IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i5p1234-d1604471.html
   My bibliography  Save this article

Gas−Hydro Coordinated Peaking Considering Source-Load Uncertainty and Deep Peaking

Author

Listed:
  • Chong Wu

    (Energy Planning and Research Institute, Southwest Electric Power Design Institute Co., Ltd., China Power Engineering Consulting Group, Chengdu 610500, China)

  • Tong Xu

    (Energy Planning and Research Institute, Southwest Electric Power Design Institute Co., Ltd., China Power Engineering Consulting Group, Chengdu 610500, China)

  • Shenhao Yang

    (Energy Planning and Research Institute, Southwest Electric Power Design Institute Co., Ltd., China Power Engineering Consulting Group, Chengdu 610500, China)

  • Yong Zheng

    (Energy Planning and Research Institute, Southwest Electric Power Design Institute Co., Ltd., China Power Engineering Consulting Group, Chengdu 610500, China)

  • Xiaobin Yan

    (Energy Planning and Research Institute, Southwest Electric Power Design Institute Co., Ltd., China Power Engineering Consulting Group, Chengdu 610500, China)

  • Maoyu Mao

    (School of Electrical and Information, Southwest Petroleum University, Chengdu 610500, China)

  • Ziyi Jiang

    (School of Electrical and Information, Southwest Petroleum University, Chengdu 610500, China)

  • Qian Li

    (School of Electrical and Information, Southwest Petroleum University, Chengdu 610500, China)

Abstract

Considering the power demand in high-altitude special environmental areas and the peak-regulation issues in the power system caused by the uncertainties associated with wind and photovoltaic power as well as load, a gas–hydro coordinated peak-shaving method that considers source-load uncertainty is proposed. Firstly, based on the regulation-related characteristics of hydropower and gas power, a gas−hydro coordinated operation mode is proposed. Secondly, the system operational risk caused by source-load uncertainty is quantified based on the Conditional Value-at-Risk theory. Then, the cost of deep peak shaving in connection with gas-fired power generation is estimated, and a gas−hydro coordinated peak-shaving model considering risk constraints and deep peak shaving is established. Finally, a specific example verifies that the proposed gas−hydro coordinated peak-regulation model can effectively improve the economy of the system. The total system profit increased by 36.03%, indicating that this method enhances the total system profit and achieves better peak-shaving effects.

Suggested Citation

  • Chong Wu & Tong Xu & Shenhao Yang & Yong Zheng & Xiaobin Yan & Maoyu Mao & Ziyi Jiang & Qian Li, 2025. "Gas−Hydro Coordinated Peaking Considering Source-Load Uncertainty and Deep Peaking," Energies, MDPI, vol. 18(5), pages 1-23, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1234-:d:1604471
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/5/1234/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/5/1234/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Huang, Kangdi & Liu, Pan & Ming, Bo & Kim, Jong-Suk & Gong, Yu, 2021. "Economic operation of a wind-solar-hydro complementary system considering risks of output shortage, power curtailment and spilled water," Applied Energy, Elsevier, vol. 290(C).
    2. Fan, Gang & Lu, Xiaochen & Chen, Kang & Zhang, Yicen & Han, Zihao & Yu, Haibin & Dai, Yiping, 2022. "Comparative analysis on design and off-design performance of novel cascade CO2 combined cycles for gas turbine waste heat utilization," Energy, Elsevier, vol. 254(PA).
    3. Ye, Lin & Dai, Binhua & Li, Zhuo & Pei, Ming & Zhao, Yongning & Lu, Peng, 2022. "An ensemble method for short-term wind power prediction considering error correction strategy," Applied Energy, Elsevier, vol. 322(C).
    4. Tianliang Wang & Xin Jiang & Yang Jin & Dawei Song & Meng Yang & Qingshan Zeng, 2019. "Peaking Compensation Mechanism for Thermal Units and Virtual Peaking Plants Union Promoting Curtailed Wind Power Integration," Energies, MDPI, vol. 12(17), pages 1-20, August.
    5. Zhang, Junhao & Guo, Aijun & Wang, Yimin & Chang, Jianxia & Wang, Xuebin & Wang, Zhen & Tian, Yuyu & Jing, Zhiqiang & Peng, Zhiwen, 2024. "How to achieve optimal photovoltaic plant capacity in hydro-photovoltaic complementary systems: Fully coupling long-term and short-term operational modes of cascade hydropower plants," Energy, Elsevier, vol. 313(C).
    6. Jin, Xiaoyu & Liu, Benxi & Liao, Shengli & Cheng, Chuntian & Yan, Zhiyu, 2022. "A Wasserstein metric-based distributionally robust optimization approach for reliable-economic equilibrium operation of hydro-wind-solar energy systems," Renewable Energy, Elsevier, vol. 196(C), pages 204-219.
    7. 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).
    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. Zhao, Hongye & Liao, Shengli & Ma, Xiangyu & Fang, Zhou & Cheng, Chuntian & Zhang, Zheng, 2024. "Short-term peak-shaving scheduling of a hydropower-dominated hydro-wind-solar photovoltaic hybrid system considering a shared multienergy coupling transmission channel," Applied Energy, Elsevier, vol. 372(C).
    2. Liu, Benxi & Liu, Tengyuan & Liao, Shengli & Lu, Jia & Cheng, Chuntian, 2023. "Short-term coordinated hybrid hydro-wind-solar optimal scheduling model considering multistage section restrictions," Renewable Energy, Elsevier, vol. 217(C).
    3. 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).
    4. Duan, Jiandong & Gao, Qi & Xia, Yerui & Tian, Qinxing & Qin, Bo, 2024. "MMD-DRO based economic dispatching considering flexible reserve provision from concentrated solar power plant," Energy, Elsevier, vol. 308(C).
    5. Jie Hao & Xiuting Guo & Yan Li & Tao Wu, 2024. "Uncertain Scheduling of the Power System Based on Wasserstein Distributionally Robust Optimization and Improved Differential Evolution Algorithm," Energies, MDPI, vol. 17(15), pages 1-21, August.
    6. Liu, Mao & Kong, Xiangyu & Ma, Chao & Zhou, Xuesong & Lin, Qingxiang, 2024. "Multi-stage fully adaptive distributionally robust unit commitment for power system based on mixed approximation rules," Applied Energy, Elsevier, vol. 376(PA).
    7. Liu, Mao & Kong, Xiangyu & Lian, Jijian & Wang, Jimin & Yang, Bohan, 2025. "Distributionally robust coordinated day-ahead scheduling of Cascade pumped hydro energy storage system and DC transmission," Applied Energy, Elsevier, vol. 384(C).
    8. Zhou, Siyu & Han, Yang & Zalhaf, Amr S. & Chen, Shuheng & Zhou, Te & Yang, Ping & Elboshy, Bahaa, 2023. "A novel multi-objective scheduling model for grid-connected hydro-wind-PV-battery complementary system under extreme weather: A case study of Sichuan, China," Renewable Energy, Elsevier, vol. 212(C), pages 818-833.
    9. 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).
    10. Son, Yeong Geon & Kim, Sung Yul, 2025. "Distributionally robust planning for power-to- gas integrated large wind farm systems incorporating hydrogen production switch control model," Energy, Elsevier, vol. 314(C).
    11. Boudy Bilal & Kaan Yetilmezsoy & Mohammed Ouassaid, 2024. "Benchmarking of Various Flexible Soft-Computing Strategies for the Accurate Estimation of Wind Turbine Output Power," Energies, MDPI, vol. 17(3), pages 1-36, February.
    12. Wang, Xiaodi & Hao, Yan & Yang, Wendong, 2024. "Novel wind power ensemble forecasting system based on mixed-frequency modeling and interpretable base model selection strategy," Energy, Elsevier, vol. 297(C).
    13. Wang, Jin & Zhao, Zhipeng & Zhou, Jinglin & Cheng, Chuntian & Su, Huaying, 2024. "Developing operating rules for a hydro–wind–solar hybrid system considering peak-shaving demands," Applied Energy, Elsevier, vol. 360(C).
    14. Cheng, Qian & Liu, Pan & Xia, Qian & Cheng, Lei & Ming, Bo & Zhang, Wei & Xu, Weifeng & Zheng, Yalian & Han, Dongyang & Xia, Jun, 2023. "An analytical method to evaluate curtailment of hydro–photovoltaic hybrid energy systems and its implication under climate change," Energy, Elsevier, vol. 278(C).
    15. Zhang, Liu & Zhang, Kaitian & Zheng, Zhong & Chai, Yi & Lian, Xiaoyuan & Zhang, Kai & Xu, Zhaojun & Chen, Sujun, 2023. "Two-stage distributionally robust integrated scheduling of oxygen distribution and steelmaking-continuous casting in steel enterprises," Applied Energy, Elsevier, vol. 351(C).
    16. Zhang, Yusheng & Ma, Chao & Yang, Yang & Pang, Xiulan & Lian, Jijian & Wang, Xin, 2022. "Capacity configuration and economic evaluation of a power system integrating hydropower, solar, and wind," Energy, Elsevier, vol. 259(C).
    17. 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).
    18. Ying-Yi Hong & Gerard Francesco DG. Apolinario, 2021. "Uncertainty in Unit Commitment in Power Systems: A Review of Models, Methods, and Applications," Energies, MDPI, vol. 14(20), pages 1-47, October.
    19. 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).
    20. Lv, Yunlong & Hu, Qin & Xu, Hang & Lin, Huiyao & Wu, Yufan, 2024. "An ultra-short-term wind power prediction method based on spatial-temporal attention graph convolutional model," Energy, Elsevier, vol. 293(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:gam:jeners:v:18:y:2025:i:5:p:1234-:d:1604471. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.