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Multi-objective generation expansion planning considering uncertainty and modeling with the Pareto Uncertainty Index
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Selçuklu, Saltuk Buğra. Multi-objective generation expansion planning considering uncertainty and modeling with the Pareto Uncertainty Index. Retrieved from https://doi.org/doi:10.7282/T3WM1GCG

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TitleMulti-objective generation expansion planning considering uncertainty and modeling with the Pareto Uncertainty Index
NameSelçuklu, Saltuk Buğra (author); Coit, David (chair); Rutgers University; Graduate School - New Brunswick
Date Created2015
Other Date2015-10 (degree)
SubjectIndustrial and Systems Engineering, Electric utilities--Mathematical models
Extent1 online resource (xx, 206 p. : ill.)
DescriptionMany real life optimization problems are multi-objective problems where objectives under consideration usually conflict with each other and they are also stochastic due to inherent uncertainties. The electricity Generation Expansion Planning (GEP) problem is an example of such problems in which the goal is to expand the electric power network with new power plant investments including renewable resources. Decisions are made where and when to build new power plants and which technology to choose for new investments. Objectives can include but are not limited to minimization of the cost and pollutant emissions and maximization of reliability. There are inherent uncertainties in the GEP problem due to climate change, demand increase, fuel prices, technological progress and many other aspects that have to be considered. Some of these uncertainties directly affect the objective functions and some affect the constraint sets in the optimization model. In this study, a new uncertainty metric, the Pareto Uncertainty Index (PUI), is presented. The PUI includes uncertainty as part of the Pareto optimality concept so that the decision or policy maker can observe the uncertainty of Pareto optimal solutions. Using the PUI approach for objective function uncertainties and chance constrained programming or scenarios for constraint set uncertainties, a new multi-objective stochastic genetic algorithm, the Pareto Uncertain Genetic Algorithm (PUGA), is presented in this research, as well. In contrast with the other multi-objective genetic algorithms and classical methods, PUGA can incorporate both the multi-objective and stochastic aspects of problem solving without any transformation. A new post-Pareto pruning approach that reduces the number of Pareto optimal solutions to a smaller practical set is also included in PUGA with the help of the uncertainty information preserved in the PUI. Furthermore, this uncertainty information is used for risk assessments of solutions depending on the risk preferences of decision makers. The PUI and PUGA concepts are demonstrated and tested on several problems including the US Northeast region generation expansion planning (NEGEP) problem.
NotePh.D.
NoteIncludes bibliographical references
Noteby Saltuk Buğra Selçuklu
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T3WM1GCG
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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