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.
Highlights
Flooding in a river system is one of the most damaging natural disasters that humanity faces.Floods have enormous destructive strength, and usually cause serious consequences in terms of economic loss and human fatalities [1,2]
This paper proposes a risk analysis model for reservoir real-time optimal operation using the scenario tree-based stochastic optimization method
In the two sections, we introduce a risk analysis model to calculate the risk of optimal reservoir operation and evaluate the uncertainty propagation process from flood forecast to optimal operation
Summary
Flooding in a river system is one of the most damaging natural disasters that humanity faces. Floods have enormous destructive strength, and usually cause serious consequences in terms of economic loss and human fatalities [1,2]. In order to prevent floods, many river basins have built dams and reservoirs to store and control flood water. A typical combination of engineering and non-engineering measures, plays an important role in the flood management of river basins [3]. Reservoir flood control operation is limited to deterministic environments, where reservoir release decisions are implemented without consideration of uncertainties [4,5,6]. Plenty of uncertainties exist in the reservoir flood control operation [7,8]
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