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Dynamic programming with successive approximation and relaxation strategy for long-term joint power generation scheduling of large-scale hydropower station group

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  • He, Zhongzheng
  • Wang, Chao
  • Wang, Yongqiang
  • Wei, Bowen
  • Zhou, Jianzhong
  • Zhang, Hairong
  • Qin, Hui

Abstract

The joint optimal operation of large-scale hydropower station group (LHSG) is faced with the higher dimension than that of cascade hydropower station, the demand for the efficient optimization techniques of the above problem is urgent. Integrating the characteristics of problem into optimization techniques is an effective way. Therefore, based on some previous research results, the approximate concavity and monotonicity characteristics of power generation utility function of dynamic programming with successive approximation (DPSA) in each stage is analyzed. Then, an improved DPSA with relaxation strategy (named DPSARS) based on the above mathematical derivations is proposed to solve the long-term joint power generation scheduling (LJPGS) of LHSG. Compared with DPSA, the time complexity exhibits quadratic increase with the number of discrete states, while DPSARS only exhibits linear increase. Then, in order to further test the convergence accuracy and efficiency of the proposed DPSARS, the model of the LJPGS problem of LHSG, composed of 61 hydropower stations in the upper reaches of the Yangtze River, is established. The experimental results show that DPSARS represents its competitive performance in solving the LJPGS problem of LHSG compared with other methods.

Suggested Citation

  • He, Zhongzheng & Wang, Chao & Wang, Yongqiang & Wei, Bowen & Zhou, Jianzhong & Zhang, Hairong & Qin, Hui, 2021. "Dynamic programming with successive approximation and relaxation strategy for long-term joint power generation scheduling of large-scale hydropower station group," Energy, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:energy:v:222:y:2021:i:c:s0360544221002097
    DOI: 10.1016/j.energy.2021.119960
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    Cited by:

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    6. Liao, Shengli & Liu, Huan & Liu, Benxi & Liu, Tian & Li, Chonghao & Su, Huaying, 2023. "Solution framework for short-term cascade hydropower system optimization operations based on the load decomposition strategy," Energy, Elsevier, vol. 277(C).
    7. Yang, Zhe & Wang, Yufeng & Yang, Kan, 2022. "The stochastic short-term hydropower generation scheduling considering uncertainty in load output forecasts," Energy, Elsevier, vol. 241(C).
    8. Feng, Suzhen & Zheng, Hao & Qiao, Yifan & Yang, Zetai & Wang, Jinwen & Liu, Shuangquan, 2022. "Weekly hydropower scheduling of cascaded reservoirs with hourly power and capacity balances," Applied Energy, Elsevier, vol. 311(C).
    9. Shuo Huang & Xinyu Wu & Yiyang Wu & Zheng Zhang, 2023. "Mid-Term Optimal Scheduling of Low-Head Cascaded Hydropower Stations Considering Inflow Unevenness," Energies, MDPI, vol. 16(17), pages 1-13, September.
    10. Fang, Zhou & Liao, Shengli & Cheng, Chuntian & Zhao, Hongye & Liu, Benxi & Su, Huaying, 2023. "Parallel improved DPSA algorithm for medium-term optimal scheduling of large-scale cascade hydropower plants," Renewable Energy, Elsevier, vol. 210(C), pages 134-147.
    11. Zhongzheng He & Chao Wang & Yongqiang Wang & Hairong Zhang & Heng Yin, 2022. "An Efficient Optimization Method for Long-term Power Generation Scheduling of Hydropower Station: Improved Dynamic Programming with a Relaxation Strategy," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1481-1497, March.
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    13. Yin, Linfei & Luo, Shikui & Ma, Chenxiao, 2021. "Expandable depth and width adaptive dynamic programming for economic smart generation control of smart grids," Energy, Elsevier, vol. 232(C).

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