IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v170y2021icp92-106.html
   My bibliography  Save this article

Operation rule extraction based on deep learning model with attention mechanism for wind-solar-hydro hybrid system under multiple uncertainties

Author

Listed:
  • Zhang, Zhendong
  • Qin, Hui
  • Li, Jie
  • Liu, Yongqi
  • Yao, Liqiang
  • Wang, Yongqiang
  • Wang, Chao
  • Pei, Shaoqian
  • Li, Pusheng
  • Zhou, Jianzhong

Abstract

With the increasing environmental problems caused by the use of traditional fossil energy, renewable clean energy has gradually attracted attention. Owing to uncertain components such as solar radiation intensity, wind speed, and power load, it brings difficulties to short-term operation of wind-solar-hydro (WSH) hybrid system. Therefore, the focus of this study is to solve the problem of probabilistic optimal operation model of WSH hybrid system under multiple uncertainties. The two keys to solving the probabilistic optimal operation model are estimating the probability density function (PDF) of model state variables and extracting the model operation rules. In this study, probability prediction, kernel density estimation, and deep learning model with attention mechanism are used to quantify uncertainty, estimate probability density functions, and extract operation rules, respectively. The experimental results show that the estimated probability density function is very practical and can provide abundant decision-making information to the dispatcher, and the extracted rules are very effective and can guide the dispatcher to perform operation. At the same time, the results also show that the rules extracted by the deep learning model using the attention mechanism increase the accuracy by 15.33% on average compared with those without attention mechanism.

Suggested Citation

  • Zhang, Zhendong & Qin, Hui & Li, Jie & Liu, Yongqi & Yao, Liqiang & Wang, Yongqiang & Wang, Chao & Pei, Shaoqian & Li, Pusheng & Zhou, Jianzhong, 2021. "Operation rule extraction based on deep learning model with attention mechanism for wind-solar-hydro hybrid system under multiple uncertainties," Renewable Energy, Elsevier, vol. 170(C), pages 92-106.
    • Handle: RePEc:eee:renene:v:170:y:2021:i:c:p:92-106
    DOI: 10.1016/j.renene.2021.01.115
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148121001221
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2021.01.115?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Li, He & Liu, Pan & Guo, Shenglian & Zuo, Qiting & Cheng, Lei & Tao, Jie & Huang, Kangdi & Yang, Zhikai & Han, Dongyang & Ming, Bo, 2022. "Integrating teleconnection factors into long-term complementary operating rules for hybrid power systems: A case study of Longyangxia hydro-photovoltaic plant in China," Renewable Energy, Elsevier, vol. 186(C), pages 517-534.
    2. 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.
    3. 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.
    4. 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).
    5. Kong, Xiangfei & Du, Xinyu & Xue, Guixiang & Xu, Zhijie, 2023. "Multi-step short-term solar radiation prediction based on empirical mode decomposition and gated recurrent unit optimized via an attention mechanism," Energy, Elsevier, vol. 282(C).
    6. Guo, Yi & Ming, Bo & Huang, Qiang & Liu, Pan & Wang, Yimin & Fang, Wei & Zhang, Wei, 2022. "Evaluating effects of battery storage on day-ahead generation scheduling of large hydro–wind–photovoltaic complementary systems," Applied Energy, Elsevier, vol. 324(C).
    7. Lu Gan & Dirong Xu & Xiuyun Chen & Pengyan Jiang & Benjamin Lev & Zongmin Li, 2023. "Sustainable portfolio re-equilibrium on wind-solar-hydro system: An integrated optimization with combined meta-heuristic," Energy & Environment, , vol. 34(5), pages 1383-1408, August.

    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:renene:v:170:y:2021:i:c:p:92-106. 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.

    We have no bibliographic references for this item. You can help adding them by using 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/renewable-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.