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Two-stage stochastic optimal operation model for hydropower station based on the approximate utility function of the carryover stage

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  • Tan, Qiao-feng
  • Lei, Xiao-hui
  • Wen, Xin
  • Fang, Guo-hua
  • Wang, Xu
  • Wang, Chao
  • Ji, Yi
  • Huang, Xian-feng

Abstract

Challenge remains to find the optimal carryover storage to balance the immediate and carryover utilities for long-term hydropower reservoir operation due to high uncertainties of long-term forecasts. Thus, this paper develops a two-stage stochastic optimal operation model to dynamically decide the optimal carryover storage. First, a successive iteration method based on periodic Markov characteristics of reservoir operation is proposed to obtain the approximate utility function of the carryover stage. Then, three two-stage stochastic optimal operation models based on different forecast accuracy (no forecasts, perfect forecasts, and uncertainty forecasts) are developed to guide the long-term hydropower reservoir operation. The applications shows that: 1) the back propagation neural network can approximate the utility function of the carryover stage with a high accuracy and avoid the need to predetermine the function type; 2) the approximate utility function of the carryover stage increases with the carryover storage and current inflow, and it changes gradually from a nearly linear surface to an approximate concave surface with the shift from the dry season to the flood season; 3) two-stage stochastic optimal operation models outperform the conventional operating rules and conventional optimization method in guiding the long-term hydropower operation.

Suggested Citation

  • Tan, Qiao-feng & Lei, Xiao-hui & Wen, Xin & Fang, Guo-hua & Wang, Xu & Wang, Chao & Ji, Yi & Huang, Xian-feng, 2019. "Two-stage stochastic optimal operation model for hydropower station based on the approximate utility function of the carryover stage," Energy, Elsevier, vol. 183(C), pages 670-682.
  • Handle: RePEc:eee:energy:v:183:y:2019:i:c:p:670-682
    DOI: 10.1016/j.energy.2019.05.116
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    References listed on IDEAS

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    5. Chaoyang Chen & Hualing Liu & Yong Xiao & Fagen Zhu & Li Ding & Fuwen Yang, 2022. "Power Generation Scheduling for a Hydro-Wind-Solar Hybrid System: A Systematic Survey and Prospect," Energies, MDPI, vol. 15(22), pages 1-31, November.
    6. He, Zhongzheng & Zhou, Jianzhong & Qin, Hui & Jia, Benjun & He, Feifei & Liu, Guangbiao & Feng, Kuaile, 2020. "A fast water level optimal control method based on two stage analysis for long term power generation scheduling of hydropower station," Energy, Elsevier, vol. 210(C).
    7. Li, Xiao & Liu, Pan & Cheng, Lei & Cheng, Qian & Zhang, Wei & Xu, Shitian & Zheng, Yalian, 2023. "Strategic bidding for a hydro-wind-photovoltaic hybrid system considering the profit beyond forecast time," Renewable Energy, Elsevier, vol. 204(C), pages 277-289.
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