An Optimal Operation Model for Hydropower Stations Considering Inflow Forecasts with Different Lead-Times
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To make full use of inflow forecasts with different lead times, a new reservoir operation model that considers the long-, medium- and short-term inflow forecasts (LMS-BSDP) for the real-time operation of hydropower stations is presented in this paper. First, a hybrid model, including a multiple linear regression model and the Xinanjiang model, is developed to obtain the 10-day inflow forecasts, and ANN models with the circulation indexes as inputs are developed to obtain the seasonal inflow forecasts. Then, the 10-day inflow forecast is divided into two segments, the first 5 days and the second 5 days, and the seasonal inflow forecast is deemed as the long-term forecast. Next, the three inflow forecasts are coupled using the Bayesian theory to develop LMS-BSDP model and the operation policies are obtained. Finally, the decision processes for the first 5 days and the entire 10 days are made according to their operation policies and the three inflow forecasts, respectively. The newly developed model is tested with the Huanren hydropower station located in China and compared with three other stochastic dynamic programming models. The simulation results demonstrate that LMS-BSDP performs best with higher power generation due to its employment of the long-term runoff forecast. The novelties of the present study lies in that it develops a new reservoir operation model that can use the long-, medium- and short-term inflow forecasts, which is a further study about the combined use of the inflow forecasts with different lead times based on the existed achievements.
KeywordsHydropower station Different lead-times Inflow forecasts LMS-BSDP model
This work is supported by the National Key Research and Development Program of China (Grant No. 2017YFC0406005), the National Natural Science Foundation of China (Grant No. 91547111, 51609025, 51709108), Scientific Research Foundation for High-level Talents of North China University of Water Resources and Electric Power (Grant No. 201702013), the National Natural Science Foundation of Chongqing (Grant No. cstc2015jcyjA90015), Open Fund Approval (Grant No. SKHL1427), and Scientific Research Foundation of Chongqing Jiaotong University (Grant No. 15JDKJC-B019).
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