Stochastic Optimization of Reservoir Operation by Applying Hedging Rules
Publication: Journal of Water Resources Planning and Management
Volume 147, Issue 2
Abstract
The high percentage of reliability of water resources systems has always been considered as a positive advantage by users. However, in arid and semiarid areas in which the flow rate of the reservoir is highly variable, it makes sense to reduce the system reliability and allocate less water to demand sites in order to avoid critical conditions such as reservoir depletion and to reduce severity of failure in low water months. In this study, a parameterization-simulation-optimization (PSO) based operation model was used whereby the efficiency of two different hedging rules was studied based on one-dimensional and two-dimensional relationships between release, storage, and input flow considering the stochastic conditions of the input flow. The optimal hedging parameters were determined through linking the reservoir simulation model to the multiobjective imperialist competitive algorithm. The model combines stochastic and historical monthly flow data of the Marun River (10,800 months in total) to optimize the system and extract hedging rules for the Marun Dam reservoir in Iran. In order to validate the developed model, the combination of stochastic data and residual historical data (612 months in total) was used. The model results from two different hedging policies were then compared with those from standard operating policies (SOPs). The results indicated that PSO model based on one-dimensional reservoir hedging policy compared to two-dimensional hedging methods and SOP was able to manage needs allocation in dry months and prevent reservoir depletion.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
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Journal of Water Resources Planning and Management
Volume 147 • Issue 2 • February 2021
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Dec 8, 2019
Accepted: Aug 14, 2020
Published online: Nov 18, 2020
Published in print: Feb 1, 2021
Discussion open until: Apr 18, 2021
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