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@PHDTHESIS{Fuchs:726027,
author = {Fuchs, Marcus},
othercontributors = {Müller, Dirk and Saelens, Dirk},
title = {{G}raph framework for automated urban energy system
modeling; 1. {A}uflage},
volume = {57},
school = {RWTH Aachen University},
type = {Dissertation},
address = {Aachen},
publisher = {E.ON Energy Research Center, RWTH Aachen University},
reportid = {RWTH-2018-224612},
isbn = {978-3-942789-56-1},
series = {E.ON Energy Research Center : EBC, Energy efficient
buildings and indoor climate},
pages = {1 Online-Ressource (xii, 120 Seiten) : Illustrationen,
Diagrammen},
year = {2017},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University 2018; Dissertation, RWTH Aachen University, 2017},
abstract = {Current efforts to decarbonize energy supply chains lead to
new challenges for urban energy system modeling. Among the
key challenges are increasing model integration to leverage
synergies between different subsystems, better prediction of
the dynamic system behavior in time-varying operation
conditions and workflow automation to handle the increasing
system complexity. To help address these challenges, this
thesis presents the graph framework uesgraphs for model
representations of urban energy systems. For the application
of district heating systems, the thesis demonstrates how
this framework facilitates automating modeling-related
workflows like the creation of scalable use cases and the
generation of system models with different modeling
approaches, which enables model comparisons and thus
supports the development of new dynamic modelling approaches
to better address the needs of future urban energy systems.
The presented graph framework uesgraphs separates the system
description into a model-neutralsystem description layer and
leaves the energy modeling itself to an additional model
layer to be defined in separate applications. To this end,
uesgraphs defines a Python package that extends existing
general graph methods to represent different energy
networks, buildings and the street network inform of a
geo-referenced system graph. An automated management of
nodelists allows users to flexibly extract subgraphs of
certain energy networks and recombine them into an urban
energy systemgraph. Based on this system description in the
graph layer, two additional Python packages introduce
methods for automated model generation for district heating
networks. The package dhcstatic defines methods for
quasi-static district heating modeling and simulation in
Python while the package uesmodels enables automated
generation of dynamic district heating system models in
Modelicacode. These applications build a toolkit for
in-depth analyses of different approaches to districtheating
modeling. A comparison between both modeling approaches
shows that a dynamic pipe model accounting for temperature
wave propagations through the network can add accuracy
regarding the short-term prediction of building substation
supply temperatures as well as of the overall network
efficiency. In addition, a dynamic substation model not only
improves short-term simulation results but also has
significant effects on the predicted overall network
performance over longer simulation periods. Together, the
developed methods and models demonstrate a graph framework
for automated urban energy system model generation that
facilitates analyses, modelling and simulation and
significantly reduces manual effort in the process.},
cin = {419510 / 080052},
ddc = {620},
cid = {$I:(DE-82)419510_20140620$ / $I:(DE-82)080052_20160101$},
typ = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2018-224612},
url = {https://publications.rwth-aachen.de/record/726027},
}
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