Resilience Analysis of Australian Electricity and Gas Transmission Networks

Kumar, SA; Tasnim, M; Basnyat, ZS; Karimi, F; Khalilpour, K

Resilience Analysis of Australian Electricity and Gas Transmission Networks

Kumar, SA Tasnim, M Basnyat, ZS Karimi, F

Khalilpour, K

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Sustainability (Switzerland), 2022, 14, (6)
Issue Date:: 2022-03-01

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Field	Value	Language
dc.contributor.author	Kumar, SA
dc.contributor.author	Tasnim, M
dc.contributor.author	Basnyat, ZS
dc.contributor.author	Karimi, F https://orcid.org/0000-0002-3343-4054
dc.contributor.author	Khalilpour, K https://orcid.org/0000-0003-4104-2488
dc.date.accessioned	2023-03-10T07:59:49Z
dc.date.available	2023-03-10T07:59:49Z
dc.date.issued	2022-03-01
dc.identifier.citation	Sustainability (Switzerland), 2022, 14, (6)
dc.identifier.issn	2071-1050
dc.identifier.issn	2071-1050
dc.identifier.uri	http://hdl.handle.net/10453/166980
dc.description.abstract	Given they are two critical infrastructure areas, the security of electricity and gas networks is highly important due to potential multifaceted social and economic impacts. Unexpected errors or sabotage can lead to blackouts, causing a significant loss for the public, businesses, and governments. Climate change and an increasing number of consequent natural disasters (e.g., bushfires and floods) are other emerging network resilience challenges. In this paper, we used network science to examine the topological resilience of national energy networks with two case studies of Australian gas and electricity networks. To measure the fragility and resilience of these energy networks, we assessed various topological features and theories of percolation. We found that both networks follow the degree distribution of power-law and the characteristics of a scale-free network. Then, using these models, we conducted node and edge removal experiments. The analysis identified the most critical nodes that can trigger cascading failure within the network upon a fault. The analysis results can be used by the network operators to improve network resilience through various mitigation strategies implemented on the identified critical nodes.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Sustainability (Switzerland)
dc.relation.isbasedon	10.3390/su14063273
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	12 Built Environment and Design
dc.title	Resilience Analysis of Australian Electricity and Gas Transmission Networks
dc.type	Journal Article
utslib.citation.volume	14
utslib.for	12 Built Environment and Design
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Professional Practice and Leadership
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-10T07:59:48Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	14
utslib.citation.issue	6

Abstract:

Given they are two critical infrastructure areas, the security of electricity and gas networks is highly important due to potential multifaceted social and economic impacts. Unexpected errors or sabotage can lead to blackouts, causing a significant loss for the public, businesses, and governments. Climate change and an increasing number of consequent natural disasters (e.g., bushfires and floods) are other emerging network resilience challenges. In this paper, we used network science to examine the topological resilience of national energy networks with two case studies of Australian gas and electricity networks. To measure the fragility and resilience of these energy networks, we assessed various topological features and theories of percolation. We found that both networks follow the degree distribution of power-law and the characteristics of a scale-free network. Then, using these models, we conducted node and edge removal experiments. The analysis identified the most critical nodes that can trigger cascading failure within the network upon a fault. The analysis results can be used by the network operators to improve network resilience through various mitigation strategies implemented on the identified critical nodes.

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http://hdl.handle.net/10453/166980