A method of direct, real-time forecasting of downstream water levels via hydropower station reregulation: A case study from Gezhouba Hydropower Plant, China.

• A new method of online, real-time prediction of downstream water levels is posed. • The Gezhouba Hydropower Plant in China is used as a reregulation case study. • A back propagation neural network-based prediction model is utilized. • Model water level predictions could fully meet real-time station dispatching needs. The forecasting accuracy of downstream water levels greatly impacts the economic operation of reregulating hydropower stations. If present, large errors in water level forecasting may require the hydropower station to discard water, which decreases the revenue from hydropower generation. Especially when the reregulating hydropower station undertakes the task of peak load regulation of the power grid, its power output may change drastically over a short period of time. Such large changes in the discharged flow of the hydropower station may inevitably cause an unsteady flow, which can lead to large fluctuations in the water levels of downstream rivers. In this case, the current forecasting methods, such as standard rating curves and empirical formulae, may inevitably result in large deviations when forecasting the change in the downstream water level. In order to reduce the volume of water discarded as a result of forecasting errors, a back propagation neural network-based forecasting model for downstream water levels of a reregulating hydropower station was constructed. The model enabled the direct, accurate, real-time forecasting of changes in downstream water levels by using measurable operation data from a hydropower station and process data on downstream water level changes. This method was applied to the actual hydropower generation dispatch of the Gezhouba Hydropower Plant in China. The results showed that as the peak load regulation volume increased, the forecasting errors of two conventional methods (standard rating curves and empirical formulae) became continuously superimposed. However, the water level forecasting error of the neural network-based method was small and could fully meet the real-time dispatching requirements of the hydropower station. In particular, under large peak load regulations, the maximum absolute values of the forecasting errors of the two conventional methods were close to 1 m, while that of the neural network-based method could be controlled within 0.3 m. [ABSTRACT FROM AUTHOR]