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Joint Scheduling and Coordinating Operation of a Mega Hydropower System Based on Gaussian Radial Basis Functions and the Borg Algorithm in the Upper Yangtze River, China.

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Title: Joint Scheduling and Coordinating Operation of a Mega Hydropower System Based on Gaussian Radial Basis Functions and the Borg Algorithm in the Upper Yangtze River, China.
Authors: Guo, Shenglian1 (AUTHOR) slguo@whu.edu.cn, Li, Chenglong1,2 (AUTHOR), Sun, Bokai1 (AUTHOR), Wang, Xiaoya1,2 (AUTHOR), Li, Peng2 (AUTHOR), Guo, Le2 (AUTHOR)
Source: Energies (19961073). May2026, Vol. 19 Issue 10, p2352. 18p.
Subject Terms: *Scheduling, *Multi-objective optimization, *Radial basis functions, *Streamflow, *Hydroelectric power plants, *Reservoir ecology, *Water power
Geographic Terms: Yangtze River (China), China
Abstract: A large number of reservoirs (or hydropower plants) have been constructed for flood control and energy production in the past several decades in the Yangtze River basin in China. The conventional scheduling rule curves (Scheme A) were designed in the reservoir construction period and did not consider river flow alternation, which needs to be modified to increase comprehensive benefits in the reservoir operation period. In this study, six large-scale cascade reservoirs or mega hydropower systems constructed and operated by the China Yangtze Three Gorges Corporation were selected for this case study. The current joint scheduling plans of cascade reservoirs (Scheme B) were introduced, and a joint scheduling and multi-objective coordinating operation model (Scheme C) was proposed for this mega hydropower system. The Gaussian radial basis functions (GRBFs) were used to fit operation policies of each reservoir, and the Borg multi-objective evolutionary algorithm was selected to optimize three-objective functions for Scheme C. The observed daily flow data series at main hydrometric stations from 2003 to 2025 were used to simulate and compare different operation scheduling schemes. The results show that the performance of joint scheduling of cascade reservoirs (both Schemes B and C) is much better than the single-reservoir scheduling (Schemes A) with overall benefit; Scheme C-best achieves a comprehensive target of decreasing average annual spillway wastewater by 12.82 billion m3 (or a decrease of 28.5%), increasing average annual power generation by 31.02 billion kWh (or an increase of 10.7%), and improving average annual impoundment efficiency rate by 5.0%. The GRBFs can fit reservoir operation policies well, while the Borg multi-objective evolutionary algorithm can quickly converge with high-precision non-dominated solution sets. The proposed joint scheduling and multi-objective coordinating operation model will provide a scientific basis for achieving maximum benefits in flood protection and hydropower generation for the mega hydropower system. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 194141467
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Label: Title
  Group: Ti
  Data: Joint Scheduling and Coordinating Operation of a Mega Hydropower System Based on Gaussian Radial Basis Functions and the Borg Algorithm in the Upper Yangtze River, China.
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  Data: <searchLink fieldCode="AR" term="%22Guo%2C+Shenglian%22">Guo, Shenglian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> slguo@whu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Chenglong%22">Li, Chenglong</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+Bokai%22">Sun, Bokai</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Xiaoya%22">Wang, Xiaoya</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Peng%22">Li, Peng</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Guo%2C+Le%22">Guo, Le</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. May2026, Vol. 19 Issue 10, p2352. 18p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Scheduling%22">Scheduling</searchLink><br />*<searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Radial+basis+functions%22">Radial basis functions</searchLink><br />*<searchLink fieldCode="DE" term="%22Streamflow%22">Streamflow</searchLink><br />*<searchLink fieldCode="DE" term="%22Hydroelectric+power+plants%22">Hydroelectric power plants</searchLink><br />*<searchLink fieldCode="DE" term="%22Reservoir+ecology%22">Reservoir ecology</searchLink><br />*<searchLink fieldCode="DE" term="%22Water+power%22">Water power</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Yangtze+River+%28China%29%22">Yangtze River (China)</searchLink><br /><searchLink fieldCode="DE" term="%22China%22">China</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: A large number of reservoirs (or hydropower plants) have been constructed for flood control and energy production in the past several decades in the Yangtze River basin in China. The conventional scheduling rule curves (Scheme A) were designed in the reservoir construction period and did not consider river flow alternation, which needs to be modified to increase comprehensive benefits in the reservoir operation period. In this study, six large-scale cascade reservoirs or mega hydropower systems constructed and operated by the China Yangtze Three Gorges Corporation were selected for this case study. The current joint scheduling plans of cascade reservoirs (Scheme B) were introduced, and a joint scheduling and multi-objective coordinating operation model (Scheme C) was proposed for this mega hydropower system. The Gaussian radial basis functions (GRBFs) were used to fit operation policies of each reservoir, and the Borg multi-objective evolutionary algorithm was selected to optimize three-objective functions for Scheme C. The observed daily flow data series at main hydrometric stations from 2003 to 2025 were used to simulate and compare different operation scheduling schemes. The results show that the performance of joint scheduling of cascade reservoirs (both Schemes B and C) is much better than the single-reservoir scheduling (Schemes A) with overall benefit; Scheme C-best achieves a comprehensive target of decreasing average annual spillway wastewater by 12.82 billion m3 (or a decrease of 28.5%), increasing average annual power generation by 31.02 billion kWh (or an increase of 10.7%), and improving average annual impoundment efficiency rate by 5.0%. The GRBFs can fit reservoir operation policies well, while the Borg multi-objective evolutionary algorithm can quickly converge with high-precision non-dominated solution sets. The proposed joint scheduling and multi-objective coordinating operation model will provide a scientific basis for achieving maximum benefits in flood protection and hydropower generation for the mega hydropower system. [ABSTRACT FROM AUTHOR]
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        Value: 10.3390/en19102352
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 18
        StartPage: 2352
    Subjects:
      – SubjectFull: Scheduling
        Type: general
      – SubjectFull: Multi-objective optimization
        Type: general
      – SubjectFull: Radial basis functions
        Type: general
      – SubjectFull: Streamflow
        Type: general
      – SubjectFull: Hydroelectric power plants
        Type: general
      – SubjectFull: Reservoir ecology
        Type: general
      – SubjectFull: Water power
        Type: general
      – SubjectFull: Yangtze River (China)
        Type: general
      – SubjectFull: China
        Type: general
    Titles:
      – TitleFull: Joint Scheduling and Coordinating Operation of a Mega Hydropower System Based on Gaussian Radial Basis Functions and the Borg Algorithm in the Upper Yangtze River, China.
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          Name:
            NameFull: Guo, Shenglian
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            NameFull: Li, Chenglong
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            – D: 15
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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              Value: 19
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              Value: 10
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            – TitleFull: Energies (19961073)
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