IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v299y2024ics0360544224012209.html
   My bibliography  Save this article

Dynamic quantitative assessment of multiple uncertainty sources in future hydropower generation prediction of cascade reservoirs with hydrological variations

Author

Listed:
  • Zhou, Shuai
  • Wang, Yimin
  • Su, Hui
  • Chang, Jianxia
  • Huang, Qiang
  • Li, Ziyan

Abstract

Hydropower is the dominant renewable energy source, which can significantly reduce carbon dioxide emissions while providing electric energy. It is a crucial way to achieve the dual-carbon goal of carbon peak and carbon neutrality. However, climate change inevitably threatens hydropower, either directly or indirectly. Global climate models (GCMs) and hydrological models (HMs) under different representative concentration pathways (RCPs) have been combined to investigate future hydropower generation responses to climate change. The contribution of multiple uncertainty sources to multi-scale hydropower generation uncertainties with hydrological variations remains unclear. Therefore, to address the above challenge, a three-step framework was proposed in this paper: (1) predicting future inflows to reservoirs using three HMs (HBV, SWAT and XAJ) with different structures driven by six GCMs (BNU-ESM, GFDL-ESM2G, GFDL-ESM2M, CanESM2, MIROC-ESM-CHEM and CSIRO-MK3-6-0) under three RCPs (i.e., RCP2.6, RCP4.5 and RCP8.5); (2) diagnosing future hydrological conditions using the Comprehensive Differential Split-sample Test; (3) predicting future hydropower generation of cascade reservoirs by developing a cascade hydropower generation model and dynamically quantifying the contribution of multiple uncertainty sources to multi-scale hydropower generation uncertainties with hydrological conditions based on the variance decomposition method through subsampling. The results indicate that GCMs, HMs, climate emission scenarios and their interactions significantly impacted the uncertainty of hydropower generation with hydrological variations. In the flood season, the relative contributions of GCMs and HMs were the largest, accounting for about 40 %. The interaction effects of multiple uncertainty sources gradually increased before, after and during the non-flood season, with its relative contribution accounting for about 36 %. These findings can facilitate the active response to the new challenges of strategic hydropower energy development under climate change, reducing the losses caused by climate change.

Suggested Citation

  • Zhou, Shuai & Wang, Yimin & Su, Hui & Chang, Jianxia & Huang, Qiang & Li, Ziyan, 2024. "Dynamic quantitative assessment of multiple uncertainty sources in future hydropower generation prediction of cascade reservoirs with hydrological variations," Energy, Elsevier, vol. 299(C).
    • Handle: RePEc:eee:energy:v:299:y:2024:i:c:s0360544224012209
    DOI: 10.1016/j.energy.2024.131447
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224012209
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.131447?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Młyński, Dariusz & Książek, Leszek & Bogdał, Andrzej, 2024. "Meteorological drought effect for Central Europe's hydropower potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    2. Liu, Yuan & Ji, Changming & Wang, Yi & Zhang, Yanke & Jiang, Zhiqiang & Ma, Qiumei & Hou, Xiaoning, 2023. "Effect of the quality of streamflow forecasts on the operation of cascade hydropower stations using stochastic optimization models," Energy, Elsevier, vol. 273(C).
    3. Li, Yanbin & Zhao, Ke & Zhang, Feng, 2023. "Identification of key influencing factors to Chinese coal power enterprises transition in the context of carbon neutrality: A modified fuzzy DEMATEL approach," Energy, Elsevier, vol. 263(PA).
    4. Shuai Zhou & Yimin Wang & Ziyan Li & Jianxia Chang & Aijun Guo, 2021. "Correction to: Quantifying the Uncertainty Interaction Between the Model Input and Structure on Hydrological Processes," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 3937-3937, September.
    5. Bortoluzzi, Mirian & Furlan, Marcelo & dos Reis Neto, José Francisco, 2022. "Assessing the impact of hydropower projects in Brazil through data envelopment analysis and machine learning," Renewable Energy, Elsevier, vol. 200(C), pages 1316-1326.
    6. Shuai Zhou & Yimin Wang & Ziyan Li & Jianxia Chang & Aijun Guo, 2021. "Quantifying the Uncertainty Interaction Between the Model Input and Structure on Hydrological Processes," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 3915-3935, September.
    7. Han, Shuo & He, Mengjiao & Zhao, Ziwen & Chen, Diyi & Xu, Beibei & Jurasz, Jakub & Liu, Fusheng & Zheng, Hongxi, 2023. "Overcoming the uncertainty and volatility of wind power: Day-ahead scheduling of hydro-wind hybrid power generation system by coordinating power regulation and frequency response flexibility," Applied Energy, Elsevier, vol. 333(C).
    8. Wang, Xiaoyang & Yu, Biying & An, Runying & Sun, Feihu & Xu, Shuo, 2022. "An integrated analysis of China’s iron and steel industry towards carbon neutrality," Applied Energy, Elsevier, vol. 322(C).
    9. Wang, Xiaokui & Bamisile, Olusola & Chen, Shuheng & Xu, Xiao & Luo, Shihua & Huang, Qi & Hu, Weihao, 2022. "Decarbonization of China's electricity systems with hydropower penetration and pumped-hydro storage: Comparing the policies with a techno-economic analysis," Renewable Energy, Elsevier, vol. 196(C), pages 65-83.
    10. Zeng, Bo & He, Chengxiang & Mao, Cuiwei & Wu, You, 2023. "Forecasting China's hydropower generation capacity using a novel grey combination optimization model," Energy, Elsevier, vol. 262(PA).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Feng Dong & Guoqing Li & Yajie Liu & Qing Xu & Caixia Li, 2023. "Spatial-Temporal Evolution and Cross-Industry Synergy of Carbon Emissions: Evidence from Key Industries in the City in Jiangsu Province, China," Sustainability, MDPI, vol. 15(5), pages 1-27, February.
    2. Wang, Jin & Zhao, Zhipeng & Zhou, Jinglin & Cheng, Chuntian & Su, Huaying, 2024. "Co-optimization for day-ahead scheduling and flexibility response mode of a hydro–wind–solar hybrid system considering forecast uncertainty of variable renewable energy," Energy, Elsevier, vol. 311(C).
    3. Zhang, Huaiyuan & Liao, Kai & Yang, Jianwei & Zheng, Shunwei & He, Zhengyou, 2024. "Frequency-constrained expansion planning for wind and photovoltaic power in wind-photovoltaic-hydro-thermal multi-power system," Applied Energy, Elsevier, vol. 356(C).
    4. Li, Hui & Nie, Weige & Duan, Huiming, 2024. "A Haavelmo grey model based on economic growth and its application to energy industry investments," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    5. Yang, Zhaofu & Liu, Hong & Yuan, Yongna & Li, Muhua, 2024. "Can renewable energy development facilitate China's sustainable energy transition? Perspective from Energy Trilemma," Energy, Elsevier, vol. 304(C).
    6. Yang, Mao & Guo, Yunfeng & Huang, Tao & Fan, Fulin & Ma, Chenglian & Fang, Guozhong, 2024. "Wind farm cluster power prediction based on graph deviation attention network with learnable graph structure and dynamic error correction during load peak and valley periods," Energy, Elsevier, vol. 312(C).
    7. Li, Xudong & Yang, Weijia & Liao, Yiwen & Zhang, Shushu & Zheng, Yang & Zhao, Zhigao & Tang, Maojia & Cheng, Yongguang & Liu, Pan, 2024. "Short-term risk-management for hydro-wind-solar hybrid energy system considering hydropower part-load operating characteristics," Applied Energy, Elsevier, vol. 360(C).
    8. Jiang, Hong-Dian & Liu, Yan-xin & Wang, Hanxu & Li, Huajiao & Jiang, Yutong, 2024. "An economy-wide and environmental assessment of an imported supply shortage for iron ore: The case of China," Economic Analysis and Policy, Elsevier, vol. 83(C), pages 606-617.
    9. Zhang, Boling & Wang, Qian & Wang, Sixia & Tong, Ruipeng, 2023. "Coal power demand and paths to peak carbon emissions in China: A provincial scenario analysis oriented by CO2-related health co-benefits," Energy, Elsevier, vol. 282(C).
    10. Hu, Hang & Yang, Lingzhi & Yang, Sheng & Zou, Yuchi & Wang, Shuai & Chen, Feng & Guo, Yufeng, 2024. "Development and assessment of an integrated wind energy system for green steelmaking based on electric arc furnace route," Energy, Elsevier, vol. 302(C).
    11. Chuanbo Zhang & Sihong Cheng & Yali Tong & Guoliang Li & Tao Yue, 2025. "An Approach to CO 2 Emission Reduction in the Iron and Steel Industry: Research Status and Development Trends of Integrated Absorption-Mineralization Technologies," Sustainability, MDPI, vol. 17(2), pages 1-30, January.
    12. Shu, Xingsheng & Ding, Wei & Peng, Yong & Wang, Ziru, 2024. "Value of long-term inflow forecast for hydropower operation: A case study in a low forecast precision region," Energy, Elsevier, vol. 298(C).
    13. Fang Wan & Jizu Li & Yunfei Han & Xilong Yao, 2024. "Research of the Impact of Hydrogen Metallurgy Technology on the Reduction of the Chinese Steel Industry’s Carbon Dioxide Emissions," Sustainability, MDPI, vol. 16(5), pages 1-24, February.
    14. Lee, Hwarang, 2023. "Decarbonization strategies for steel production with uncertainty in hydrogen direct reduction," Energy, Elsevier, vol. 283(C).
    15. Xu, Yan & Yu, Qi & Du, Pei & Wang, Jianzhou, 2024. "A paradigm shift in solar energy forecasting: A novel two-phase model for monthly residential consumption," Energy, Elsevier, vol. 305(C).
    16. Mahfoud, Rabea Jamil & Alkayem, Nizar Faisal & Zhang, Yuquan & Zheng, Yuan & Sun, Yonghui & Alhelou, Hassan Haes, 2023. "Optimal operation of pumped hydro storage-based energy systems: A compendium of current challenges and future perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 178(C).
    17. Zhong, Mingwei & Fan, Jingmin & Luo, Jianqiang & Xiao, Xuanyi & He, Guanglin & Cai, Rui, 2024. "InfoCAVB-MemoryFormer: Forecasting of wind and photovoltaic power through the interaction of data reconstruction and data augmentation," Applied Energy, Elsevier, vol. 371(C).
    18. Zhu, Shijie & Shi, Xilin & Yang, Chunhe & Bai, Weizheng & Wei, Xinxing & Yang, Kun & Li, Peng & Li, Hang & Li, Yinping & Wang, Guibin, 2024. "Site selection evaluation for salt cavern hydrogen storage in China," Renewable Energy, Elsevier, vol. 224(C).
    19. Liu, Jia & Ma, Tao & Wu, Huijun & Yang, Hongxing, 2023. "Study on optimum energy fuel mix for urban cities integrated with pumped hydro storage and green vehicles," Applied Energy, Elsevier, vol. 331(C).
    20. Hugo Gaspar Hernandez-Palma & Jonny Rafael Plaza Alvarado & Jesús Enrique García Guiliany & Guilherme Luiz Dotto & Claudete Gindri Ramos, 2024. "Implications of Machine Learning in the Generation of Renewable Energies in Latin America from a Globalized Vision: A Systematic Review," International Journal of Energy Economics and Policy, Econjournals, vol. 14(2), pages 1-10, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:299:y:2024:i:c:s0360544224012209. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.