Inflow pattern stochastic dynamic programming and its application in reservoir operation optimization

Simple item page
dc.contributor.advisorBouchart, Francois
dc.contributor.authorChen, Lei
dc.date.accessioned2005-08-16T16:53:12Z
dc.date.available2005-08-16T16:53:12Z
dc.date.issued2004
dc.descriptionBibliography: p. 195-212en
dc.descriptionIncludes 1 CDen
dc.description.abstractStochastic Dynamic Programming (SDP) has often been a useful technique for identifying the optimal operating policies for reservoirs. The stochastic nature of inflows is usually described by an order one Markov process, of which the current inflow is only conditioned on the preceding inflow, and represented by a transition probability Pr(Qt+i\Qt). An important controversy in the literature of stochastic reservoir optimizations, as well as that of stream flow modeling, concerns the appropriateness of using order one Markov process to describe the inflow sequence. If correlations among inflows beyond lag 1 are strong (the shorter the time step, the wider the correlations will span among them, e.g. daily inflows normally show wider span of correlations than weekly or monthly inflows), which implies that the occurrence of current inflow can be more precisely conditioned on more previous inflows Pr(Qt+i\Qt,Qt~ii ...,Qt-k) (higher order autoregressive models are often used in stochastic hydrological modeling under this situation). Theoretically, these previous inflows can be treated as state variables to be included in the SDP formulation. However, this addition of state variables yields a mathematical formulation that is impractical to solve due to the "curse of dimensionality" inherent in the SDP formulation. Specifically, these difficulties stem from the structural limitations of the SDP, whereby the addition of each new state variable forces exponential increases in the number of system states to be evaluated, quickly making it impractical to solve. In this study, the previous inflows Qt, Qt-i, •••> Qt-k are treated as an inflow pattern to be included in the SDP formulation as a single state variable. The number of state variables remains the same as that of the SDP, with one storage state variable and one hydrologie state variable. The computational efficiency of the SDP model is maintained, while the model captures the higher order correlation structure in the inflow sequence. The resulting model is termed inflow Pattern Stochastic Dynamic Programming (PSDP) model. The improvement of PSDP models over their classic SDP counterparts are demonstrated through their application to the Glenmore Reservoir, which is located in southwest Calgary, Canada.en
dc.format.extentxv, 238 leaves : ill. ; 30 cm.en
dc.identifier.citationChen, L. (2004). Inflow pattern stochastic dynamic programming and its application in reservoir operation optimization (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/17394en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/17394
dc.identifier.isbn0612935191en
dc.identifier.lccAC1 .T484 2004 C445en
dc.identifier.urihttp://hdl.handle.net/1880/41404
dc.language.isoeng
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.titleInflow pattern stochastic dynamic programming and its application in reservoir operation optimization
dc.typedoctoral thesis
thesis.degree.disciplineCivil Engineering
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameDoctor of Philosophy (PhD)
ucalgary.item.requestcopytrue
ucalgary.thesis.accessionTheses Collection 58.002:Box 1494 520492011
ucalgary.thesis.notesUARCen
ucalgary.thesis.uarcreleaseyen
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
2004_Chen_L.pdf
Size:
85.63 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Legacy Theses