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dc.contributor.author
Shchetinin, Dmitry
dc.contributor.supervisor
Hug, Gabriela
dc.contributor.supervisor
Chatzivasileiadis, Spyridon
dc.contributor.supervisor
Molzahn, Daniel
dc.date.accessioned
2019-01-18T09:09:23Z
dc.date.available
2019-01-18T08:30:44Z
dc.date.available
2019-01-18T09:09:23Z
dc.date.issued
2018-12
dc.identifier.uri
http://hdl.handle.net/20.500.11850/317137
dc.identifier.doi
10.3929/ethz-b-000317137
dc.description.abstract
With higher penetration of renewable generation and market liberalization, operating points of electric power systems become increasingly variable and less predictable. To ensure economically efficient and secure operation of such systems, fast and robust optimization algorithms are required. Despite considerable research efforts, the development of these algorithms remains a challenge due to the nonlinearity and high dimensionality of system models. This dissertation focuses on the optimal power flow (OPF) problem, which is at the heart of techniques used in power system operation and planning. As this problem is non-convex and highly nonlinear, modern solvers cannot always find its locally optimal or even feasible point. To address this issue, an approximation of the OPF problem is proposed that helps reduce its complexity without compromising the solution quality. Moreover, the obtained solution is guaranteed to be physically meaningful. Next, this work presents several computationally efficient techniques for strengthening convex relaxations of the OPF problem. A tighter relaxation helps provide a better estimate of a globally optimal solution of the original problem and recover a physically meaningful operating point. Lastly, this work presents several approaches to incorporating risk-based security indices in the OPF problem. To reduce the computational burden of solving the resulting problems, decomposition algorithms are employed. The proposed techniques were tested on grids of various sizes. The results demonstrate that these techniques can potentially help improve optimization tools used in power system operation.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
ETH Zurich
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.title
Optimization of Power System Operation: Approximations, Relaxations, and Decomposition
en_US
dc.type
Doctoral Thesis
dc.rights.license
In Copyright - Non-Commercial Use Permitted
ethz.size
229 p.
en_US
ethz.identifier.diss
25598
en_US
ethz.publication.place
Zurich
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02632 - Inst. f. El. Energieübertragung u. Hoch. / Power Systems and High Voltage Lab.::09481 - Hug, Gabriela / Hug, Gabriela
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02632 - Inst. f. El. Energieübertragung u. Hoch. / Power Systems and High Voltage Lab.::09481 - Hug, Gabriela / Hug, Gabriela
en_US
ethz.date.deposited
2019-01-18T08:31:12Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2019-01-18T09:09:37Z
ethz.rosetta.lastUpdated
2019-01-18T09:09:37Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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