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dc.contributor.author
Baader, Florian J.
dc.contributor.author
Bardow, André
dc.contributor.author
Dahmen, Manuel
dc.contributor.editor
Yamashita, Yoshiyuki
dc.contributor.editor
Kano, Manabu
dc.date.accessioned
2022-08-05T09:26:04Z
dc.date.available
2022-08-05T07:47:28Z
dc.date.available
2022-08-05T09:26:04Z
dc.date.issued
2022
dc.identifier.isbn
978-0-323-85159-6
en_US
dc.identifier.issn
1570-7946
dc.identifier.other
10.1016/b978-0-323-85159-6.50065-8
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/562119
dc.description.abstract
Electrolyzers can reduce their electricity costs through demand response (DR) by adapting their production rate to time-varying market prices. Although the production rate can often be adapted rapidly, exploiting the full DR potential of an electrolyzer requires to consider slow temperature dynamics, leading to challenging mixed-integer dynamic optimization problems. In this contribution, we propose a dynamic ramping reformulation for real-time scheduling optimization of electrolyzers considering these slow temperature dynamics. Starting from a nonlinear dynamic model, the limits of the temperature gradient are derived to guarantee that the optimization result is feasible on the original model. The limits are then approximated conservatively by piece-wise affine functions leading to a mixed-integer linear program (MILP). Varying the number of piece-wise affine segments allows to explicitly balance model conservativeness against computational burden. We apply our reformulation to a validated alkaline electrolyzer model from literature. Our dynamic temperature ramping approach reduces production costs by 15.9 % compared to nominal operation. A quasi-steady-state optimization, which is restricted to production rates with steady-state temperatures in the allowed range, only leads to 12.8 % improvement. The proposed formulation achieves optimization runtimes below one minute, which is sufficiently fast for real-time scheduling.
en_US
dc.language.iso
en
en_US
dc.publisher
Elsevier
en_US
dc.subject
Electrolysis
en_US
dc.subject
Demand response
en_US
dc.subject
Mixed-integer linear programming
en_US
dc.title
MILP Formulation for Dynamic Demand Response of Electrolyzers
en_US
dc.type
Conference Paper
dc.date.published
2022-07-30
ethz.book.title
14th International Symposium on Process Systems Engineering
en_US
ethz.journal.title
Computer Aided Chemical Engineering
ethz.journal.volume
49
en_US
ethz.pages.start
391
en_US
ethz.pages.end
396
en_US
ethz.event
14th International Symposium on Process Systems Engineering (PSE 2021+)
en_US
ethz.event.location
Kyoto Japan
en_US
ethz.event.date
June 19-23, 2021
en_US
ethz.identifier.scopus
ethz.publication.place
Amsterdam
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::02668 - Inst. f. Energie- und Verfahrenstechnik / Inst. Energy and Process Engineering::09696 - Bardow, André / Bardow, André
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::02668 - Inst. f. Energie- und Verfahrenstechnik / Inst. Energy and Process Engineering::09696 - Bardow, André / Bardow, André
en_US
ethz.date.deposited
2022-08-05T07:47:35Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2022-08-05T09:26:12Z
ethz.rosetta.lastUpdated
2022-08-05T09:26:12Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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