DEVELOPMENT OF OPTIMAL OPERATING POLICY FOR PAGLADIA MULTIPURPOSE RESERVOIR

Show simple item record

dc.contributor.author Ahmed, Juran Ali
dc.date.accessioned 2015-09-15T12:46:13Z
dc.date.available 2015-09-15T12:46:13Z
dc.date.issued 2004
dc.identifier.other ROLL NO.01610402
dc.identifier.uri http://gyan.iitg.ernet.in/handle/123456789/52
dc.description Supervisor: Arup Kr. Sarma en_US
dc.description.abstract Pagladia multipurpose reservoir, located on the river Pagladia, a major north bank tributary of Brahmaputra, proposes to serve three purposes, namely, flood control, irrigation and power generation. In order to achieve these, a proper operating policy of the reservoir is imperative. Recent researches have revealed the potential of heuristic methods in deriving reservoir-operating policy. In this study, the potential of Genetic Algorithm (GA) and Artificial Neural Network (ANN) in deriving an optimal operating policy has been explored through their application in the Pagladia multipurpose reservoir. Efficiency of the policies derived by these recent techniques has been assessed through their critical comparison with policies derived by some long-established techniques. To have the advantage of using a long streamflow series in the development of a reservoir operating policy, an ANN based synthetic streamflow generation model has been developed and compared with the Thomas-Fiering and Autoregressive Moving Average (ARMA) models. Synthetic streamflow series generated by the ANN based model has been used in the development of operating policies, as its statistics have been found to be in better agreement with those of the observed historic series. For solving the reservoir optimization problem for Pagladia multipurpose reservoir, deterministic Dynamic Programming (DP) has first been solved. Both multiple linear regression and ANN have been used to infer general monthly operating policy from the DP results, and these models are being termed as DPR and DPN models respectively in this study. Stochastic Dynamic Programming (SDP) model, which uses an explicit stochastic optimization technique, has been developed next for deriving monthly optimal operating policy for the Pagladia multipurpose reservoir. Finally, GA, which is of recent origin and has the capability of solving complex optimization problem, has been used to derive optimal monthly operating policy for the reservoir. Performance of all the operating policies developed by different models has been analyzed on the basis of the reservoir simulation results for 228 months of historic streamflow series (1977-1996). For making a fair comparison among all the models, a total of eight performance criteria covering different aspects of reservoir operation, have been used. The study has shown that GA, which is a robust optimization technique, is quite capable of developing multipurpose reservoir operating policy and has been found to give the most efficient operating policy for the Pagladia multipurpose reservoir. Policies derived by SDP and GA have been found to be competitive in respect of some of the performance criteria. GA out performs the DPR and the DPN models developed in this study. Although performances of different models vary with different performance criteria, considering overall performance and giving priority to irrigation, the application of operating policy derived by GA model has been suggested to be the most appropriate for the Pagladia multipurpose reservoir. en_US
dc.language.iso en en_US
dc.relation.ispartofseries TH-0151;
dc.subject CIVIL ENGINEERING en_US
dc.title DEVELOPMENT OF OPTIMAL OPERATING POLICY FOR PAGLADIA MULTIPURPOSE RESERVOIR en_US
dc.type Thesis en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search


Browse

My Account