Research on Economic Operation of Cascade Small Hydropower Stations Within Plants Based on Refined Efficiency Models
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-pla...
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MDPI AG
2025-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/4/964 |
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| author | Daohong Wei Chunpeng Feng Dong Liu |
| author_facet | Daohong Wei Chunpeng Feng Dong Liu |
| author_sort | Daohong Wei |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-912ba70edf6e4b9ea34e67cb8f2075fe |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-912ba70edf6e4b9ea34e67cb8f2075fe2025-08-20T02:44:56ZengMDPI AGEnergies1996-10732025-02-0118496410.3390/en18040964Research on Economic Operation of Cascade Small Hydropower Stations Within Plants Based on Refined Efficiency ModelsDaohong Wei0Chunpeng Feng1Dong Liu2College of Energy and Power Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, ChinaCollege of Energy and Power Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, ChinaCollege of Energy and Power Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, ChinaIn 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.https://www.mdpi.com/1996-1073/18/4/964hydraulic turbineoperating performance curvePSO-BP neural networkin-plant economic operationgenetic algorithm |
| spellingShingle | Daohong Wei Chunpeng Feng Dong Liu Research on Economic Operation of Cascade Small Hydropower Stations Within Plants Based on Refined Efficiency Models Energies hydraulic turbine operating performance curve PSO-BP neural network in-plant economic operation genetic algorithm |
| title | Research on Economic Operation of Cascade Small Hydropower Stations Within Plants Based on Refined Efficiency Models |
| title_full | Research on Economic Operation of Cascade Small Hydropower Stations Within Plants Based on Refined Efficiency Models |
| title_fullStr | Research on Economic Operation of Cascade Small Hydropower Stations Within Plants Based on Refined Efficiency Models |
| title_full_unstemmed | Research on Economic Operation of Cascade Small Hydropower Stations Within Plants Based on Refined Efficiency Models |
| title_short | Research on Economic Operation of Cascade Small Hydropower Stations Within Plants Based on Refined Efficiency Models |
| title_sort | research on economic operation of cascade small hydropower stations within plants based on refined efficiency models |
| topic | hydraulic turbine operating performance curve PSO-BP neural network in-plant economic operation genetic algorithm |
| url | https://www.mdpi.com/1996-1073/18/4/964 |
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