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Model of selecting prediction window in ramps forecasting

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  • Ouyang, Tinghui
  • Zha, Xiaoming
  • Qin, Liang
  • Xiong, Yi
  • Huang, Heming

Abstract

Prediction of wind power ramp events is important to the stability operation of power system, it is realized by combining wind power prediction of several continuous units for long-term power prediction, then using detecting algorithms to extract ramps. A prediction unit is a prediction time window. Its size impacts the accuracy of predicting ramps, and an optimization model is proposed to select the suitable window size. First, a swinging door algorithm is applied to extract ramp events from historical data. A model for optimizing the time window size is established based on the minimum non-ramp data in a ramp window. The solution of the proposed model is discussed, including the selection of variables, constraints and algorithm. The model presented in this paper is tested, and performance of selected time window is discussed. Computational analysis demonstrates the validity of the model.

Suggested Citation

  • Ouyang, Tinghui & Zha, Xiaoming & Qin, Liang & Xiong, Yi & Huang, Heming, 2017. "Model of selecting prediction window in ramps forecasting," Renewable Energy, Elsevier, vol. 108(C), pages 98-107.
    • Handle: RePEc:eee:renene:v:108:y:2017:i:c:p:98-107
    DOI: 10.1016/j.renene.2017.02.035
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    References listed on IDEAS

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    Cited by:

    1. Junwei Fu & Yuna Ni & Yuming Ma & Jian Zhao & Qiuyi Yang & Shiyi Xu & Xiang Zhang & Yuhua Liu, 2023. "A Visualization-Based Ramp Event Detection Model for Wind Power Generation," Energies, MDPI, vol. 16(3), pages 1-16, January.
    2. Guglielmo D’Amico & Filippo Petroni & Salvatore Vergine, 2022. "Ramp Rate Limitation of Wind Power: An Overview," Energies, MDPI, vol. 15(16), pages 1-15, August.
    3. Ouyang, Tinghui & Zha, Xiaoming & Qin, Liang & He, Yusen & Tang, Zhenhao, 2019. "Prediction of wind power ramp events based on residual correction," Renewable Energy, Elsevier, vol. 136(C), pages 781-792.
    4. Tang, Zhenhao & Zhao, Gengnan & Ouyang, Tinghui, 2021. "Two-phase deep learning model for short-term wind direction forecasting," Renewable Energy, Elsevier, vol. 173(C), pages 1005-1016.
    5. Lee, Joseph C.Y. & Draxl, Caroline & Berg, Larry K., 2022. "Evaluating wind speed and power forecasts for wind energy applications using an open-source and systematic validation framework," Renewable Energy, Elsevier, vol. 200(C), pages 457-475.
    6. He, Yaoyao & Zhu, Chuang & An, Xueli, 2023. "A trend-based method for the prediction of offshore wind power ramp," Renewable Energy, Elsevier, vol. 209(C), pages 248-261.

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