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A three-stage decision-making study on capacity configuration of hydropower-wind-photovoltaic-storage complementary systems considering uncertainty

Author

Listed:
  • Li, Wanying
  • Dong, Fugui
  • Liu, Jiamei
  • Wang, Peijun
  • Zhao, Xinru

Abstract

The hydropower-wind-photovoltaic-storage complementary system can effectively facilitate the consumption of new energy, which serves as a viable approach to achieving the dual-carbon goal. Therefore, this study proposes a three-stage decision-making framework for the hydropower-wind-photovoltaic-storage complementary system capacity configuration: the first stage involves generating 8760-h scenarios of hydropower-wind-photovoltaic joint power output using Latin hypercube sampling and fuzzy C-means clustering; the second stage entails the generation of multi-objective capacity configuration Pareto schemes using the improved non-dominated sorting genetic algorithm III and Gurobi; and the third stage involves decision-making on the schemes based on the dynamic social network-subjective and objective weight-multi-attribute decision-making method. Finally, the validity of the framework is verified and discussed based on the completed #23 hydropower plant. The results demonstrate that hydropower-wind-photovoltaic joint power output effectively mitigates long-term power fluctuations. Twenty typical scenarios are generated through scenario reduction. The improved solution algorithm achieves rapid convergence, with all 50 schemes residing on the Pareto front after three generations. The optimal capacity configuration ratio of hydropower-wind-photovoltaic-storage is 1080:470:578:207, and a more conservative capacity configuration investment strategy is more appropriate. If the #23 hydropower plant lacks a regulating reservoir, the net income from constructing an additional 210MW/2100 MW·h of pumped storage is maximized.

Suggested Citation

  • Li, Wanying & Dong, Fugui & Liu, Jiamei & Wang, Peijun & Zhao, Xinru, 2024. "A three-stage decision-making study on capacity configuration of hydropower-wind-photovoltaic-storage complementary systems considering uncertainty," Energy, Elsevier, vol. 313(C).
    • Handle: RePEc:eee:energy:v:313:y:2024:i:c:s036054422403785x
    DOI: 10.1016/j.energy.2024.134007
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    4. Zhao, Zhipeng & Wang, Qibao & Cheng, Chuntian & Kang, Yongxi, 2025. "A scenario-based multi-agent reinforcement learning approach for efficient solving to long-term optimization of cascade hydropower reservoirs," Energy, Elsevier, vol. 340(C).

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