IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v393y2025ics0306261925008025.html

Using stochastic dual dynamic programming to design long-term operation policy of hydro-wind-solar energy systems considering multiple coupled uncertainties and end-of-year carryover storage

Author

Listed:
  • Jin, Xiaoyu
  • Cheng, Chuntian
  • Cai, Shubing
  • Yan, Lingzhi
  • Zhao, Zhipeng

Abstract

Hydropower with reservoirs is increasingly important for balancing seasonal variability of growing variable renewable energy (VRE) through its reservoir regulation capability. However, the coupling of the seasonal variability and randomness of VRE with the stochastic nature of inflows makes it extremely challenging to manage long-term hydropower operations related to generation decisions within the current scheduling periods and future end-of-year carryover storage control. To address these challenges, we propose a stochastic dual dynamic programming-based framework for designing long-term hydro-wind-solar complementary operation policies. Inflow and VRE output uncertainties are captured by two different approaches: Markov chain and AutoRegressive Moving Average. These approaches enable the integration of stage-wise dependent randomness into the stochastic decision-making process. Model reconstruction techniques based on Disjunctive Programming are proposed to transform stage-wise nonlinear models into linear ones. Subsequently, Benders cuts families are constructed to constrain the feasible decision space related to hydropower operation and stochastic parameters, while managing the end-of-year carryover storage requirement. Case studies of a large-scale hydro-wind-solar energy system in China indicate that the proposed framework can derive effective complementary operation policies considering future reservoir storage management requirements under multiple coupled uncertainties. Real simulation results indicate that the framework can effectively enhance channel utilization by leveraging hydropower flexibility to support VRE integration, with the monthly average channel utilization rate exceeding 80 %. Besides, hydro-wind-solar complementary operation policies with varying end-of-year carryover storage requirements can be designed, with lower storage requirements trending to enhance hydropower output in a hydro-wind-solar complementary mode.

Suggested Citation

  • Jin, Xiaoyu & Cheng, Chuntian & Cai, Shubing & Yan, Lingzhi & Zhao, Zhipeng, 2025. "Using stochastic dual dynamic programming to design long-term operation policy of hydro-wind-solar energy systems considering multiple coupled uncertainties and end-of-year carryover storage," Applied Energy, Elsevier, vol. 393(C).
  • Handle: RePEc:eee:appene:v:393:y:2025:i:c:s0306261925008025
    DOI: 10.1016/j.apenergy.2025.126072
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261925008025
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2025.126072?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Jin, Xiaoyu & Liu, Benxi & Liao, Shengli & Cheng, Chuntian & Zhang, Yi & Jia, Zebin, 2024. "Assessing hydropower capability for accommodating variable renewable energy considering peak shaving of multiple power grids," Energy, Elsevier, vol. 305(C).
    2. Shi, Yunhong & Wang, Honglei & Li, Chengjiang & Negnevitsky, Michael & Wang, Xiaolin, 2024. "Stochastic optimization of system configurations and operation of hybrid cascade hydro-wind-photovoltaic with battery for uncertain medium- and long-term load growth," Applied Energy, Elsevier, vol. 364(C).
    3. Koh, Rachel & Kern, Jordan & Galelli, Stefano, 2022. "Hard-coupling water and power system models increases the complementarity of renewable energy sources," Applied Energy, Elsevier, vol. 321(C).
    4. Zhang, Yi & Cheng, Chuntian & Cai, Huaxiang & Jin, Xiaoyu & Jia, Zebin & Wu, Xinyu & Su, Huaying & Yang, Tiantian, 2022. "Long-term stochastic model predictive control and efficiency assessment for hydro-wind-solar renewable energy supply system," Applied Energy, Elsevier, vol. 316(C).
    5. Li, He & Liu, Pan & Guo, Shenglian & Ming, Bo & Cheng, Lei & Yang, Zhikai, 2019. "Long-term complementary operation of a large-scale hydro-photovoltaic hybrid power plant using explicit stochastic optimization," Applied Energy, Elsevier, vol. 238(C), pages 863-875.
    6. Sujin Kim & Raghu Pasupathy & Shane G. Henderson, 2015. "A Guide to Sample Average Approximation," International Series in Operations Research & Management Science, in: Michael C Fu (ed.), Handbook of Simulation Optimization, edition 127, chapter 0, pages 207-243, Springer.
    7. Smriti Mallapaty, 2020. "How China could be carbon neutral by mid-century," Nature, Nature, vol. 586(7830), pages 482-483, October.
    8. Powell, Warren B., 2019. "A unified framework for stochastic optimization," European Journal of Operational Research, Elsevier, vol. 275(3), pages 795-821.
    9. Zhang, Yi & Cheng, Chuntian & Yang, Tiantian & Jin, Xiaoyu & Jia, Zebin & Shen, Jianjian & Wu, Xinyu, 2022. "Assessment of climate change impacts on the hydro-wind-solar energy supply system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    10. Wang, Huan & Liao, Shengli & Liu, Benxi & Zhao, Hongye & Ma, Xiangyu & Zhou, Binbin, 2024. "Long-term complementary scheduling model of hydro-wind-solar under extreme drought weather conditions using an improved time-varying hedging rule," Energy, Elsevier, vol. 305(C).
    11. Shapiro, Alexander, 2011. "Analysis of stochastic dual dynamic programming method," European Journal of Operational Research, Elsevier, vol. 209(1), pages 63-72, February.
    12. Javed, Muhammad Shahzad & Jurasz, Jakub & McPherson, Madeleine & Dai, Yanjun & Ma, Tao, 2022. "Quantitative evaluation of renewable-energy-based remote microgrids: curtailment, load shifting, and reliability," Renewable and Sustainable Energy Reviews, Elsevier, vol. 164(C).
    13. Omar J. Guerra, 2021. "Beyond short-duration energy storage," Nature Energy, Nature, vol. 6(5), pages 460-461, May.
    14. Yijing Wang & Rong Wang & Katsumasa Tanaka & Philippe Ciais & Josep Penuelas & Yves Balkanski & Jordi Sardans & Didier Hauglustaine & Wang Liu & Xiaofan Xing & Jiarong Li & Siqing Xu & Yuankang Xiong , 2023. "Accelerating the energy transition towards photovoltaic and wind in China," Nature, Nature, vol. 619(7971), pages 761-767, July.
    15. Sebastian Sterl & Inne Vanderkelen & Celray James Chawanda & Daniel Russo & Robert J. Brecha & Ann Griensven & Nicole P. M. Lipzig & Wim Thiery, 2020. "Smart renewable electricity portfolios in West Africa," Nature Sustainability, Nature, vol. 3(9), pages 710-719, September.
    16. Xu, Bin & Zhu, Feilin & Zhong, Ping-an & Chen, Juan & Liu, Weifeng & Ma, Yufei & Guo, Le & Deng, Xiaoliang, 2019. "Identifying long-term effects of using hydropower to complement wind power uncertainty through stochastic programming," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Cheng, Qian & Liu, Pan & Feng, Maoyuan & Cheng, Lei & Ming, Bo & Xie, Kang & Yang, Zhikai & Zhang, Xiaojing & Zheng, Yalian & Ye, Hao, 2025. "Leveraging a deep learning model to improve mid- and long-term operations of hydro-wind-photovoltaic complementary systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 222(C).

    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. Jin, Xiaoyu & Liu, Benxi & Liao, Shengli & Cheng, Chuntian & Jurasz, Jakub & Zhang, Yi & Lu, Jia, 2023. "Exploring the transition role of cascade hydropower in 100% decarbonized energy systems," Energy, Elsevier, vol. 279(C).
    2. Zhao, Zhipeng & Deng, Zhihao & Jin, Xiaoyu & Jia, Zebin & Cao, Rui & Cheng, Chuntian, 2025. "Managing long-term operation of cascade hydropower plants under energy transition with physics-constrained long-short term memory networks," Applied Energy, Elsevier, vol. 393(C).
    3. Zhang, Zhendong & Dai, Huichao & Wang, Yongqiang, 2025. "Long-medium-short term nested operation model of hydro-wind-solar hybrid power system considering flood control, power generation, ecology and navigation," Energy, Elsevier, vol. 334(C).
    4. Yang, Zhikai & Liu, Pan & Xia, Qian & Li, He & Cheng, Qian & Cheng, Lei, 2024. "Operating rules for hydro-photovoltaic systems: A variance-based sensitivity analysis," Applied Energy, Elsevier, vol. 372(C).
    5. Cheng, Qian & Liu, Pan & Ming, Bo & Yang, Zhikai & Cheng, Lei & Liu, Zheyuan & Huang, Kangdi & Xu, Weifeng & Gong, Lanqiang, 2024. "Synchronizing short-, mid-, and long-term operations of hydro-wind-photovoltaic complementary systems," Energy, Elsevier, vol. 305(C).
    6. Feng, Zhong-kai & Wang, Xin & Niu, Wen-jing, 2025. "Complementary operation optimization of cascade hydropower reservoirs and photovoltaic energy using cooperation search algorithm and conditional generative adversarial networks," Energy, Elsevier, vol. 328(C).
    7. Cheng, Qian & Liu, Pan & Feng, Maoyuan & Cheng, Lei & Ming, Bo & Luo, Xinran & Liu, Weibo & Xu, Weifeng & Huang, Kangdi & Xia, Jun, 2023. "Complementary operation with wind and photovoltaic power induces the decrease in hydropower efficiency," Applied Energy, Elsevier, vol. 339(C).
    8. Cao, Yupu & Xu, Bo & Zhang, Chi & Li, Fang-Fang & Liu, Zhanwei, 2025. "Strategic site-level planning of VRE integration in hydro-wind-solar systems under uncertainty," Energy, Elsevier, vol. 328(C).
    9. Jiang, Jianhua & Ming, Bo & Liu, Pan & Huang, Qiang & Guo, Yi & Chang, Jianxia & Zhang, Wei, 2023. "Refining long-term operation of large hydro–photovoltaic–wind hybrid systems by nesting response functions," Renewable Energy, Elsevier, vol. 204(C), pages 359-371.
    10. Cheng, Qian & Liu, Pan & Xia, Jun & Ming, Bo & Cheng, Lei & Chen, Jie & Xie, Kang & Liu, Zheyuan & Li, Xiao, 2022. "Contribution of complementary operation in adapting to climate change impacts on a large-scale wind–solar–hydro system: A case study in the Yalong River Basin, China," Applied Energy, Elsevier, vol. 325(C).
    11. Cheng, Qian & Liu, Pan & Feng, Maoyuan & Cheng, Lei & Ming, Bo & Xie, Kang & Yang, Zhikai & Zhang, Xiaojing & Zheng, Yalian & Ye, Hao, 2025. "Leveraging a deep learning model to improve mid- and long-term operations of hydro-wind-photovoltaic complementary systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 222(C).
    12. Jin, Xiaoyu & Hu, Xiangyang & Cheng, Chuntian & Yan, Lingzhi & Cai, Shubing & Liu, Benxi, 2025. "Enhancing power peak shaving with cascade hydropower: A buffer for wind and solar power uncertainty by deep learning-based data-driven approach," Energy, Elsevier, vol. 335(C).
    13. 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).
    14. Lee, Jinkyu & Bae, Sanghyeon & Kim, Woo Chang & Lee, Yongjae, 2023. "Value function gradient learning for large-scale multistage stochastic programming problems," European Journal of Operational Research, Elsevier, vol. 308(1), pages 321-335.
    15. Irawan, Chandra Ade & Jones, Dylan & Hofman, Peter S. & Zhang, Lina, 2023. "Integrated strategic energy mix and energy generation planning with multiple sustainability criteria and hierarchical stakeholders," European Journal of Operational Research, Elsevier, vol. 308(2), pages 864-883.
    16. Li, He & Zhao, Yuxiang & Liu, Pan & Cheng, Lei & Ming, Bo & Yang, Zhikai, 2025. "Adaptive operation of large hydro-wind-solar integrated systems by aggregation-decomposition under inconsistent resource conditions," Renewable Energy, Elsevier, vol. 248(C).
    17. Jin, Xiaoyu & Liu, Benxi & Liao, Shengli & Cheng, Chuntian & Zhang, Yi & Zhao, Zhipeng & Lu, Jia, 2022. "Wasserstein metric-based two-stage distributionally robust optimization model for optimal daily peak shaving dispatch of cascade hydroplants under renewable energy uncertainties," Energy, Elsevier, vol. 260(C).
    18. Lv, Furong & Tang, Haiping, 2025. "Assessing the impact of climate change on the optimal solar–wind hybrid power generation potential in China: A focus on stability and complementarity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 212(C).
    19. Yang, Yuqi & Zhou, Jianzhong & Liu, Guangbiao & Mo, Li & Wang, Yongqiang & Jia, Benjun & He, Feifei, 2020. "Multi-plan formulation of hydropower generation considering uncertainty of wind power," Applied Energy, Elsevier, vol. 260(C).
    20. Cheng, Xiong & Wan, Shixing & Zhengfeng, Bao & Wang, Lei & Li, Wenwu & Li, Xianshan & Zhong, Hao, 2025. "Credible capacity gain identification method of peak-shaving scheduling of cascade hydro-wind-solar complementary system," Renewable Energy, Elsevier, vol. 248(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:appene:v:393:y:2025:i:c:s0306261925008025. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.