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@PHDTHESIS{Hennen:759529,
author = {Hennen, Maike Renate},
othercontributors = {Bardow, André and Shah, Nilay},
title = {{D}ecision support for the synthesis of energy systems by
analysis of the near-optimal solution space; 1. {A}uflage},
volume = {19},
school = {RWTH Aachen University},
type = {Dissertation},
address = {Aachen},
publisher = {Wissenschaftsverlag Mainz GmbH},
reportid = {RWTH-2019-03644},
isbn = {978-3-95886-277-7},
series = {Aachener Beiträge zur technischen Thermodynamik},
pages = {1 Online-Ressource (XXI, 153 Seiten) : Illustrationen,
Diagramme},
year = {2019},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, RWTH Aachen University, 2019},
abstract = {Synthesis of energy systems is a complex design task with a
plethora of decision options. To evaluate these decision
options, mathematical optimization is often used to identify
the optimal solution. However, for decision support, more
information than just the optimal solution is required. The
decision maker needs to know design alternatives and their
trade-offs to make a well-informed decision. Hence,
mathematical optimization should be used as tool to generate
multiple design alternatives. One way to generate design
alternatives is the exploration of the near-optimal solution
space. In this thesis, a decision support system is proposed
for decision support by analysis of the near-optimal
solution space. The near-optimal solution space consists of
the near-optimal design space and the near-optimal objective
space. For exploration of the objective space, a method is
proposed to efficiently identify solutions which reveal
trade-offs in the objective functions. For the design space,
a method is proposed to span all near-optimal design
alternatives by minimizing and maximizing design variables.
The decision support system provides a holistic analysis of
the near-optimal solution space by combining solutions from
the near-optimal objective space and the design space. All
generated solutions are analyzed to reveal feasible ranges
of variables and objective functions. Additionally, the
analysis determines trade-offs between decisions in both the
design space and the objective space. Based on the results
of the analysis, the decision maker can derive preferences.
In an interactive feedback loop, these preferences are added
to the synthesis problem to support the final synthesis
decision. The proposed decision support system is applied to
two real-world case studies. The first case study originates
from pharmaceutical industry and focuses on the supply side
of an energy system; the second case study is a retrofit of
an urban energy system and also takes into account
demand-side measures such as investments in insulation. For
these two entirely different case studies, the decision
support system provides decision support by identifying
feasible designs, their costs and emissions, and the most
important design trade-offs. Thereby, the decision maker is
enabled to take well-informed decisions in the synthesis of
energy systems.},
cin = {412110},
ddc = {620},
cid = {$I:(DE-82)412110_20140620$},
typ = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
doi = {10.18154/RWTH-2019-03644},
url = {https://publications.rwth-aachen.de/record/759529},
}
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