IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v298y2024ics0360544224009915.html
   My bibliography  Save this article

Value of long-term inflow forecast for hydropower operation: A case study in a low forecast precision region

Author

Listed:
  • Shu, Xingsheng
  • Ding, Wei
  • Peng, Yong
  • Wang, Ziru

Abstract

Many studies have evaluated the value of long-term inflow forecast for hydropower operation in high-precision watersheds and explored the impact of forecast uncertainty on hydropower benefits. However, few of them focuses on the low-accuracy regions and the specific impact of forecast errors on hydropower benefits. Taking Hunjiang cascaded system in China as an example, this study investigated the value of low accuracy long-term inflow forecast for hydropower operation, revealed the influence mechanism of forecast errors on hydropower benefits, and determined the critical threshold for accuracy of beneficial forecast. Results show that low accuracy forecast but higher than critical threshold can increase hydropower generation. Hunjiang has a low accuracy long-term forecast with qualified rate being 30–40 %, but annual hydropower generation can be increased by 14.86–29.58 million kWh. The influence mechanism of forecast errors on hydropower operation presents three typical scenarios, two of which are harmless. One is that the error tolerance of operation rules can avoid some decision-making errors; the other one is that when current misreporting is consistent with the inflow in a longer lead time, the misreporting may instead be beneficial. We conclude that low accuracy long-term forecast is valuable but higher accuracy brings higher benefits.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:298:y:2024:i:c:s0360544224009915
    DOI: 10.1016/j.energy.2024.131218
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224009915
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.131218?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. Xingsheng Shu & Wei Ding & Yong Peng & Ziru Wang & Jian Wu & Min Li, 2021. "Monthly Streamflow Forecasting Using Convolutional Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5089-5104, December.
    2. Yuxin Zhu & Jianzhong Zhou & Yongchuan Zhang & Zhiqiang Jiang & Benjun Jia & Wei Fang, 2022. "Optimal Energy Storage Operation Chart and Output Distribution of Cascade Reservoirs Based on Operating Rules Derivation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(14), pages 5751-5766, November.
    3. Kai Lun Chong & Sai Hin Lai & Yu Yao & Ali Najah Ahmed & Wan Zurina Wan Jaafar & Ahmed El-Shafie, 2020. "Performance Enhancement Model for Rainfall Forecasting Utilizing Integrated Wavelet-Convolutional Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(8), pages 2371-2387, June.
    4. 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).
    5. Xiaoli Zhang & Yong Peng & Wei Xu & Bende Wang, 2019. "An Optimal Operation Model for Hydropower Stations Considering Inflow Forecasts with Different Lead-Times," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(1), pages 173-188, January.
    6. Jiang, Zhiqiang & Li, Anqiang & Ji, Changming & Qin, Hui & Yu, Shan & Li, Yuanzheng, 2016. "Research and application of key technologies in drawing energy storage operation chart by discriminant coefficient method," Energy, Elsevier, vol. 114(C), pages 774-786.
    7. Qiao-feng Tan & Guo-hua Fang & Xin Wen & Xiao-hui Lei & Xu Wang & Chao Wang & Yi Ji, 2020. "Bayesian Stochastic Dynamic Programming for Hydropower Generation Operation Based on Copula Functions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(5), pages 1589-1607, March.
    8. Gupta, Akshita & Kumar, Arun & Khatod, Dheeraj Kumar, 2019. "Optimized scheduling of hydropower with increase in solar and wind installations," Energy, Elsevier, vol. 183(C), pages 716-732.
    9. M. Shourian & S. Mousavi & A. Tahershamsi, 2008. "Basin-wide Water Resources Planning by Integrating PSO Algorithm and MODSIM," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(10), pages 1347-1366, October.
    10. Tan, Qiao-feng & Lei, Xiao-hui & Wen, Xin & Fang, Guo-hua & Wang, Xu & Wang, Chao & Ji, Yi & Huang, Xian-feng, 2019. "Two-stage stochastic optimal operation model for hydropower station based on the approximate utility function of the carryover stage," Energy, Elsevier, vol. 183(C), pages 670-682.
    11. 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.
    12. Xiaoling Ding & Xiaocong Mo & Jianzhong Zhou & Sheng Bi & Benjun Jia & Xiang Liao, 2021. "Long-Term Scheduling of Cascade Reservoirs Considering Inflow Forecasting Uncertainty Based on a Disaggregation Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(2), pages 645-660, January.
    13. P. Mujumdar & B. Nirmala, 2007. "A Bayesian Stochastic Optimization Model for a Multi-Reservoir Hydropower System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(9), pages 1465-1485, September.
    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. Qiao-feng Tan & Guo-hua Fang & Xin Wen & Xiao-hui Lei & Xu Wang & Chao Wang & Yi Ji, 2020. "Bayesian Stochastic Dynamic Programming for Hydropower Generation Operation Based on Copula Functions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(5), pages 1589-1607, March.
    2. Niu, Wen-jing & Feng, Zhong-kai & Cheng, Chun-tian, 2018. "Optimization of variable-head hydropower system operation considering power shortage aspect with quadratic programming and successive approximation," Energy, Elsevier, vol. 143(C), pages 1020-1028.
    3. 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).
    4. Jin, Xiaoyu & Liu, Benxi & Liao, Shengli & Cheng, Chuntian & Li, Gang & Liu, Lingjun, 2022. "Impacts of different wind and solar power penetrations on cascade hydroplants operation," Renewable Energy, Elsevier, vol. 182(C), pages 227-244.
    5. Katerina Spanoudaki & Panayiotis Dimitriadis & Emmanouil A. Varouchakis & Gerald A. Corzo Perez, 2022. "Estimation of Hydropower Potential Using Bayesian and Stochastic Approaches for Streamflow Simulation and Accounting for the Intermediate Storage Retention," Energies, MDPI, vol. 15(4), pages 1-20, February.
    6. Feng, Zhong-kai & Niu, Wen-jing & Cheng, Chun-tian, 2018. "Optimal allocation of hydropower and hybrid electricity injected from inter-regional transmission lines among multiple receiving-end power grids in China," Energy, Elsevier, vol. 162(C), pages 444-452.
    7. Ding, Ziyu & Wen, Xin & Tan, Qiaofeng & Yang, Tiantian & Fang, Guohua & Lei, Xiaohui & Zhang, Yu & Wang, Hao, 2021. "A forecast-driven decision-making model for long-term operation of a hydro-wind-photovoltaic hybrid system," Applied Energy, Elsevier, vol. 291(C).
    8. Xiaoling Ding & Xiaocong Mo & Jianzhong Zhou & Sheng Bi & Benjun Jia & Xiang Liao, 2021. "Long-Term Scheduling of Cascade Reservoirs Considering Inflow Forecasting Uncertainty Based on a Disaggregation Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(2), pages 645-660, January.
    9. Chaoyang Chen & Hualing Liu & Yong Xiao & Fagen Zhu & Li Ding & Fuwen Yang, 2022. "Power Generation Scheduling for a Hydro-Wind-Solar Hybrid System: A Systematic Survey and Prospect," Energies, MDPI, vol. 15(22), pages 1-31, November.
    10. Feng, Zhong-kai & Niu, Wen-jing & Wang, Wen-chuan & Zhou, Jian-zhong & Cheng, Chun-tian, 2019. "A mixed integer linear programming model for unit commitment of thermal plants with peak shaving operation aspect in regional power grid lack of flexible hydropower energy," Energy, Elsevier, vol. 175(C), pages 618-629.
    11. Amir Hatamkhani & Mojtaba Shourian & Ali Moridi, 2021. "Optimal Design and Operation of a Hydropower Reservoir Plant Using a WEAP-Based Simulation–Optimization Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(5), pages 1637-1652, March.
    12. Xinyu Wu & Yuan Lei & Chuntian Cheng & Qilin Ying, 2023. "An Optimal Operation Method for Parallel Hydropower Systems Combining Reservoir Level Control and Power Distribution," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(4), pages 1729-1745, March.
    13. Crescenzo Pepe & Silvia Maria Zanoli, 2024. "Digitalization, Industry 4.0, Data, KPIs, Modelization and Forecast for Energy Production in Hydroelectric Power Plants: A Review," Energies, MDPI, vol. 17(4), pages 1-35, February.
    14. Ewa Chomać-Pierzecka & Andrzej Kokiel & Joanna Rogozińska-Mitrut & Anna Sobczak & Dariusz Soboń & Jacek Stasiak, 2022. "Hydropower in the Energy Market in Poland and the Baltic States in the Light of the Challenges of Sustainable Development-An Overview of the Current State and Development Potential," Energies, MDPI, vol. 15(19), pages 1-19, October.
    15. Sarmad Dashti Latif & Ali Najah Ahmed, 2023. "A review of deep learning and machine learning techniques for hydrological inflow forecasting," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(11), pages 12189-12216, November.
    16. Yi Liu & Zhiqiang Jiang & Zhongkai Feng & Yuyun Chen & Hairong Zhang & Ping Chen, 2019. "Optimization of Energy Storage Operation Chart of Cascade Reservoirs with Multi-Year Regulating Reservoir," Energies, MDPI, vol. 12(20), pages 1-20, October.
    17. Jing Bai & Jiahui Wang & Jin Ran & Xingyuan Li & Chuang Tu, 2024. "An Improved Neural Network Algorithm for Energy Consumption Forecasting," Sustainability, MDPI, vol. 16(21), pages 1-19, October.
    18. Wen, Lulu & Zhou, Kaile & Li, Jun & Wang, Shanyong, 2020. "Modified deep learning and reinforcement learning for an incentive-based demand response model," Energy, Elsevier, vol. 205(C).
    19. Lisicki, Michal & Lubitz, William & Taylor, Graham W., 2016. "Optimal design and operation of Archimedes screw turbines using Bayesian optimization," Applied Energy, Elsevier, vol. 183(C), pages 1404-1417.
    20. Li, Xiao & Liu, Pan & Cheng, Lei & Cheng, Qian & Zhang, Wei & Xu, Shitian & Zheng, Yalian, 2023. "Strategic bidding for a hydro-wind-photovoltaic hybrid system considering the profit beyond forecast time," Renewable Energy, Elsevier, vol. 204(C), pages 277-289.

    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:298:y:2024:i:c:s0360544224009915. 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.