A methodology for criticality analysis in integrated energy systems

Integrated energy systems (IES) such as polygeneration plants and bioenergy-based industrial symbiosis (BBIS) networks offer the prospect of increased efficiency and reduced carbon emissions. However, these highly-integrated systems are also characterized by the strong interdependence among componen...

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Main Authors: Benjamin, Michael Francis D., Tan, Raymond Girard R., Razon, Luis F.
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Published: Animo Repository 2014
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Online Access:https://animorepository.dlsu.edu.ph/faculty_research/2262
https://animorepository.dlsu.edu.ph/context/faculty_research/article/3261/type/native/viewcontent/s10098_014_0846_0.html
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spelling oai:animorepository.dlsu.edu.ph:faculty_research-32612023-10-17T07:22:37Z A methodology for criticality analysis in integrated energy systems Benjamin, Michael Francis D. Tan, Raymond Girard R. Razon, Luis F. Integrated energy systems (IES) such as polygeneration plants and bioenergy-based industrial symbiosis (BBIS) networks offer the prospect of increased efficiency and reduced carbon emissions. However, these highly-integrated systems are also characterized by the strong interdependence among component units. This interdependency results in the risk of propagation of cascading failures within such networks, where disturbances in the operation of one component results in ripple effects that affect the other units in the system. In this work, a novel criticality index is proposed to quantify the effects of a component unit's failure to run at full capacity within an IES. This index is defined as the ratio of the fractional change in the net output to the fractional change in capacity of the component causing the failure. The component units in the entire system can then be ranked based on this index. Such risk-based information can thus be used as an important input for developing risk mitigation measures and policies. Without this information, risk management based only on network topology could result to counterintuitive results. A simple polygeneration plant and two BBIS case studies are presented to demonstrate the computation of the criticality index. © 2014 Springer-Verlag. 2014-10-09T07:00:00Z text text/html https://animorepository.dlsu.edu.ph/faculty_research/2262 info:doi/10.1007/s10098-014-0846-0 https://animorepository.dlsu.edu.ph/context/faculty_research/article/3261/type/native/viewcontent/s10098_014_0846_0.html Faculty Research Work Animo Repository Energy parks Industrial districts Polygeneration systems Chemical Engineering
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
topic Energy parks
Industrial districts
Polygeneration systems
Chemical Engineering
spellingShingle Energy parks
Industrial districts
Polygeneration systems
Chemical Engineering
Benjamin, Michael Francis D.
Tan, Raymond Girard R.
Razon, Luis F.
A methodology for criticality analysis in integrated energy systems
description Integrated energy systems (IES) such as polygeneration plants and bioenergy-based industrial symbiosis (BBIS) networks offer the prospect of increased efficiency and reduced carbon emissions. However, these highly-integrated systems are also characterized by the strong interdependence among component units. This interdependency results in the risk of propagation of cascading failures within such networks, where disturbances in the operation of one component results in ripple effects that affect the other units in the system. In this work, a novel criticality index is proposed to quantify the effects of a component unit's failure to run at full capacity within an IES. This index is defined as the ratio of the fractional change in the net output to the fractional change in capacity of the component causing the failure. The component units in the entire system can then be ranked based on this index. Such risk-based information can thus be used as an important input for developing risk mitigation measures and policies. Without this information, risk management based only on network topology could result to counterintuitive results. A simple polygeneration plant and two BBIS case studies are presented to demonstrate the computation of the criticality index. © 2014 Springer-Verlag.
format text
author Benjamin, Michael Francis D.
Tan, Raymond Girard R.
Razon, Luis F.
author_facet Benjamin, Michael Francis D.
Tan, Raymond Girard R.
Razon, Luis F.
author_sort Benjamin, Michael Francis D.
title A methodology for criticality analysis in integrated energy systems
title_short A methodology for criticality analysis in integrated energy systems
title_full A methodology for criticality analysis in integrated energy systems
title_fullStr A methodology for criticality analysis in integrated energy systems
title_full_unstemmed A methodology for criticality analysis in integrated energy systems
title_sort methodology for criticality analysis in integrated energy systems
publisher Animo Repository
publishDate 2014
url https://animorepository.dlsu.edu.ph/faculty_research/2262
https://animorepository.dlsu.edu.ph/context/faculty_research/article/3261/type/native/viewcontent/s10098_014_0846_0.html
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