On the determination of multi-reservoir operating policy under uncertainty

Persistent Link:
http://hdl.handle.net/10150/279842
Title:
On the determination of multi-reservoir operating policy under uncertainty
Author:
Ahmed, Iftekhar, 1973-
Issue Date:
2001
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:
A mean-variance stochastic optimization algorithm is developed for long-term operation of multi-reservoir systems. Two important factors in reservoir management problems are the prediction on benefits (expected value of water in storage at the end of a simulation period) and inflows. These, together with information on the state of the reservoir, constitute the information input to the decision-making unit and fully determine the release decision. Traditional optimization models are based on deriving a release policy that optimizes a given objective. Such approaches do not account for the fact that the release, which is a function of random inflow and thus a random variable itself, may have a distribution with different variance measure based on the available forecasts. While much effort has been placed in developing an efficient method to incorporate the uncertainty in inflows and their spatial and temporal correlations in reservoir operations, very few approaches (e.g., Dynamic Programming) use the benefit (Cost-to-Go) in a real-time implementation. The proposed temporal decomposition approach takes into account the value of water at the end of the operating horizon as a boundary condition. The first-period decision is common between forecasts while the decisions for remaining periods vary with forecast sequence. The parameter iteration method (Gal, 1979; Zhang et al., 1991) is used to approximate the value of the benefit function (or the Cost-to-Go). In desire to minimize the variance of the objective (expected return) to obtain robust operating (release) policy, a mathematically sound mean-variance formulation is implemented in real time that considers spatial and temporal correlation in streamflows. The foundation of the formulation presented is rooted in stochastic portfolio optimization scheme of Markowitz (1959). The mathematical routines for forecasts and optimization are utilized to set up a user-friendly Decision Support System for multi-reservoir management under hydroclimatic uncertainties.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Engineering, Civil.; Operations Research.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Civil Engineering and Engineering Mechanics
Degree Grantor:
University of Arizona
Advisor:
Lansey, Kevin E.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleOn the determination of multi-reservoir operating policy under uncertaintyen_US
dc.creatorAhmed, Iftekhar, 1973-en_US
dc.contributor.authorAhmed, Iftekhar, 1973-en_US
dc.date.issued2001en_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.abstractA mean-variance stochastic optimization algorithm is developed for long-term operation of multi-reservoir systems. Two important factors in reservoir management problems are the prediction on benefits (expected value of water in storage at the end of a simulation period) and inflows. These, together with information on the state of the reservoir, constitute the information input to the decision-making unit and fully determine the release decision. Traditional optimization models are based on deriving a release policy that optimizes a given objective. Such approaches do not account for the fact that the release, which is a function of random inflow and thus a random variable itself, may have a distribution with different variance measure based on the available forecasts. While much effort has been placed in developing an efficient method to incorporate the uncertainty in inflows and their spatial and temporal correlations in reservoir operations, very few approaches (e.g., Dynamic Programming) use the benefit (Cost-to-Go) in a real-time implementation. The proposed temporal decomposition approach takes into account the value of water at the end of the operating horizon as a boundary condition. The first-period decision is common between forecasts while the decisions for remaining periods vary with forecast sequence. The parameter iteration method (Gal, 1979; Zhang et al., 1991) is used to approximate the value of the benefit function (or the Cost-to-Go). In desire to minimize the variance of the objective (expected return) to obtain robust operating (release) policy, a mathematically sound mean-variance formulation is implemented in real time that considers spatial and temporal correlation in streamflows. The foundation of the formulation presented is rooted in stochastic portfolio optimization scheme of Markowitz (1959). The mathematical routines for forecasts and optimization are utilized to set up a user-friendly Decision Support System for multi-reservoir management under hydroclimatic uncertainties.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectEngineering, Civil.en_US
dc.subjectOperations Research.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineCivil Engineering and Engineering Mechanicsen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorLansey, Kevin E.en_US
dc.identifier.proquest3026583en_US
dc.identifier.bibrecord.b42179932en_US
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