Simulation-based optimization models for electricity generation expansion planning problems considering human health externalities
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Rodgers, Mark David.
Simulation-based optimization models for electricity generation expansion planning problems considering human health externalities. Retrieved from
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TitleSimulation-based optimization models for electricity generation expansion planning problems considering human health externalities
Date Created2016
Other Date2016-10 (degree)
Extent1 online resource (xiii, 190 p. : ill.)
DescriptionThis 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.
NotePh.D.
NoteIncludes bibliographical references
NoteIncludes vita
Noteby Mark David Rodgers
Genretheses, ETD doctoral
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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