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Short-term wind power prediction based on spatial model

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  • Ye, Lin
  • Zhao, Yongning
  • Zeng, Cheng
  • Zhang, Cihang

Abstract

Large-scale integration of wind energy into power systems may cause operational problems due to the stochastic nature of wind. A short-term wind power prediction model based on physical approach and spatial correlation is proposed to characterize the uncertainty and dependence structure of wind turbines' outputs in the wind farm. Firstly, continuous partial differential equation of each wind turbine has been developed according to its specific spatial location and the layout of its neighboring correlated wind turbines. Then, spatial correlation matrix of wind speed is derived by discretizing differential equation at each wind turbine using a finite volume method (FVM). Wind speed at each turbine is acquired by solving the relevant differential equation under given boundary conditions. Finally, the wind speed is converted to wind power production via a practical power curve model. Prediction results showed that the spatial correlation model can accurately characterize the correlations among outputs of wind turbines and reduce the error of short-term wind power prediction.

Suggested Citation

  • Ye, Lin & Zhao, Yongning & Zeng, Cheng & Zhang, Cihang, 2017. "Short-term wind power prediction based on spatial model," Renewable Energy, Elsevier, vol. 101(C), pages 1067-1074.
    • Handle: RePEc:eee:renene:v:101:y:2017:i:c:p:1067-1074
    DOI: 10.1016/j.renene.2016.09.069
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    14. Konstantinos Konstas & Panos T. Chountalas & Eleni A. Didaskalou & Dimitrios A. Georgakellos, 2023. "A Pragmatic Framework for Data-Driven Decision-Making Process in the Energy Sector: Insights from a Wind Farm Case Study," Energies, MDPI, vol. 16(17), pages 1-26, August.
    15. Huang, Yu & Zhang, Bingzhe & Pang, Huizhen & Wang, Biao & Lee, Kwang Y. & Xie, Jiale & Jin, Yupeng, 2022. "Spatio-temporal wind speed prediction based on Clayton Copula function with deep learning fusion," Renewable Energy, Elsevier, vol. 192(C), pages 526-536.
    16. Xiaojun Shen & Chongcheng Zhou & Xuejiao Fu, 2018. "Study of Time and Meteorological Characteristics of Wind Speed Correlation in Flat Terrains Based on Operation Data," Energies, MDPI, vol. 11(1), pages 1-16, January.
    17. Jia, Mengshuo & Huang, Shaowei & Wang, Zhiwen & Shen, Chen, 2021. "Privacy-preserving distributed parameter estimation for probability distribution of wind power forecast error," Renewable Energy, Elsevier, vol. 163(C), pages 1318-1332.
    18. Yimei Wang & Yongqian Liu & Li Li & David Infield & Shuang Han, 2018. "Short-Term Wind Power Forecasting Based on Clustering Pre-Calculated CFD Method," Energies, MDPI, vol. 11(4), pages 1-19, April.
    19. Li, Yang & Shen, Xiaojun & Zhou, Chongcheng, 2023. "Dynamic multi-turbines spatiotemporal correlation model enabled digital twin technology for real-time wind speed prediction," Renewable Energy, Elsevier, vol. 203(C), pages 841-853.
    20. Wu, Zhou & Zeng, Shaoxiong & Jiang, Ruiqi & Zhang, Haoran & Yang, Zhile, 2023. "Explainable temporal dependence in multi-step wind power forecast via decomposition based chain echo state networks," Energy, Elsevier, vol. 270(C).
    21. Wang, Cong & Zhang, Hongli & Fan, Wenhui & Ma, Ping, 2017. "A new chaotic time series hybrid prediction method of wind power based on EEMD-SE and full-parameters continued fraction," Energy, Elsevier, vol. 138(C), pages 977-990.
    22. Tawn, R. & Browell, J., 2022. "A review of very short-term wind and solar power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    23. Wang, Ying & Wang, Jianzhou & Li, Zhiwu & Yang, Hufang & Li, Hongmin, 2021. "Design of a combined system based on two-stage data preprocessing and multi-objective optimization for wind speed prediction," Energy, Elsevier, vol. 231(C).
    24. Zheng, Ling & Zhou, Bin & Or, Siu Wing & Cao, Yijia & Wang, Huaizhi & Li, Yong & Chan, Ka Wing, 2021. "Spatio-temporal wind speed prediction of multiple wind farms using capsule network," Renewable Energy, Elsevier, vol. 175(C), pages 718-730.
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