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Quantitative Model and Metrics of Electrical Grids’ Resilience Evaluated at a Power Distribution Level

Kwasinski, Alexis (2016) Quantitative Model and Metrics of Electrical Grids’ Resilience Evaluated at a Power Distribution Level. Energies, 9 (2). ISSN 1996-1073

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This paper presents a framework to systematically measure and assess power grids’ resilience with a focus on performance as perceived by customers at the power distribution level. The proposed framework considers an analogous measure of availability as a basic metric for resilience and defines other key resilience-related concepts and metrics, such as resistance and brittleness. This framework also provides a measurement for the degree of functional dependency of loads on power grids and demonstrates how the concepts of resilience and dependency are inherently related. It also discusses the implications of considering human-centered processes as fundamental constituting components of infrastructure systems. Thanks to its quantitative nature, the proposed resilience framework enables the creation of tools to evaluate power grids’ performance as a lifeline and to assess the effects of plans for optimal electrical power infrastructure deployment and operation. The discussion is supported by practical examples and empirical records from field damage assessments conducted after recent notable natural disasters.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Kwasinski, Alexisakwasins@pitt.eduakwasins
Date: 3 February 2016
Date Type: Publication
Journal or Publication Title: Energies
Volume: 9
Number: 2
Publisher: MDPI AG
DOI or Unique Handle: 10.3390/en9020093
Schools and Programs: Swanson School of Engineering > Electrical and Computer Engineering
Refereed: Yes
Uncontrolled Keywords: brittleness, dependence, fragility, power distribution, power grids, resilience, resistance
ISSN: 1996-1073
Official URL:
Funders: Defense Threat Reduction Agency
Article Type: Research Article
Date Deposited: 15 Mar 2021 16:04
Last Modified: 15 Mar 2021 16:04


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