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

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Simulation-based optimization models for electricity generation expansion planning problems considering human health externalities./
Author:	Rodgers, Mark David.
Description:	1 online resource (58 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 78-07(E), Section: B.
Contained By:	Dissertation Abstracts International78-07B(E).
Subject:	Industrial engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9781369606867

Simulation-based optimization models for electricity generation expansion planning problems considering human health externalities.
Rodgers, Mark David.

Simulation-based optimization models for electricity generation expansion planning problems considering human health externalities. - 1 online resource (58 pages)

Source: Dissertation Abstracts International, Volume: 78-07(E), Section: B.

Thesis (Ph.D.)--Rutgers The State University of New Jersey, School of Graduate Studies, 2016.

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369606867Subjects--Topical Terms:

679492
Industrial engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Simulation-based optimization models for electricity generation expansion planning problems considering human health externalities.
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502 $a Thesis (Ph.D.)--Rutgers The State University of New Jersey, School of Graduate Studies, 2016.
504 $a Includes bibliographical references
520 $a 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.
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