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A New Approach to Obtain Synthetic Wind Power Forecasts for Integration Studies

Author

Listed:
  • Jon Olauson

    (Division of Electricity, Department of Engineering Sciences, Uppsala University, Box 534, 751 21 Uppsala, Sweden)

  • Johan Bladh

    (Vattenfall, Evenemangsgatan 13, 169 92 Solna, Sweden)

  • Joakim Lönnberg

    (Vattenfall, Evenemangsgatan 13, 169 92 Solna, Sweden)

  • Mikael Bergkvist

    (Division of Electricity, Department of Engineering Sciences, Uppsala University, Box 534, 751 21 Uppsala, Sweden)

Abstract

When performing wind integration studies, synthetic wind power forecasts are key elements. Historically, data from operational forecasting systems have been used sparsely, likely due to the high costs involved. Purely statistical methods for simulating wind power forecasts are more common,but have problems mimicking all relevant aspects of actual forecasts. Consequently, a new approach to obtain wind power forecasts for integration studies is proposed, relying on long time series of freely and globally available reforecasts. In order to produce synthetic forecasts with similar properties as operational ditto, some processing (noise addition and error reduction) is necessary. Validations with measurements from Belgium and Sweden show that the method is adequate; and distributions, correlations, autocorrelations and power spectral densities of forecast errors correspond well. Furthermore, abrupt changes when forecasts are updated and the existence of level and phase errors are reproduced. The influence from terrain complexity on error magnitude is promising, but more data is necessary for a proper validation.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:9:y:2016:i:10:p:800-:d:79808
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    References listed on IDEAS

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    Cited by:

    1. Francisco Martínez-Álvarez & Alicia Troncoso & José C. Riquelme, 2017. "Recent Advances in Energy Time Series Forecasting," Energies, MDPI, vol. 10(6), pages 1-3, June.
    2. Shiyu Liu & Gengfeng Li & Haipeng Xie & Xifan Wang, 2017. "Correlation Characteristic Analysis for Wind Speed in Different Geographical Hierarchies," Energies, MDPI, vol. 10(2), pages 1-20, February.

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