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Simulation-based optimization models for electricity generation expansion planning problems considering human health externalities

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Title
Simulation-based optimization models for electricity generation expansion planning problems considering human health externalities
Name (type = personal)
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Rodgers
NamePart (type = given)
Mark David
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Mark David Rodgers
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author
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David W
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David W Coit
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Advisory Committee
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chair
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Felder
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Frank A
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Frank A Felder
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Advisory Committee
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co-chair
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Albin
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Susan
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Susan Albin
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Advisory Committee
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internal member
Name (type = personal)
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Wang
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Honggang
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Honggang Wang
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Advisory Committee
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internal member
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Carlton
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Annmarie
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Annmarie Carlton
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Advisory Committee
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Rutgers University
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degree grantor
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Graduate School - New Brunswick
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theses
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DateCreated (qualifier = exact)
2016
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2016-10
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2016
Place
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xx
Language
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eng
Abstract (type = abstract)
This dissertation is focused on the development of mathematical models to solve electricity generation expansion planning problems that minimize total system-wide costs, including human health externalities. A generation expansion planning model is a mathematical optimization framework employed to determine the type of generation technology to invest in, and when and where these investments should be made in order to minimize market costs such as investment costs, fixed and variable operating & maintenance costs, and fuel costs over a long term planning horizon. Fossil fuels (such as coal, oil, and natural gas), which are the primary sources of energy for electricity, are among the most economical sources of electricity. However, burning fossil fuels creates by-products that contribute to ground-level ozone, particulates, and acid rain, which have harmful health effects. Based on EPA research, exposure to these elements causes various respiratory-related illnesses leading to lost days at school or work on a daily basis. In this research, a simulation-based approach is employed to quantify human health externalities by linking the outputs of expansion planning simulations with an EPA screening tool that determines the human health externalities from the electricity sector. From this data set, a statistical prediction model is employed to approximate health costs as a function of electricity generation. This explicit representation of the relationship between electricity generation and health externalities is then incorporated in the objective function of a generation expansion planning problem as a metamodel or surrogate curve. This research is the first comprehensive attempt to dynamically quantify human health externalities in the context of generation expansion planning. Additionally, this research leads contributions for developing generation expansion planning models considering human health externalities as costs in the objective function. This research also leads contributions for developing large scale simulation-based optimization models, by applying a rigorous search algorithm to determine candidate solution points to enhance prediction capabilities of the metamodel, and thus yield more accurate and realistic optimization solutions.
Subject (authority = RUETD)
Topic
Industrial and Systems Engineering
Subject (authority = ETD-LCSH)
Topic
Systems engineering--Computer simulaton
Subject (authority = ETD-LCSH)
Topic
Mathematical optimizations
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Title
Rutgers University Electronic Theses and Dissertations
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ETD_7503
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electronic resource
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application/pdf
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text/xml
Extent
1 online resource (xiii, 190 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = vita)
Includes vita
Note (type = statement of responsibility)
by Mark David Rodgers
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Title
Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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NjNbRU
Identifier (type = doi)
doi:10.7282/T30V8G3W
Genre (authority = ExL-Esploro)
ETD doctoral
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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
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Rodgers
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Mark
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David
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Permission or license
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2016-08-19 11:07:21
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Mark Rodgers
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Affiliation
Rutgers University. Graduate School - New Brunswick
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
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Copyright protected
Availability
Status
Open
Reason
Permission or license
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