Abstract
Today, variable flow pattern, which uses static rule curves, is considered one of the challenges of reservoir operation. One way to overcome this problem is to develop forecast-based rule curves. However, managers must have an estimate of the influence of forecast accuracy on operation performance due to the intrinsic limitations of forecast models. This study attempts to develop a forecast model and investigate the effects of the corresponding accuracy on the operation performance of two conventional rule curves. To develop a forecast model, two methods according to autocorrelation and wrapper-based feature selection models are introduced to deal with the wavelet components of inflow. Finally, the operation performances of two polynomial and hedging rule curves are investigated using forecasted and actual inflows. The results of applying the model to the Dez reservoir in Iran visualized that a 4% improvement in the correlation coefficient of the coupled forecast model could reduce the relative deficit of the polynomial rule curve by 8.1%. Moreover, with 2% and 10% improvement in the Willmott and Nash—Sutcliffe indices, the same 8.1% reduction in the relative deficit can be expected. Similar results are observed for hedging rules where increasing forecast accuracy decreased the relative deficit by 15.5%. In general, it was concluded that hedging rule curves are more sensitive to forecast accuracy than polynomial rule curves are.
Highlights
Published: 2 October 2021Recently, water resource models have been used increasingly to study the vulnerability of human systems under probable water scarcity [1]
The results show that hydropower energy generation increases compared to conventional rule curves
The results showed that the forecast term could improve the evaluation criteria against standard operation policy
Summary
Published: 2 October 2021Recently, water resource models have been used increasingly to study the vulnerability of human systems under probable water scarcity [1]. The forecast accuracy of the future surface and sub-surface inflows, which is always associated with different uncertainties, influences the efficiency of water resource planning and management [2,3]. The release rate is determined using operating rule curves. These curves are fitted based on historical time series, and the release rate somehow depends on past events [5]. Possible inflow changes could decrease operating performance. To overcome this problem, a wide range of studies has been investigated to combine future conditions into operating policy. Liu et al [6] suggested a Bayesian-based deep learning model to assess the impact of streamflow uncertainty on the efficiency of operation rule curves
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