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dc.contributor.author
Voll, Philip
dc.contributor.author
Jennings, Mark
dc.contributor.author
Hennen, Maike
dc.contributor.author
Shah, Nilay
dc.contributor.author
Bardow, André
dc.date.accessioned
2020-07-22T08:53:37Z
dc.date.available
2020-07-20T12:10:29Z
dc.date.available
2020-07-22T08:53:37Z
dc.date.issued
2015-03-15
dc.identifier.issn
0360-5442
dc.identifier.issn
1873-6785
dc.identifier.other
10.1016/j.energy.2015.01.055
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/427384
dc.description.abstract
An optimisation-based decision support methodology is presented for the synthesis of energy supply systems on the conceptual level. Previous work in this field has tended to focus on the generation of the single optimal solution. However, given that mathematical models never perfectly represent the real world and that planners are often not aware of all practical constraints, the mathematically optimal solution usually only approximates the real-world optimum, and thus has only limited significance. The presented approach therefore exploits the near-optimal solution space for more rational synthesis decisions. For this purpose, integer-cut constraints are employed to systematically generate a set of near-optimal solutions alongside the optimal solution. In place of the traditional analysis of the single optimal solution, we analyse the generated solution set to identify common features (the “must-haves”) and differences (the “real choices”) among the good solutions, and features not observed in any of the generated solutions (the “must-avoids”). This approach provides valuable insights into the synthesis problem and opens up a wide range of rational decision options. The proposed concept is applied to three different real-world problems at the industrial, district, and urban scale. For all three test cases, many near-optimal solutions are identified with different equipment configurations but similar objective function values. Thus, a ranking of the identified solutions strictly based on a single objective value is not productive. Instead, we show that the near-optimal solutions analysis supports the decision process to identify a wider basis of system options, which may be consulted upon to reach rational synthesis decisions.
en_US
dc.language.iso
en
en_US
dc.publisher
Elsevier
en_US
dc.subject
Optimization
en_US
dc.subject
Near-optimal solutions
en_US
dc.subject
Distributed energy systems
en_US
dc.subject
Industrial energy systems
en_US
dc.subject
District energy systems
en_US
dc.subject
Urban energy systems
en_US
dc.title
The optimum is not enough : A near-optimal solution paradigm for energy systems synthesis
en_US
dc.type
Journal Article
dc.date.published
2015-02-10
ethz.journal.title
Energy
ethz.journal.volume
82
en_US
ethz.journal.abbreviated
Energy
ethz.pages.start
446
en_US
ethz.pages.end
456
en_US
ethz.identifier.wos
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
2020-07-20T12:10:37Z
ethz.source
BATCH
ethz.eth
no
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2020-07-22T08:53:47Z
ethz.rosetta.lastUpdated
2021-02-15T15:36:41Z
ethz.rosetta.versionExported
true
ethz.COinS
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