A review of approaches to uncertainty assessment in energy system optimization models

Simple item page
dc.contributor.authorYue, Xiufeng
dc.contributor.authorPye, Steve
dc.contributor.authorDeCarolis, Joseph
dc.contributor.authorLi, Francis G. N.
dc.contributor.authorRogan, Fionn
dc.contributor.authorÓ Gallachóir, Brian P.
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderNational Science Foundationen
dc.contributor.funderEngineering and Physical Sciences Research Councilen
dc.contributor.funderNTR Foundation, Ireland
dc.date.accessioned2018-08-14T12:02:42Z
dc.date.available2018-08-14T12:02:42Z
dc.date.issued2018-07-25
dc.date.updated2018-08-14T11:49:39Z
dc.description.abstractEnergy system optimization models (ESOMs) have been used extensively in providing insights to decision makers on issues related to climate and energy policy. However, there is a concern that the uncertainties inherent in the model structures and input parameters are at best underplayed and at worst ignored. Compared to other types of energy models, ESOMs tend to use scenarios to handle uncertainties or treat them as a marginal issue. Without adequately addressing uncertainties, the model insights may be limited, lack robustness, and may mislead decision makers. This paper provides an in-depth review of systematic techniques that address uncertainties for ESOMs. We have identified four prevailing uncertainty approaches that have been applied to ESOM type models: Monte Carlo analysis, stochastic programming, robust optimization, and modelling to generate alternatives. For each method, we review the principles, techniques, and how they are utilized to improve the robustness of the model results to provide extra policy insights. In the end, we provide a critical appraisal on the use of these methods.en
dc.description.sponsorshipScience Foundation Ireland and the National Science Foundation (Grant Number 16/US-C2C/3290); Engineering and Physical Sciences Research Council (Grant EP/K039326/1)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationYue, X., Pye, S., DeCarolis, J., Li, F. G. N., Rogan, F. and Ó Gallachóir, B. (2018) 'A review of approaches to uncertainty assessment in energy system optimization models', Energy Strategy Reviews, 21, pp. 204-217. doi:10.1016/j.esr.2018.06.003en
dc.identifier.doi10.1016/j.esr.2018.06.003
dc.identifier.endpage217en
dc.identifier.issn2211-467X
dc.identifier.journaltitleEnergy Strategy Reviewsen
dc.identifier.startpage204en
dc.identifier.urihttps://hdl.handle.net/10468/6607
dc.identifier.volume21en
dc.language.isoenen
dc.publisherElsevier Ltd.en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Supplement/12/RC/2302s/IE/Marine Renewable Energy Ireland (MaREI) - EU Grant Manager/en
dc.relation.urihttp://www.sciencedirect.com/science/article/pii/S2211467X18300543
dc.rights© 2018, Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license.en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectEnergy system modellingen
dc.subjectUncertaintyen
dc.subjectMonte Carlo analysisen
dc.subjectStochastic programmingen
dc.subjectRobust optimizationen
dc.subjectModelling to generate alternativesen
dc.titleA review of approaches to uncertainty assessment in energy system optimization modelsen
dc.typeArticle (peer-reviewed)en
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
A_Review_of_Approaches_to_Uncertainty_Assessment_in_ESOM_Manuscript_Accepted_Version.pdf
Size:
1.05 MB
Format:
Adobe Portable Document Format
Description:
Accepted Version
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
2.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Environmental Research Institute - Journal Articles
Centre for Marine and Renewable Energy (MaREI) - Journal Articles
Energy Engineering - Journal Articles