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dc.contributor.advisorRebennack, Steffen
dc.contributor.authorLohmann, Timo
dc.date.accessioned2007-01-03T05:56:01Z
dc.date.accessioned2022-02-03T12:51:16Z
dc.date.available2007-01-03T05:56:01Z
dc.date.available2022-02-03T12:51:16Z
dc.date.issued2015
dc.identifierT 7685
dc.identifier.urihttps://hdl.handle.net/11124/17040
dc.description2015 Spring.
dc.descriptionIncludes illustrations.
dc.descriptionIncludes bibliographical references (pages 133-140).
dc.description.abstractElectric sector models are powerful tools that guide policy makers and stakeholders. Long-term power generation expansion planning models are a prominent example and determine a capacity expansion for an existing power system over a long planning horizon. With the changes in the power industry away from monopolies and regulation, the focus of these models has shifted to competing electric companies maximizing their profit in a deregulated electricity market. In recent years, consumers have started to participate in demand response programs, actively influencing electricity load and price in the power system. We introduce a model that features investment and retirement decisions over a long planning horizon of more than 20 years, as well as an hourly representation of day-ahead electricity markets in which sellers of electricity face buyers. This combination makes our model both unique and challenging to solve. Decomposition algorithms, and especially Benders decomposition, can exploit the model structure. We present a novel method that can be seen as an alternative to generalized Benders decomposition and relies on dynamic linear overestimation. We prove its finite convergence and present computational results, demonstrating its superiority over traditional approaches. In certain special cases of our model, all necessary solution values in the decomposition algorithms can be directly calculated and solving mathematical programming problems becomes entirely obsolete. This leads to highly efficient algorithms that drastically outperform their programming problem-based counterparts. Furthermore, we discuss the implementation of all tailored algorithms and the challenges from a modeling software developer's standpoint, providing an insider's look into the modeling language GAMS. Finally, we apply our model to the Texas power system and design two electricity policies motivated by the U.S. Environment Protection Agency's recently proposed CO2 emissions targets for the power sector.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado School of Mines. Arthur Lakes Library
dc.relation.ispartof2010-2019 - Mines Theses & Dissertations
dc.rightsCopyright of the original work is retained by the author.
dc.subjectpower generation expansion planning
dc.subjectelectricity policy analysis
dc.subjectefficient cut calculation
dc.subjectdemand response
dc.subjectbenders decomposition
dc.subjectalgebraic modeling languages
dc.subject.lcshElectric power production -- Planning
dc.subject.lcshElectric power production -- Economic aspects
dc.subject.lcshElectric power production -- Environmental aspects
dc.subject.lcshAlgorithms
dc.titleLong-term power generation expansion planning with short-term demand response: model, algorithms, implementation, and electricity policies
dc.typeText
dc.contributor.committeememberHering, Amanda S.
dc.contributor.committeememberFell, Harrison
dc.contributor.committeememberNewman, Alexandra M.
dc.contributor.committeememberSanti, Paul M. (Paul Michael), 1964-
thesis.degree.nameDoctor of Philosophy (Ph.D.)
thesis.degree.levelDoctoral
thesis.degree.disciplineEconomics and Business
thesis.degree.grantorColorado School of Mines


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