001     904188
005     20240712112914.0
024 7 _ |a 10.1016/j.compchemeng.2021.107321
|2 doi
024 7 _ |a 0098-1354
|2 ISSN
024 7 _ |a 1873-4375
|2 ISSN
024 7 _ |a 2128/30419
|2 Handle
024 7 _ |a altmetric:106989079
|2 altmetric
024 7 _ |a WOS:000649713200011
|2 WOS
037 _ _ |a FZJ-2021-05758
082 _ _ |a 660
100 1 _ |a Leenders, Ludger
|0 P:(DE-HGF)0
|b 0
245 _ _ |a Scheduling coordination of multiple production and utility systems in a multi-leader multi-follower Stackelberg game
260 _ _ |a Amsterdam [u.a.]
|c 2021
|b Elsevier Science
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1642772461_3620
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a Large industrial sites commonly contain multiple production and utility systems. In practice, integrated optimization is often not possible because the necessary complete information cannot be exchanged between the systems. Often, industrial sites optimize the operation of production and utility systems sequentially without any feedback, which leads to suboptimal operation.In this paper, we propose a method to coordinate between production and utility systems in a multi-leader multi-follower Stackelberg game. The proposed method does not require complete information exchange. The only information exchanged in one feedback loop is the energy demand and demand-dependent energy cost.In two case studies, the method reduces the total production cost by 7.6% and 3.4% compared to the common sequential optimization. These cost savings correspond to 84% and 88% of the potential cost savings by an integrated optimization. In summary, the proposed method reduces cost significantly, while only incomplete information is exchanged between production and utility systems.
536 _ _ |a 899 - ohne Topic (POF4-899)
|0 G:(DE-HGF)POF4-899
|c POF4-899
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de
700 1 _ |a Ganz, Kirstin
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Bahl, Björn
|0 P:(DE-HGF)0
|b 2
700 1 _ |a Hennen, Maike
|0 P:(DE-HGF)0
|b 3
700 1 _ |a Baumgärtner, Nils
|0 P:(DE-HGF)0
|b 4
700 1 _ |a Bardow, André
|0 P:(DE-Juel1)172023
|b 5
|e Corresponding author
|u fzj
773 _ _ |a 10.1016/j.compchemeng.2021.107321
|g Vol. 150, p. 107321 -
|0 PERI:(DE-600)1499971-7
|p 107321 -
|t Computers & chemical engineering
|v 150
|y 2021
|x 0098-1354
856 4 _ |u https://juser.fz-juelich.de/record/904188/files/1-s2.0-S0098135421000995-main.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:904188
|p openaire
|p open_access
|p VDB
|p driver
|p dnbdelivery
910 1 _ |a RWTH Aachen
|0 I:(DE-588b)36225-6
|k RWTH
|b 0
|6 P:(DE-HGF)0
910 1 _ |a ETH Zurich
|0 I:(DE-HGF)0
|b 0
|6 P:(DE-HGF)0
910 1 _ |a RWTH Aachen
|0 I:(DE-588b)36225-6
|k RWTH
|b 1
|6 P:(DE-HGF)0
910 1 _ |a RWTH Aachen
|0 I:(DE-588b)36225-6
|k RWTH
|b 2
|6 P:(DE-HGF)0
910 1 _ |a RWTH Aachen
|0 I:(DE-588b)36225-6
|k RWTH
|b 3
|6 P:(DE-HGF)0
910 1 _ |a RWTH Aachen
|0 I:(DE-588b)36225-6
|k RWTH
|b 4
|6 P:(DE-HGF)0
910 1 _ |a RWTH Aachen
|0 I:(DE-588b)36225-6
|k RWTH
|b 5
|6 P:(DE-Juel1)172023
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 5
|6 P:(DE-Juel1)172023
910 1 _ |a ETH Zurich
|0 I:(DE-HGF)0
|b 5
|6 P:(DE-Juel1)172023
913 1 _ |a DE-HGF
|b Programmungebundene Forschung
|l ohne Programm
|1 G:(DE-HGF)POF4-890
|0 G:(DE-HGF)POF4-899
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-800
|4 G:(DE-HGF)POF
|v ohne Topic
|x 0
914 1 _ |y 2021
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2021-01-29
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2021-01-29
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1160
|2 StatID
|b Current Contents - Engineering, Computing and Technology
|d 2021-01-29
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2021-01-29
915 _ _ |a Creative Commons Attribution-NonCommercial-NoDerivs CC BY-NC-ND 4.0
|0 LIC:(DE-HGF)CCBYNCND4
|2 HGFVOC
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b COMPUT CHEM ENG : 2019
|d 2021-01-29
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2021-01-29
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2021-01-29
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
|d 2021-01-29
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2021-01-29
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2021-01-29
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2021-01-29
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)IEK-10-20170217
|k IEK-10
|l Modellierung von Energiesystemen
|x 0
980 1 _ |a FullTexts
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)IEK-10-20170217
981 _ _ |a I:(DE-Juel1)ICE-1-20170217


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21