IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i17p6368-d1231629.html
   My bibliography  Save this article

Mid-Term Optimal Scheduling of Low-Head Cascaded Hydropower Stations Considering Inflow Unevenness

Author

Listed:
  • Shuo Huang

    (Institute of Hydropower and Hydroinformatics, Dalian University of Technology, Dalian 116024, China)

  • Xinyu Wu

    (Institute of Hydropower and Hydroinformatics, Dalian University of Technology, Dalian 116024, China)

  • Yiyang Wu

    (Institute of Hydropower and Hydroinformatics, Dalian University of Technology, Dalian 116024, China)

  • Zheng Zhang

    (China Yangtze Power Co., Ltd., Yichang 443000, China)

Abstract

China has a vast scale of hydropower, and the small hydropower stations account for a large proportion. In flood season, the excessive inflow keeps these stations at a high reservoir level, leading to a worse condition of hindered power output and a great error in the calculation of power generation. Therefore, this paper proposes a mid-term optimal scheduling model for low-head cascaded hydropower stations considering inflow unevenness, in which the power output is controlled by the expected power output curve and daily inflow–maximum power output curve. A case study of nine hydropower stations on the Guangxi power grid shows that, regardless of considering the fitted curve or not, there are different degrees of error between the planned and actual situations. However, the error and power generation are decreased when considering the fitted curve, which reflects the impact of hindered power output. Meanwhile, according to the comparison, the weekly plan is more in line with the real condition when using this model to solve the problem. The results indicate that this model improves the accuracy of power output calculation for low-head hydropower stations with uneven inflow, playing a key role in the process of scheduling.

Suggested Citation

  • Shuo Huang & Xinyu Wu & Yiyang Wu & Zheng Zhang, 2023. "Mid-Term Optimal Scheduling of Low-Head Cascaded Hydropower Stations Considering Inflow Unevenness," Energies, MDPI, vol. 16(17), pages 1-13, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:17:p:6368-:d:1231629
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/17/6368/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/17/6368/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ramesh Teegavarapu & Slobodan Simonovic, 2002. "Optimal Operation of Reservoir Systems using Simulated Annealing," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 16(5), pages 401-428, October.
    2. Feng, Suzhen & Zheng, Hao & Qiao, Yifan & Yang, Zetai & Wang, Jinwen & Liu, Shuangquan, 2022. "Weekly hydropower scheduling of cascaded reservoirs with hourly power and capacity balances," Applied Energy, Elsevier, vol. 311(C).
    3. Bartosz Ceran & Jakub Jurasz & Robert Wróblewski & Adam Guderski & Daria Złotecka & Łukasz Kaźmierczak, 2020. "Impact of the Minimum Head on Low-Head Hydropower Plants Energy Production and Profitability," Energies, MDPI, vol. 13(24), pages 1-21, December.
    4. Schäffer, Linn Emelie & Helseth, Arild & Korpås, Magnus, 2022. "A stochastic dynamic programming model for hydropower scheduling with state-dependent maximum discharge constraints," Renewable Energy, Elsevier, vol. 194(C), pages 571-581.
    5. Fredo, Guilherme Luiz Minetto & Finardi, Erlon Cristian & de Matos, Vitor Luiz, 2019. "Assessing solution quality and computational performance in the long-term generation scheduling problem considering different hydro production function approaches," Renewable Energy, Elsevier, vol. 131(C), pages 45-54.
    6. Feng, Zhong-kai & Niu, Wen-jing & Cheng, Chun-tian & Wu, Xin-yu, 2017. "Optimization of hydropower system operation by uniform dynamic programming for dimensionality reduction," Energy, Elsevier, vol. 134(C), pages 718-730.
    7. Rashid, Muhammad Usman & Abid, Irfan & Latif, Abid, 2022. "Optimization of hydropower and related benefits through Cascade Reservoirs for sustainable economic growth," Renewable Energy, Elsevier, vol. 185(C), pages 241-254.
    8. He, Zhongzheng & Wang, Chao & Wang, Yongqiang & Wei, Bowen & Zhou, Jianzhong & Zhang, Hairong & Qin, Hui, 2021. "Dynamic programming with successive approximation and relaxation strategy for long-term joint power generation scheduling of large-scale hydropower station group," Energy, Elsevier, vol. 222(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. Fang, Zhou & Liao, Shengli & Cheng, Chuntian & Zhao, Hongye & Liu, Benxi & Su, Huaying, 2023. "Parallel improved DPSA algorithm for medium-term optimal scheduling of large-scale cascade hydropower plants," Renewable Energy, Elsevier, vol. 210(C), pages 134-147.
    2. Yin, Linfei & Luo, Shikui & Ma, Chenxiao, 2021. "Expandable depth and width adaptive dynamic programming for economic smart generation control of smart grids," Energy, Elsevier, vol. 232(C).
    3. Wu, Xinyu & Wu, Yiyang & Cheng, Xilong & Cheng, Chuntian & Li, Zehong & Wu, Yongqi, 2023. "A mixed-integer linear programming model for hydro unit commitment considering operation constraint priorities," Renewable Energy, Elsevier, vol. 204(C), pages 507-520.
    4. Liao, Shengli & Yang, Hualong & Liu, Benxi & Zhao, Hongye & Liu, Huan & Ma, Xiangyu & Wu, Huijun, 2022. "Daily peak-shaving model of cascade hydropower serving multi-grids considering an HVDC channel shared constraint," Renewable Energy, Elsevier, vol. 199(C), pages 112-122.
    5. Chuanxiong Kang & Cheng Chen & Jinwen Wang, 2018. "An Efficient Linearization Method for Long-Term Operation of Cascaded Hydropower Reservoirs," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(10), pages 3391-3404, August.
    6. Rongqi Zhang & Shanghong Zhang & Xiaoxiong Wen & Zhu Jing, 2023. "Refined Scheduling Based on Dynamic Capacity Model for Short-term Hydropower Generation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 21-35, January.
    7. K. Ramakrishnan & C. Suribabu & T. Neelakantan, 2010. "Crop Calendar Adjustment Study for Sathanur Irrigation System in India Using Genetic Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(14), pages 3835-3851, November.
    8. Iasonas Kouveliotis-Lysikatos & Angelica Waernlund & Manuel Marin & Mikael Amelin & Lennart Söder, 2021. "Open Source Modelling and Simulation of the Nordic Hydro Power System," Energies, MDPI, vol. 14(5), pages 1-17, March.
    9. Jakub Jurasz & Jerzy Mikulik & Paweł B. Dąbek & Mohammed Guezgouz & Bartosz Kaźmierczak, 2021. "Complementarity and ‘Resource Droughts’ of Solar and Wind Energy in Poland: An ERA5-Based Analysis," Energies, MDPI, vol. 14(4), pages 1-24, February.
    10. Dimitrios Karpouzos & Konstantinos Katsifarakis, 2013. "A Set of New Benchmark Optimization Problems for Water Resources Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(9), pages 3333-3348, July.
    11. Mohammad Azizipour & Vahid Ghalenoei & M. H. Afshar & S. S. Solis, 2016. "Optimal Operation of Hydropower Reservoir Systems Using Weed Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(11), pages 3995-4009, September.
    12. Ming, Bo & Liu, Pan & Guo, Shenglian & Cheng, Lei & Zhou, Yanlai & Gao, Shida & Li, He, 2018. "Robust hydroelectric unit commitment considering integration of large-scale photovoltaic power: A case study in China," Applied Energy, Elsevier, vol. 228(C), pages 1341-1352.
    13. Li, Huanhuan & Chen, Diyi & Arzaghi, Ehsan & Abbassi, Rouzbeh & Xu, Beibei & Patelli, Edoardo & Tolo, Silvia, 2018. "Safety assessment of hydro-generating units using experiments and grey-entropy correlation analysis," Energy, Elsevier, vol. 165(PA), pages 222-234.
    14. Zhao, Zhigao & Yang, Jiandong & Chung, C.Y. & Yang, Weijia & He, Xianghui & Chen, Man, 2021. "Performance enhancement of pumped storage units for system frequency support based on a novel small signal model," Energy, Elsevier, vol. 234(C).
    15. Pedro H. M. Nascimento & Vinícius A. Cabral & Ivo C. Silva Junior & Frederico F. Panoeiro & Leonardo M. Honório & André L. M. Marcato, 2021. "Spillage Forecast Models in Hydroelectric Power Plants Using Information from Telemetry Stations and Hydraulic Control," Energies, MDPI, vol. 14(1), pages 1-16, January.
    16. Shinuk Kang & Sangho Lee & Taeuk Kang, 2017. "Development and Application of Storage-Zone Decision Method for Long-Term Reservoir Operation Using the Dynamically Dimensioned Search Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 219-232, January.
    17. Periçaro, Gislaine A. & Karas, Elizabeth W. & Gonzaga, Clóvis C. & Marcílio, Débora C. & Oening, Ana Paula & Matioli, Luiz Carlos & Detzel, Daniel H.M. & de Geus, Klaus & Bessa, Marcelo R., 2020. "Optimal non-anticipative scenarios for nonlinear hydro-thermal power systems," Applied Mathematics and Computation, Elsevier, vol. 387(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. Zhongzheng He & Chao Wang & Yongqiang Wang & Hairong Zhang & Heng Yin, 2022. "An Efficient Optimization Method for Long-term Power Generation Scheduling of Hydropower Station: Improved Dynamic Programming with a Relaxation Strategy," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1481-1497, March.
    20. Feng, Zhong-kai & Niu, Wen-jing & Cheng, Chun-tian & Zhou, Jian-zhong, 2017. "Peak shaving operation of hydro-thermal-nuclear plants serving multiple power grids by linear programming," Energy, Elsevier, vol. 135(C), pages 210-219.

    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:16:y:2023:i:17:p:6368-:d:1231629. 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.