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Restoring the missing high-frequency fluctuations in a wind power model based on reanalysis data

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  • Olauson, Jon
  • Bergström, Hans
  • Bergkvist, Mikael

Abstract

A previously developed model based on MERRA reanalysis data underestimates the high-frequency variability and step changes of hourly, aggregated wind power generation. The goal of this work is to restore these fluctuations. Since the volatility of the high-frequency signal varies in time, machine learning techniques were employed to predict the volatility. As predictors, derivatives of the output from the original “MERRA model” as well as empirical orthogonal functions of several meteorological variables were used. A FFT-IFFT approach, including a search algorithm for finding appropriate phase angles, was taken to generate a signal that was subsequently transformed to simulated high-frequency fluctuations using the predicted volatility. When comparing to the original MERRA model, the improved model output has a power spectral density and step change distribution in much better agreement with measurements. Moreover, the non-stationarity of the high-frequency fluctuations was captured to a large degree. The filtering and noise addition however resulted in a small increase in the RMS error.

Suggested Citation

  • Olauson, Jon & Bergström, Hans & Bergkvist, Mikael, 2016. "Restoring the missing high-frequency fluctuations in a wind power model based on reanalysis data," Renewable Energy, Elsevier, vol. 96(PA), pages 784-791.
    • Handle: RePEc:eee:renene:v:96:y:2016:i:pa:p:784-791
    DOI: 10.1016/j.renene.2016.05.008
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    References listed on IDEAS

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    1. Cannon, D.J. & Brayshaw, D.J. & Methven, J. & Coker, P.J. & Lenaghan, D., 2015. "Using reanalysis data to quantify extreme wind power generation statistics: A 33 year case study in Great Britain," Renewable Energy, Elsevier, vol. 75(C), pages 767-778.
    2. Olauson, Jon & Bergkvist, Mikael, 2015. "Modelling the Swedish wind power production using MERRA reanalysis data," Renewable Energy, Elsevier, vol. 76(C), pages 717-725.
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    Cited by:

    1. Liu, Chenyu & Zhang, Xuemin & Mei, Shengwei & Zhen, Zhao & Jia, Mengshuo & Li, Zheng & Tang, Haiyan, 2022. "Numerical weather prediction enhanced wind power forecasting: Rank ensemble and probabilistic fluctuation awareness," Applied Energy, Elsevier, vol. 313(C).
    2. Hdidouan, Daniel & Staffell, Iain, 2017. "The impact of climate change on the levelised cost of wind energy," Renewable Energy, Elsevier, vol. 101(C), pages 575-592.
    3. Jon Olauson & Johan Bladh & Joakim Lönnberg & Mikael Bergkvist, 2016. "A New Approach to Obtain Synthetic Wind Power Forecasts for Integration Studies," Energies, MDPI, vol. 9(10), pages 1-16, October.
    4. Ikegami, Takashi & Urabe, Chiyori T. & Saitou, Tetsuo & Ogimoto, Kazuhiko, 2018. "Numerical definitions of wind power output fluctuations for power system operations," Renewable Energy, Elsevier, vol. 115(C), pages 6-15.
    5. Lopez-Villalobos, C.A. & Rodriguez-Hernandez, O. & Martínez-Alvarado, O. & Hernandez-Yepes, J.G., 2021. "Effects of wind power spectrum analysis over resource assessment," Renewable Energy, Elsevier, vol. 167(C), pages 761-773.

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