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Journal ArticleDOI

Derivation of monthly reservoir release policies

Nageshwar R. Bhaskar, +1 more
- 01 Dec 1980 - 
- Vol. 16, Iss: 6, pp 987-993
TLDR
In this article, a single multiple-purpose reservoir is analyzed using a backward looking dynamic program algorithm to obtain optimal releases, and the dynamic program is solved for both one-sided and two-sided quadratic loss functions.
Abstract
A single multiple-purpose reservoir is analyzed using a backward looking dynamic program algorithm to obtain optimal releases. The dynamic program is solved for both one-sided and two-sided quadratic loss functions. Monthly policies are derived by regressing the optimal set of releases on the input and state variables. Linear and nonlinear release policies are developed, then verified and compared through simulation. For a two-sided quadratic loss function, linear policies are as good or better than nonlinear policies. However, for a one-sided quadratic loss function, nonlinear policies give improved performance over linear policies. It is also illustrated that the maximum R2 criterion for selecting release policies may not always be appropriate. Hoover Reservoir, located on Big Walnut Creek in central Ohio, is used as a case example.

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Citations
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Journal ArticleDOI

Reservoir Management and Operations Models: A State‐of‐the‐Art Review

TL;DR: The objective of this paper is to review the state-of-the-art of mathematical models developed for reservoir operations, including simulation, which include linear programming, dynamic programming, nonliner programming, and simulation.
Journal ArticleDOI

Simulation-optimization modeling: a survey and potential application in reservoir systems operation.

TL;DR: Simulation, optimization and combined simulation–optimization modeling approach are discussed and an overview of their applications reported in literature is provided to help system managers decide appropriate methodology for application to their systems.
Journal ArticleDOI

Optimization of Reservoir Management and Operation with Hedging Rules

TL;DR: In this paper, a mixed integer linear programming (MILP) model that considers both the traditional reservoir rule curves and the hedging rules to manage and operate a multipurpose, multireservoir system was developed.
Journal ArticleDOI

Deriving a General Operating Policy for Reservoirs Using Neural Network

TL;DR: In this paper, a dynamic programming (DP) model was used to improve the operation and efficient management of available water for the Aliyar Dam in Tamil Nadu, India, using a neural network procedure (DPN) and using a multiple linear regression procedure (DPR) model.
Journal ArticleDOI

Operating Rule Optimization for Missouri River Reservoir System

TL;DR: For this multipurpose multireservoir system, simple data display and simulation modeling are found to be superior to classical regression techniques for inferring and refining promising operating rules from deterministic optimization results.
References
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Journal ArticleDOI

The Linear Decision Rule in Reservoir Management and Design: 1, Development of the Stochastic Model

TL;DR: In this paper, a linear decision rule is proposed to specify the release during any period of reservoir operation as the difference between the storage at the beginning of the period and a decision parameter for the period.
Journal ArticleDOI

Linear decision rule in reservoir management and design: 4. A rule that minimizes output variance

TL;DR: In this article, a generalized formulation of a linear decision rule in reservoir management and design is presented, and the necessary steps to implement it are carried out for a particular form of the new rule.
Journal ArticleDOI

Chance-Constrained Model of System of Reservoirs

TL;DR: In this paper chance-constrained programming is used to model a system of linked multipurpose reservoirs to determine an optimal operating policy for a given time sequence of minimum and maximum reservoir levels.