Impact of Runoff Forecast Errors on the Optimal Operation of Reservoir Power Generation

Chongxun Mo, Yuyi Zhong, Keke Huang (), Mengxiang Bao, Yi Huang, Changdi Tao and Huabo Jiang
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Chongxun Mo: Guangxi University
Yuyi Zhong: Guangxi University
Keke Huang: Guangxi University
Mengxiang Bao: Guangxi Guilin Water Resource and Electric Power Survey, Design and Research Institute
Yi Huang: Guangxi Water & Power Design Institute Co., Ltd
Changdi Tao: Guangxi Water & Power Design Institute Co., Ltd
Huabo Jiang: Guangxi Water & Power Design Institute Co., Ltd

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2025, vol. 39, issue 13, No 21, 7175-7197

Abstract: Abstract The optimal operation of reservoir power generation aims to improve power generation benefits through rational water allocation while considering flood control, water supply, and ecological needs. However, runoff forecast errors can affect the reliability of operation schemes. To assess their impact, this study first developed a convolutional neural network (CNN), gated recurrent unit (GRU), and CNN-GRU, and an optimal model for runoff forecasting was selected. Then, a best-fit error probability distribution function was identified from the normal distribution, logistic distribution, and t location-scale distribution. Simulated error sets of runoff forecasts were then generated using the Monte Carlo simulation. Next, the simulated runoff sets considering forecast errors were input into the optimal operation model of reservoir power generation, which was solved using the differential evolution (DE) algorithm. Finally, the impact of runoff forecast errors on the optimal operation of reservoir power generation during the dry year was evaluated. The results in the Chengbi River Basin revealed that: (1) CNN-GRU achieved the best forecast performance, with an NSE of 0.931; (2) the t location-scale distribution was the most suitable error probability distribution, and the average relative errors in the dry year obtained through Monte Carlo simulation ranged from − 0.014 to 0.097; (3) as the average error rate of the forecast runoff increased from 5% to 15%, the deviation between the forecast-based and ideal annual power generation ranged from − 1.10% to -5.84%. This study may provide an effective tool and theoretical support for analysing runoff forecast errors on the optimal operation of reservoirs.

Keywords: Deep learning; Monte Carlo simulation; Runoff forecast; Error simulation; Optimal operation of reservoir power generation; Risk analysis of power generation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11269-025-04291-7

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