Impact of Turbine Efficiency Characteristics Changes on Economic Benefits of Hydropower Plants in Short-Term Scheduling

To quantitatively analyze the impact of turbine efficiency characteristics on economic benefits of hydropower plants, this work first establishes a model using the deeplearning neural network (DNN) to calculate efficiency based on the turbine efficiency hill diagram. Then, based on the efficiency model, a short-term scheduling model for hydropower unit commitment (HUC) in the hydro-wind-photovoltaic hybrid energy system is established. Finally, taking a hydropower station in China as a case, the impact of changes in turbine efficiency characteristics on the economic benefits of the hydropower plant is quantitatively analyzed based on the HUC results. The findings demonstrate that: (1) The efficiency model based on DNN achieves a correlation coefficient of 0.9997 and a mean squared error of 3.95×10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−5</sup>, indicating low error and good fitting quality of the model; (2) For a hydropower station with a total installed capacity of 5,100 MW, an average difference of 0.66% in efficiency of hydropower unit can lead to variations in water consumption during daily scheduling in August. This difference results in an economic benefit of ¥11,807.18 for the month and an annual economic benefit of ¥1.3460 million.