Improved stochastic dynamic programming for optimal reservoir operation based on the asymptotic convergence of benefit differences.

Persistent Link:
http://hdl.handle.net/10150/191602
Title:
Improved stochastic dynamic programming for optimal reservoir operation based on the asymptotic convergence of benefit differences.
Author:
Guitron de los Reyes, Alberto,1945-
Issue Date:
1974
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Stochastic dynamic programming has been widely used to solve for optimal operating rules for a single reservoir system. In this thesis a new iterative scheme is given which results to be more efficient in terms of computational effort than the conventional stochastic dynamic approach. The scheme is a hybrid one composed of the conventional procedure alternating with iterations over a fixed policy in order to increase the chance of finding the optimal policy more rapidly. Likewise this thesis introduces a refined technique to derive transition probability matrices and the use of bounded variables in the recursive equation to provide an easier way to verify the convergence of the cyclic gain of the system. A computer program is developed to implement the new iterative scheme and then it is applied to a real world problem in order to derive quantitative comparisons. A real savings of twenty-five percent of the computational time required with the conventional procedure is obtained with the new iterative scheme.
Type:
Thesis-Reproduction (electronic); text
LCSH Subjects:
Hydrology.; Reservoirs -- Mathematical models.; Stochastic programming.; Dynamic programming.
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Hydrology and Water Resources; Graduate College
Degree Grantor:
University of Arizona
Committee Chair:
Davis, Donald R.

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleImproved stochastic dynamic programming for optimal reservoir operation based on the asymptotic convergence of benefit differences.en_US
dc.creatorGuitron de los Reyes, Alberto,1945-en_US
dc.contributor.authorGuitron de los Reyes, Alberto,1945-en_US
dc.date.issued1974en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractStochastic dynamic programming has been widely used to solve for optimal operating rules for a single reservoir system. In this thesis a new iterative scheme is given which results to be more efficient in terms of computational effort than the conventional stochastic dynamic approach. The scheme is a hybrid one composed of the conventional procedure alternating with iterations over a fixed policy in order to increase the chance of finding the optimal policy more rapidly. Likewise this thesis introduces a refined technique to derive transition probability matrices and the use of bounded variables in the recursive equation to provide an easier way to verify the convergence of the cyclic gain of the system. A computer program is developed to implement the new iterative scheme and then it is applied to a real world problem in order to derive quantitative comparisons. A real savings of twenty-five percent of the computational time required with the conventional procedure is obtained with the new iterative scheme.en_US
dc.description.notehydrology collectionen_US
dc.typeThesis-Reproduction (electronic)en_US
dc.typetexten_US
dc.subject.lcshHydrology.en_US
dc.subject.lcshReservoirs -- Mathematical models.en_US
dc.subject.lcshStochastic programming.en_US
dc.subject.lcshDynamic programming.en_US
thesis.degree.nameM.S.en_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineHydrology and Water Resourcesen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.chairDavis, Donald R.en_US
dc.identifier.oclc212753671en_US
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