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Water 2018, 10(5), 606; https://doi.org/10.3390/w10050606

Risk Analysis for Reservoir Real-Time Optimal Operation Using the Scenario Tree-Based Stochastic Optimization Method

1
College of Hydrology and Water Resources, Hohai University, No.1 Xikang Road, Nanjing 210098, China
2
State Key Laboratory of Hydrology Water Resources and Hydraulic Engineering, Hohai University, No.1 Xikang Road, Nanjing 210098, China
3
School of Earth Sciences and Engineering, Hohai University, No.1 Xikang Road, Nanjing 210098, China
4
Department of Civil and Environmental Engineering, University of California, Los Angeles, CA 90095, USA
*
Author to whom correspondence should be addressed.
Received: 23 March 2018 / Revised: 26 April 2018 / Accepted: 4 May 2018 / Published: 6 May 2018
(This article belongs to the Section Water Resources Management and Governance)
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Abstract

The inherent uncertainty of inflow forecasts hinders the reservoir real-time optimal operation. This paper proposes a risk analysis model for reservoir real-time optimal operation using the scenario tree-based stochastic optimization method. We quantify the probability distribution of inflow forecast uncertainty by developing the relationship between two forecast accuracy metrics and the standard deviation of relative forecast error. An inflow scenario tree is generated via Monte Carlo simulation to represent the uncertain inflow forecasts. We establish a scenario tree-based stochastic optimization model to explicitly incorporate inflow forecast uncertainty into the stochastic optimization process. We develop a risk analysis model based on the principle of maximum entropy (POME) to evaluate the uncertainty propagation process from flood forecasts to optimal operation. We apply the proposed methodology to a flood control system in the Daduhe River Basin, China. In addition, numerical experiments are carried out to investigate the effect of two different forecast accuracy metrics and different forecast accuracy levels on reservoir optimal flood control operation as well as risk analysis. The results indicate that the proposed methods can provide decision-makers with valuable risk information for guiding reservoir real-time optimal operation and enable risk-informed decisions to be made with higher reliabilities. View Full-Text
Keywords: real-time reservoir operation; inflow forecast uncertainty; risk analysis; stochastic optimization; scenario tree real-time reservoir operation; inflow forecast uncertainty; risk analysis; stochastic optimization; scenario tree
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Sun, Y.; Zhu, F.; Chen, J.; Li, J. Risk Analysis for Reservoir Real-Time Optimal Operation Using the Scenario Tree-Based Stochastic Optimization Method. Water 2018, 10, 606.

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