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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
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    Citations

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    Cited by:

    1. 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).
    2. 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.
    3. 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).
    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. 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.
    6. 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).
    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.

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