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Evaluation of energy economic optimization models using multi-criteria decision-making approach

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dc.citedby1
dc.contributor.authorAlamoodi A.H.en_US
dc.contributor.authorAl-Samarraay M.S.en_US
dc.contributor.authorAlbahri O.S.en_US
dc.contributor.authorDeveci M.en_US
dc.contributor.authorAlbahri A.S.en_US
dc.contributor.authorYussof S.en_US
dc.contributor.authorid57205435311en_US
dc.contributor.authorid57383689800en_US
dc.contributor.authorid57201013684en_US
dc.contributor.authorid55734383000en_US
dc.contributor.authorid57201009814en_US
dc.contributor.authorid16023225600en_US
dc.date.accessioned2025-03-03T07:41:45Z
dc.date.available2025-03-03T07:41:45Z
dc.date.issued2024
dc.description.abstractAchieving high performance in energy systems is crucial for sustainability. Energy economy optimization (EEO) models offer transparent analysis for energy policy decision-making. However, evaluating and benchmarking these models is a complex multicriteria decision making (MCDM) problem. Challenges include multiple criteria, data variation, and the importance of diverse criteria. This study develops an integrated MCDM approach to evaluate and benchmark EEO models. The methodology involves three phases. First, 12 commonly used EEO models and five evaluation criteria (software licenses, public source code, redistribution, public source data, and commercial software) are identified to create an evaluation decision matrix. Second, the fuzzy-weighted zero-consistency method (FWZIC) is used to evaluate and assign weights to the criteria. These weights are utilized in the benchmarking phase. Third, individual and group fuzzy decision by opinion score method (FDOSM) techniques are integrated to benchmark the EEO models based on the weights acquired. The FWZIC weighting reveals that the public source code criterion has the highest weight (0.3347), while redistribution has the lowest weight (0.1021). The group FDOSM results show that the OSeMOSYS model ranks first with the highest score (0.1595), while the DNE21+, MARIA, and MESSAGE models have the lowest score (0.0646), ranking them last. Systematic ranking, sensitivity ranking, and comparative analysis verify the proposed evaluation and benchmarking framework. ? 2024 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo124842
dc.identifier.doi10.1016/j.eswa.2024.124842
dc.identifier.scopus2-s2.0-85199949546
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85199949546&doi=10.1016%2fj.eswa.2024.124842&partnerID=40&md5=2ea700981344193fab2736c6c6536c2a
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36269
dc.identifier.volume255
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleExpert Systems with Applications
dc.subjectBenchmarking
dc.subjectCodes (symbols)
dc.subjectEconomics
dc.subjectOptimization
dc.subjectEnergy economy
dc.subjectEnergy economy optimization model
dc.subjectEnergy systems
dc.subjectFuzzy decision
dc.subjectFuzzy decision by opinion score method
dc.subjectFuzzy-weighted zero-consistency method
dc.subjectMulti-attribute decision analysis
dc.subjectMulticriteria decision-making
dc.subjectOptimization models
dc.subjectSource codes
dc.subjectDecision making
dc.titleEvaluation of energy economic optimization models using multi-criteria decision-making approachen_US
dc.typeArticleen_US
dspace.entity.typePublication
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