Research on Economic Operation of Cascade Small Hydropower Stations Within Plants Based on Refined Efficiency Models

Daohong Wei, Chunpeng Feng and Dong Liu ()
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Daohong Wei: College of Energy and Power Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
Chunpeng Feng: College of Energy and Power Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
Dong Liu: College of Energy and Power Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China

Energies, 2025, vol. 18, issue 4, 1-18

Abstract: In order to enhance the overall power generation efficiency of cascade hydropower, it is essential to conduct modelling optimization of its in-plant operation. However, existing studies have devoted minimal attention to the detailed modelling of turbine operating performance curves within the in-plant economic operation model. This represents a significant challenge to the practical application of the optimization results. This study presents a refined model of a hydraulic turbine operating performance curve, which was established by combining a particle swarm optimization (PSO) algorithm and a backpropagation (BP) neural network. The model was developed using a cascade small hydropower group as an illustrative example. On this basis, an in-plant economic operation model of a cascade small hydropower group was established, which is based on the principle of ’setting electricity by water’ and has the goal of maximizing power generation. The model was optimized using a genetic algorithm, which was employed to optimize the output of the units. In order to ascertain the efficacy of the methodology proposed in this study, typical daily operational scenarios of a cascade small hydropower group were selected for comparison. The results demonstrate that, in comparison with the actual operational strategy, the proposed model and method enhance the total output by 3.38%, 2.11%, and 3.56%, respectively, across the three typical scenarios. This method enhances the efficiency of power generation within the cascade small hydropower group and demonstrates substantial engineering application value.

Keywords: hydraulic turbine; operating performance curve; PSO-BP neural network; in-plant economic operation; genetic algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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