IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v35y2021i2d10.1007_s11269-020-02748-5.html
   My bibliography  Save this article

Long-Term Scheduling of Cascade Reservoirs Considering Inflow Forecasting Uncertainty Based on a Disaggregation Model

Author

Listed:
  • Xiaoling Ding

    (Huazhong University of Science and Technology
    Changjiang River Scientific Research Institute of Changjiang Water Resource Commission)

  • Xiaocong Mo

    (Changjiang River Scientific Research Institute of Changjiang Water Resource Commission)

  • Jianzhong Zhou

    (Huazhong University of Science and Technology)

  • Sheng Bi

    (Changjiang River Scientific Research Institute of Changjiang Water Resource Commission)

  • Benjun Jia

    (Huazhong University of Science and Technology)

  • Xiang Liao

    (Hubei University of technology)

Abstract

Forecasted inflow is one of the most important input information for reservoir operation planning. However, current inflow prediction accuracy is difficult to meet the needs of long-term operation. Therefore, it is of great significance to consider the uncertainty of inflow forecast and study its influence on reservoir operation decisions. In this paper, a long-term scheduling framework considering inflow forecast uncertainty based on a temporal disaggregation method is proposed. First, the uncertainty of forecast is described from two aspects. Gaussian distribution is used to simulate the annual forecast error, and an Adaptive Nearest Neighbor Gaussian sampling method (A-NGS) is proposed to decompose annual inflow into temporal series. Based on the implicit scheduling model, the sample set of generation scheduling plans, actual dispatching schemes and theoretical optimal results are constructed. On this basis, a series of indexes are presented to evaluate the inflow simulation performance and the scheduling benefits. A case study of the Xiluodu-Xiangjiaba cascade reservoirs is conducted to analyze the effects of forecast uncertainty on operation benefits, and the effectiveness of forecast information is identified. Compared with the deterministic fragment method, the inflow processes simulated by A-NGS achieve better precision and behave more conducive to the scheduling. Although the uncertainty of forecast errors will bring some hydropower generation losses, a certain degree of forecast accuracy is effective to improve scheduling benefits when in the electricity market.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:waterr:v:35:y:2021:i:2:d:10.1007_s11269-020-02748-5
    DOI: 10.1007/s11269-020-02748-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-020-02748-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11269-020-02748-5?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. 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.
    2. 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.
    3. Alcigeimes Celeste & Max Billib, 2012. "Improving Implicit Stochastic Reservoir Optimization Models with Long-Term Mean Inflow Forecast," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(9), pages 2443-2451, July.
    4. Wei Xu & Xiaoli Zhang & Anbang Peng & Yue Liang, 2020. "Deep Reinforcement Learning for Cascaded Hydropower Reservoirs Considering Inflow Forecasts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 3003-3018, July.
    5. Hamideh Hosseini Safa & Saeed Morid & Mahnoush Moghaddasi, 2012. "Incorporating Economy and Long-term Inflow Forecasting Uncertainty into Decision-making for Agricultural Water Allocation during Droughts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(8), pages 2267-2281, June.
    6. V. Chandramouli & Paresh Deka, 2005. "Neural Network Based Decision Support Model for Optimal Reservoir Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 19(4), pages 447-464, August.
    7. Fernando Mainardi Fan & Dirk Schwanenberg & Rodolfo Alvarado & Alberto Assis dos Reis & Walter Collischonn & Steffi Naumman, 2016. "Performance of Deterministic and Probabilistic Hydrological Forecasts for the Short-Term Optimization of a Tropical Hydropower Reservoir," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(10), pages 3609-3625, August.
    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. 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).

    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. 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.
    2. Fi-John Chang & Yu-Chung Wang & Wen-Ping Tsai, 2016. "Modelling Intelligent Water Resources Allocation for Multi-users," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1395-1413, March.
    3. Lu, Di & Wang, Bende & Wang, Yaodong & Zhou, Huicheng & Liang, Qiuhua & Peng, Yong & Roskilly, Tony, 2015. "Optimal operation of cascade hydropower stations using hydrogen as storage medium," Applied Energy, Elsevier, vol. 137(C), pages 56-63.
    4. 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).
    5. Fi-John Chang & Yu-Chung Wang & Wen-Ping Tsai, 2016. "Modelling Intelligent Water Resources Allocation for Multi-users," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1395-1413, March.
    6. 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.
    7. Zhiqiang Jiang & Zhengyang Tang & Yi Liu & Yuyun Chen & Zhongkai Feng & Yang Xu & Hairong Zhang, 2019. "Area Moment and Error Based Forecasting Difficulty and its Application in Inflow Forecasting Level Evaluation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(13), pages 4553-4568, October.
    8. Chang-ming Ji & Ting Zhou & Hai-tao Huang, 2014. "Operating Rules Derivation of Jinsha Reservoirs System with Parameter Calibrated Support Vector Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(9), pages 2435-2451, July.
    9. Yizhong Chen & Li He & Hongwei Lu & Jing Li & Lixia Ren, 2018. "Planning for Regional Water System Sustainability Through Water Resources Security Assessment Under Uncertainties," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(9), pages 3135-3153, July.
    10. Yi-min Wang & Jian-xia Chang & Qiang Huang, 2010. "Simulation with RBF Neural Network Model for Reservoir Operation Rules," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(11), pages 2597-2610, September.
    11. 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.
    12. R. Roozbahani & S. Schreider & B. Abbasi, 2013. "Economic Sharing of Basin Water Resources between Competing Stakeholders," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 2965-2988, June.
    13. Jônatas Belotti & Hugo Siqueira & Lilian Araujo & Sérgio L. Stevan & Paulo S.G. de Mattos Neto & Manoel H. N. Marinho & João Fausto L. de Oliveira & Fábio Usberti & Marcos de Almeida Leone Filho & Att, 2020. "Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants," Energies, MDPI, vol. 13(18), pages 1-22, September.
    14. Yuri B. Kirsta & Ol’ga V. Lovtskaya, 2021. "Good-quality Long-term Forecast of Spring-summer Flood Runoff for Mountain Rivers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 811-825, February.
    15. Pao-Shan Yu & Tao-Chang Yang & Chen-Min Kuo & Yi-Tai Wang, 2014. "A Stochastic Approach for Seasonal Water-Shortage Probability Forecasting Based on Seasonal Weather Outlook," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 3905-3920, September.
    16. Jenq-Tzong Shiau & Hui-Ting Hsu, 2016. "Suitability of ANN-Based Daily Streamflow Extension Models: a Case Study of Gaoping River Basin, Taiwan," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1499-1513, March.
    17. Noman Khan & Fath U Min Ullah & Ijaz Ul Haq & Samee Ullah Khan & Mi Young Lee & Sung Wook Baik, 2021. "AB-Net: A Novel Deep Learning Assisted Framework for Renewable Energy Generation Forecasting," Mathematics, MDPI, vol. 9(19), pages 1-18, October.
    18. V. Ramaswamy & F. Saleh, 2020. "Ensemble Based Forecasting and Optimization Framework to Optimize Releases from Water Supply Reservoirs for Flood Control," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(3), pages 989-1004, February.
    19. Rosalva Mendoza Ramírez & Maritza Liliana Arganis Juárez & Ramón Domínguez Mora & Luis Daniel Padilla Morales & Óscar Arturo Fuentes Mariles & Alejandro Mendoza Reséndiz & Eliseo Carrizosa Elizondo & , 2021. "Operation Policies through Dynamic Programming and Genetic Algorithms, for a Reservoir with Irrigation and Water Supply Uses," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(5), pages 1573-1586, March.
    20. Maya Rajnarayan Ray & Arup Kumar Sarma, 2016. "Influence of Time Discretization and Input Parameter on the ANN Based Synthetic Streamflow Generation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4695-4711, October.

    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:spr:waterr:v:35:y:2021:i:2:d:10.1007_s11269-020-02748-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.