much like decision makers with the knowledge of the most critical components in a physical system, the identification of these critical industries provides decision makers with priorities for resources. We illustrate our approach with an interdependency model driven by US Bureau of Economic Analysis data to describe industry interconnectedness.

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Empirical Data and Regression Analysis for Estimation of Infrastructure Resilience with Application to Electric Power Outages

Cited by 48 publications

References 39 publications

The economic costs of natural disasters, terrorist attacks, and other calamities: An analysis of economic models that quantify the losses caused by disruptions

The economic costs of natural disasters, terrorist attacks, and other calamities: An analysis of economic models that quantify the losses caused by disruptions

Allocating Resources to Enhance Resilience, with Application to Superstorm Sandy and an Electric Utility

Fuzzy Importance Measures for Ranking Key Interdependent Sectors Under Uncertainty

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