IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v356y2024ics0306261923017439.html
   My bibliography  Save this article

A novel day-ahead scheduling model to unlock hydropower flexibility limited by vibration zones in hydropower-variable renewable energy hybrid system

Author

Listed:
  • Han, Shuo
  • Yuan, Yifan
  • He, Mengjiao
  • Zhao, Ziwen
  • Xu, Beibei
  • Chen, Diyi
  • Jurasz, Jakub

Abstract

Giving full play to the flexibility of hydropower and integrating more variable renewable energy (VRE) is of great significance for accelerating the transformation of China's power energy system. For middle- to large-sized hydropower units, its irregular vibration zone (VZ) is a major factor affecting its flexibility, because VZs limits the power output and adjustment range. In the existing studies on hydropower unit commitment and flexibility scheduling, VZs only limits the output of hydropower units, while the impact on the flexibility adjustment ability of units is ignored. The hydropower flexibility in day-ahead scheme is overestimated, which increases the risk of system flexibility shortage causing power curtailment and load loss. In this study, a novel day-ahead scheduling model considering the flexibility limited by the VZs and the probability of flexibility shortage is constructed for the day-ahead generation scheme of hydropower-VRE hybrid generation system (HVHGS) to optimize the target load, VRE output and hydropower unit commitments. The impaction of the VZs on hydropower flexibility is firstly modeled by chance constraints and stochastic programming method. Moreover, a data-driven model based on machine learning and an efficient solving approach based on successive linear programming is carry out to describe the uncertainty of VRE output more realistically and ensure the timeliness of optimization scheme, respectively. The proposed model is applied to a real hydropower station in the Hongshui River Basin in China. In 16 representative scenarios, the proposed model can complete the optimization in an acceptable time, with a maximum of 444.57 s. Compared with the traditional interval optimization model, the proposed model effectively improves the flexibility supply capacity of hydropower in the day-ahead scheme. The maximum reduction value of flexibility shortage probability and expectation reach 98.77% and 442.66 MW, respectively. In particular, the flexibility of the model is most obvious under heavy load demand in flood season, and it is almost not at the cost of daily target load adjustment, which provides practical reference for decision makers.

Suggested Citation

  • Han, Shuo & Yuan, Yifan & He, Mengjiao & Zhao, Ziwen & Xu, Beibei & Chen, Diyi & Jurasz, Jakub, 2024. "A novel day-ahead scheduling model to unlock hydropower flexibility limited by vibration zones in hydropower-variable renewable energy hybrid system," Applied Energy, Elsevier, vol. 356(C).
  • Handle: RePEc:eee:appene:v:356:y:2024:i:c:s0306261923017439
    DOI: 10.1016/j.apenergy.2023.122379
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923017439
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2023.122379?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. Yuan, Ran & Wang, Bo & Mao, Zhixin & Watada, Junzo, 2021. "Multi-objective wind power scenario forecasting based on PG-GAN," Energy, Elsevier, vol. 226(C).
    2. Wang, Zhenni & Wen, Xin & Tan, Qiaofeng & Fang, Guohua & Lei, Xiaohui & Wang, Hao & Yan, Jinyue, 2021. "Potential assessment of large-scale hydro-photovoltaic-wind hybrid systems on a global scale," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    3. Cheng, Xianliang & Feng, Suzhen & Zheng, Hao & Wang, Jinwen & Liu, Shuangquan, 2022. "A hierarchical model in short-term hydro scheduling with unit commitment and head-dependency," Energy, Elsevier, vol. 251(C).
    4. Wang, Zizhao & Wu, Feng & Li, Yang & Li, Jingyan & Liu, Ying & Liu, Wenge, 2023. "Day-ahead dispatch approach for cascaded hydropower-photovoltaic complementary system based on two-stage robust optimization," Energy, Elsevier, vol. 265(C).
    5. 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).
    6. Zhang, Juntao & Cheng, Chuntian & Yu, Shen & Wu, Huijun & Gao, Mengping, 2021. "Sharing hydropower flexibility in interconnected power systems: A case study for the China Southern power grid," Applied Energy, Elsevier, vol. 288(C).
    7. Zhao, Mingzhe & Wang, Yimin & Wang, Xuebin & Chang, Jianxia & Chen, Yunhua & Zhou, Yong & Guo, Aijun, 2022. "Flexibility evaluation of wind-PV-hydro multi-energy complementary base considering the compensation ability of cascade hydropower stations," Applied Energy, Elsevier, vol. 315(C).
    8. 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).
    9. 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).
    10. Tan, Qiaofeng & Wen, Xin & Sun, Yuanliang & Lei, Xiaohui & Wang, Zhenni & Qin, Guanghua, 2021. "Evaluation of the risk and benefit of the complementary operation of the large wind-photovoltaic-hydropower system considering forecast uncertainty," Applied Energy, Elsevier, vol. 285(C).
    11. Cheng, Chuntian & Su, Chengguo & Wang, Peilin & Shen, Jianjian & Lu, Jianyu & Wu, Xinyu, 2018. "An MILP-based model for short-term peak shaving operation of pumped-storage hydropower plants serving multiple power grids," Energy, Elsevier, vol. 163(C), pages 722-733.
    12. Xiong, Hualin & Egusquiza, Mònica & Alberg Østergaard, Poul & Pérez-Díaz, Juan I. & Sun, Guoxiu & Egusquiza, Eduard & Patelli, Edoardo & Xu, Beibei & Duan, Hongjiang & Chen, Diyi & Luo, Xingqi, 2021. "Multi-objective optimization of a hydro-wind-photovoltaic power complementary plant with a vibration avoidance strategy," Applied Energy, Elsevier, vol. 301(C).
    13. Ming, Bo & Liu, Pan & Guo, Shenglian & Zhang, Xiaoqi & Feng, Maoyuan & Wang, Xianxun, 2017. "Optimizing utility-scale photovoltaic power generation for integration into a hydropower reservoir by incorporating long- and short-term operational decisions," Applied Energy, Elsevier, vol. 204(C), pages 432-445.
    14. Feng, Zhong-kai & Niu, Wen-jing & Wang, Sen & Cheng, Chun-tian & Jiang, Zhi-qiang & Qin, Hui & Liu, Yi, 2018. "Developing a successive linear programming model for head-sensitive hydropower system operation considering power shortage aspect," Energy, Elsevier, vol. 155(C), pages 252-261.
    15. Yuan, Wenlin & Wang, Xinqi & Su, Chengguo & Cheng, Chuntian & Liu, Zhe & Wu, Zening, 2021. "Stochastic optimization model for the short-term joint operation of photovoltaic power and hydropower plants based on chance-constrained programming," Energy, Elsevier, vol. 222(C).
    16. Zhang, Juntao & Cheng, Chuntian & Yu, Shen & Su, Huaying, 2022. "Chance-constrained co-optimization for day-ahead generation and reserve scheduling of cascade hydropower–variable renewable energy hybrid systems," Applied Energy, Elsevier, vol. 324(C).
    17. Jia, Rui & He, Mengjiao & Zhang, Xinyu & Zhao, Ziwen & Han, Shuo & Jurasz, Jakub & Chen, Diyi & Xu, Beibei, 2022. "Optimal operation of cascade hydro-wind-photovoltaic complementary generation system with vibration avoidance strategy," Applied Energy, Elsevier, vol. 324(C).
    18. Camal, S. & Teng, F. & Michiorri, A. & Kariniotakis, G. & Badesa, L., 2019. "Scenario generation of aggregated Wind, Photovoltaics and small Hydro production for power systems applications," Applied Energy, Elsevier, vol. 242(C), pages 1396-1406.
    19. Erick Delage & Yinyu Ye, 2010. "Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems," Operations Research, INFORMS, vol. 58(3), pages 595-612, June.
    20. Hu, Jinxing & Li, Hongru, 2022. "A transfer learning-based scenario generation method for stochastic optimal scheduling of microgrid with newly-built wind farm," Renewable Energy, Elsevier, vol. 185(C), pages 1139-1151.
    21. Zhou, Yuzhou & Zhao, Jiexing & Zhai, Qiaozhu, 2021. "100% renewable energy: A multi-stage robust scheduling approach for cascade hydropower system with wind and photovoltaic power," Applied Energy, Elsevier, vol. 301(C).
    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. Hong Pan & Zhengliang Luo & Chenyang Hang & Yuan Zheng & Fang Feng & Xiaonan Zheng, 2024. "Optimization of Load Distribution Method for Hydropower Units Based on Output Fluctuation Constraint and Double-Layer Nested Model," Mathematics, MDPI, vol. 12(5), pages 1-17, February.
    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. Jiehong Kong & Igor Iliev & Hans Ivar Skjelbred, 2024. "Including Lifetime Hydraulic Turbine Cost into Short-Term Hybrid Scheduling of Hydro and Solar," Energies, MDPI, vol. 17(21), pages 1-17, October.
    4. Bian, Yifan & Xie, Lirong & Ye, Jiahao & Ma, Lan, 2024. "A new shared energy storage business model for data center clusters considering energy storage degradation," Renewable Energy, Elsevier, vol. 225(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. 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).
    2. 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).
    3. 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.
    4. Feng, Zhong-kai & Huang, Qing-qing & Niu, Wen-jing & Su, Hua-ying & Li, Shu-shan & Wu, Hui-jun & Wang, Jia-yang, 2024. "Peak operation optimization of cascade hydropower reservoirs and solar power plants considering output forecasting uncertainty," Applied Energy, Elsevier, vol. 358(C).
    5. Jing, Zhiqiang & Wang, Yimin & Chang, Jianxia & Wang, Xuebin & Zhou, Yong & Li, Liang & Tian, Yuyu, 2024. "Benefit compensation of hydropower-wind-photovoltaic complementary operation in the large clean energy base," Applied Energy, Elsevier, vol. 354(PA).
    6. Lu, Na & Wang, Guangyan & Su, Chengguo & Ren, Zaimin & Peng, Xiaoyue & Sui, Quan, 2024. "Medium- and long-term interval optimal scheduling of cascade hydropower-photovoltaic complementary systems considering multiple uncertainties," Applied Energy, Elsevier, vol. 353(PA).
    7. Li, Yan & Ming, Bo & Huang, Qiang & Wang, Yimin & Liu, Pan & Guo, Pengcheng, 2022. "Identifying effective operating rules for large hydro–solar–wind hybrid systems based on an implicit stochastic optimization framework," Energy, Elsevier, vol. 245(C).
    8. Zhang, Juntao & Cheng, Chuntian & Yu, Shen & Su, Huaying, 2022. "Chance-constrained co-optimization for day-ahead generation and reserve scheduling of cascade hydropower–variable renewable energy hybrid systems," Applied Energy, Elsevier, vol. 324(C).
    9. Ma, Chao & Xu, Ximeng & Pang, Xiulan & Li, Xiaofeng & Zhang, Pengfei & Liu, Lu, 2024. "Scenario-based ultra-short-term rolling optimal operation of a photovoltaic-energy storage system under forecast uncertainty," Applied Energy, Elsevier, vol. 356(C).
    10. Zhang, Yusheng & Zhao, Xuehua & Wang, Xin & Li, Aiyun & Wu, Xinhao, 2023. "Multi-objective optimization design of a grid-connected hybrid hydro-photovoltaic system considering power transmission capacity," Energy, Elsevier, vol. 284(C).
    11. 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.
    12. 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).
    13. 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).
    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. 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).
    16. Zhang, Yusheng & Ma, Chao & Yang, Yang & Pang, Xiulan & Liu, Lu & Lian, Jijian, 2021. "Study on short-term optimal operation of cascade hydro-photovoltaic hybrid systems," Applied Energy, Elsevier, vol. 291(C).
    17. Wang, Zizhao & Li, Yang & Wu, Feng & Wu, Jiawei & Shi, Linjun & Lin, Keman, 2024. "Multi-objective day-ahead scheduling of cascade hydropower-photovoltaic complementary system with pumping installation," Energy, Elsevier, vol. 290(C).
    18. 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.
    19. Lei, Kaixuan & Chang, Jianxia & Wang, Xuebin & Guo, Aijun & Wang, Yimin & Ren, Chengqing, 2023. "Peak shaving and short-term economic operation of hydro-wind-PV hybrid system considering the uncertainty of wind and PV power," Renewable Energy, Elsevier, vol. 215(C).
    20. Yang, Zhikai & Liu, Pan & Cheng, Lei & Liu, Deli & Ming, Bo & Li, He & Xia, Qian, 2021. "Sizing utility-scale photovoltaic power generation for integration into a hydropower plant considering the effects of climate change: A case study in the Longyangxia of China," Energy, Elsevier, vol. 236(C).

    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:356:y:2024:i:c:s0306261923017439. 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.