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@PHDTHESIS{Richter:690762,
author = {Richter, Pascal},
othercontributors = {Frank, Martin and Müller, Siegfried and Castro Diaz,
Manuel J.},
title = {{S}imulation and optimization of solar thermal power
plants},
school = {RWTH Aachen University},
type = {Dissertation},
address = {Aachen},
reportid = {RWTH-2017-05351},
pages = {1 Online-Ressource (xviii, 204 Seiten) : Illustrationen,
Diagramme},
year = {2017},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, RWTH Aachen University, 2017},
abstract = {The contribution of renewable energies to global energy use
has significantly increased over the past decades –
completely new industry branches have developed. Among the
renewable energy technologies, concentrated solar thermal
power plants are a promising option for power generation.
Their basic technical idea is quite simple: Large mirrors
are used to concentrate rays of sunlight on a receiver for
heating up a fluid. The heat of the fluid transfers water
into steam, such that the steam powers a turbine to generate
electricity.In the course of the technical progress of this
young technology, permanently new issues occur. Mathematical
methods and simulation sciences offer adequate techniques
for understanding some of these complex processes. They can
help to develop more efficient and thus more competitive
solar power plants. Within this work, two problems out of
the construction and operation of solar thermal power plants
are regarded and are successfully solved with the help of
numerics and optimization.The first part deals with a solar
tower power plant which consists of a field of hundreds or
thousands of heliostats whose mirrors concentrate the direct
solar radiation onto a receiver placed at the top of a
tower. An open problem is to find the optimal placement of
the heliostats around the tower. Because this global
optimization problem has non-convex constraints a heuristic
is needed to solve this problem. A forward solver is modeled
as a deterministic ray-tracer using ideas from the
convolution method. Due to its fast simulation speed
compared to state of the art solvers, this model allows for
more complex optimization techniques. Within this work, an
evolutionary algorithm is developed, where modifications to
the genotype representation and the evolutionary operators
like recombination and mutation has been made to increase
the convergence rate dramatically. Numerical results show
the applicability of this approach. The optimization method
developed within this work can be used to yield more
efficient and thus more competitive heliostat fields. This
tool was already used for the optimization of a test
facility in South Africa.In the second part, a solar thermal
power plant with linear Fresnel collectors is regarded.
Parallel rows of large mirrors are used to concentrate rays
of sunlight on a long absorber tube of about 1000 m length.
Different fluids can be used as heat transfer, e.g. thermal
oil, water/steam, or molten salt. For optimal control of the
power plant there is need of accurate knowledge about the
ongoing processes in the absorber tubes. Here we regard the
case of using water in the absorber tubes, like in the PE2
solar power plant in Spain. Current numerical approaches are
lacking of necessary mathematical properties such as
hyperbolicity or do not use thermodynamic properties like
entropy dissipation. Mathematically, two-phase flow of water
can be described by a Baer-Nunziato type PDE system. Thus, a
two-velocity two-pressure seven-equations model is
developed, such that several thermodynamic and mathematical
properties are fulfilled. But here the problem occurs, that
this system is in non-conservative form, such that
appropriate numerical solvers have to be developed.Within
this work, a new path-conservative entropy-preserving scheme
and a Godunov solver of the Suliciu-relaxated model are
developed and compared.},
cin = {115020 / 110000},
ddc = {510},
cid = {$I:(DE-82)115020_20140620$ / $I:(DE-82)110000_20140620$},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2017-05351},
url = {https://publications.rwth-aachen.de/record/690762},
}
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