Dynamic Programming in Water Resources Development

Buras, Nathan

doi:10.1016/b978-0-12-021803-5.50014-1

Cited by 17 publications

(8 citation statements)

References 29 publications

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“…The Stochastic Dynamic Programming Model (SDP) described here is an extension of the model discussed by Butcher (1971), Buras (1966Buras ( , 1972, Torabi and Mobasheri (1973), Caselton and Russell (1976), and Klemes (1977). Recently, Esmaeil-Beik and Yu (1984) The sets of characteristic storage volumes and streamflows are chosen so that the entire ranges of possible storage volumes and streamflows are considered.…”

Section: Sdp Modelmentioning

confidence: 99%

COMPARISON OF STOCHASTIC AND DETERMINISTIC DYNAMIC PROGRAMMING FOR RESERVOIR OPERATING RULE GENERATION¹

Karamouz

Houck

1987

J American Water Resour Assoc

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Two dynamic programming models — one deterministic and one stochastic — that may be used to generate reservoir operating rules are compared. The deterministic model (DPR) consists of an algorithm that cycles through three components: a dynamic program, a regression analysis, and a simulation. In this model, the correlation between the general operating rules, defined by the regression analysis and evaluated in the simulation, and the optimal deterministic operation defined by the dynamic program is increased through an iterative process. The stochastic dynamic program (SDP) describes streamflows with a discrete lag‐one Markov process. To test the usefulness of both models in generating reservoir operating rules, real‐time reservoir operation simulation models are constructed for three hydrologically different sites. The rules generated by DPR and SDP are then applied in the operation simulation model and their performance is evaluated. For the test cases, the DPR generated rules are more effective in the operation of medium to very large reservoirs and the SDP generated rules are more effective for the operation of small reservoirs.

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Section: Sdp Modelmentioning

confidence: 99%

COMPARISON OF STOCHASTIC AND DETERMINISTIC DYNAMIC PROGRAMMING FOR RESERVOIR OPERATING RULE GENERATION¹

Karamouz

Houck

1987

J American Water Resour Assoc

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“…This method was selected because the objective function is nonlinear, and the decisions are carried in time, sequentially. The DP is used extensively in the optimization of water resources systems (Buras, 1966). The DP formulated by Bellman (1957) is a procedure for optimizing the multi-stage decision process.…”

Section: Optimization Modelmentioning

confidence: 99%

COST EFFECTIVE OPERATION OF URBAN WATER SUPPLY SYSTEM USING DYNAMIC PROGRAMMING¹

Sabet¹,

Helweg²

1985

J American Water Resour Assoc

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Efficient operation of a city water supply system is an important goal of all municipalities. Efficient operation should result in minimum operation cost through reduction in total energy use and/ or reduction in on‐peak energy consumption. An optimization model was designed for operating the water supply systems of cities using groundwater. The Newton‐Raphson pipe network was used for network analysis and a dynamic programming optimization algorithm was used for determining a schedule for pump operation in the pipe network system. The model is most suitable for use in small cities with up to 45,000 in population, but with large‐scale disintegration techniques may also be used for larger cities. The savings in operation costs are a function of energy cost and energy use pattern and water use pattern in the area.

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“…The foregoing might be an argument for explicit stochastic models [Buras, 1966;Loucks, 1967]; however, it is not since all known models turn out to be computationally infeasible for multireservoir situations. By explicit stochastic models we mean those models that use probability distributions of streamflow directly rather than samples drawn in streamflow synthesis procedures.…”

Section: Introductionmentioning

confidence: 99%

Multireservoir Operation Studies

Roefs¹,

Bodin²

1970

Water Resources Research

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An implicit stochastic process is compared to possible alternative analytical processes. An implicit stochastic process refers to a sequence of steps consisting of streamflow synthesis, deterministic optimization, and multivariate analysis of deterministic optimization results. The comparison is incomplete in that the performance of the multivariate analysis of results is not within the scope of this paper. Some formulations based on the method of Dantzig‐Wolfe decomposition are shown to be reasonably accurate representations of a nonlinear multireservoir deterministic optimization problem. One of these methods is shown to consume only moderate amounts of computer time for a problem of distinctly nontrivial size. The research issues yet to be resolved in achieving the overall objective (i.e., a multireservoir operation rule for flow regulation and energy production) are identified.

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Dynamic Programming in Water Resources Development

Cited by 17 publications

References 29 publications

COMPARISON OF STOCHASTIC AND DETERMINISTIC DYNAMIC PROGRAMMING FOR RESERVOIR OPERATING RULE GENERATION¹

COMPARISON OF STOCHASTIC AND DETERMINISTIC DYNAMIC PROGRAMMING FOR RESERVOIR OPERATING RULE GENERATION¹

COST EFFECTIVE OPERATION OF URBAN WATER SUPPLY SYSTEM USING DYNAMIC PROGRAMMING¹

Multireservoir Operation Studies

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Dynamic Programming in Water Resources Development

Cited by 17 publications

References 29 publications

COMPARISON OF STOCHASTIC AND DETERMINISTIC DYNAMIC PROGRAMMING FOR RESERVOIR OPERATING RULE GENERATION1

COMPARISON OF STOCHASTIC AND DETERMINISTIC DYNAMIC PROGRAMMING FOR RESERVOIR OPERATING RULE GENERATION1

COST EFFECTIVE OPERATION OF URBAN WATER SUPPLY SYSTEM USING DYNAMIC PROGRAMMING1

Multireservoir Operation Studies

Contact Info

Product

Resources

About

COMPARISON OF STOCHASTIC AND DETERMINISTIC DYNAMIC PROGRAMMING FOR RESERVOIR OPERATING RULE GENERATION¹

COMPARISON OF STOCHASTIC AND DETERMINISTIC DYNAMIC PROGRAMMING FOR RESERVOIR OPERATING RULE GENERATION¹

COST EFFECTIVE OPERATION OF URBAN WATER SUPPLY SYSTEM USING DYNAMIC PROGRAMMING¹