Abstract
dc:description.abstract<p>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.</p>
Degree
thesis:*- Name thesis:degree_name
- Master of Science
- Level thesis:degree_level
- thesis
- Department dc:contributor.department
- Department of Industrial and Manufacturing Systems Engineering
- Year dc:date.issued
- 2009
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Jin, Shan
- Advisor dc:contributor.advisor
-
- Sarah M. Ryan
Rights
- Language dc:language.iso
- en
Identifiers
dc:identifier.*- Identifier
- archive/lib.dr.iastate.edu/etd/10803/
- OAI identifier oai:identifier
- oai:dr.lib.iastate.edu:20.500.12876/25009