Degree Type

Thesis

Date of Award

2009

Degree Name

Master of Science

Department

Industrial and Manufacturing Systems Engineering

First Advisor

Sarah M. Ryan

Abstract

Generation expansion planning concerns investment and operation decisions for different types of power plants over a multi-decade horizon under various uncertainties. The goal of this research is to improve decision-making under various long term uncertainties and assure a robust generation expansion plan with low cost and risk over all possible future scenarios. In a multi-year numerical case study, we present a procedure to deal with the long term uncertainties by first modeling them as a multidimensional stochastic process and then generating a scenario tree accordingly. Two-stage stochastic programming is applied to minimize the total expected cost, and robust optimization is further applied to reduce the cost variance. Results of experiments on a realistic case study are compared. An efficient frontier of the planning solutions that illustrates the tradeoff between the cost and risk is further shown and analyzed.

DOI

https://doi.org/10.31274/etd-180810-3102

Copyright Owner

Shan Jin

Language

en

Date Available

2012-04-30

File Format

application/pdf

File Size

85 pages

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