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Open AccessArticle

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

1
Division of Electricity, Department of Engineering Sciences, Uppsala University, Box 534, 751 21 Uppsala, Sweden
2
Vattenfall, Evenemangsgatan 13, 169 92 Solna, Sweden
*
Author to whom correspondence should be addressed.
Energies 2016, 9(10), 800; https://doi.org/10.3390/en9100800
Received: 29 June 2016 / Accepted: 27 September 2016 / Published: 3 October 2016
(This article belongs to the Special Issue Energy Time Series Forecasting)
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. View Full-Text
Keywords: wind power forecasting; synthetic forecasts; global ensemble forecasting system (GEFS) reforecast; power system studies; wind power integration; production planning; dispatch wind power forecasting; synthetic forecasts; global ensemble forecasting system (GEFS) reforecast; power system studies; wind power integration; production planning; dispatch
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MDPI and ACS Style

Olauson, J.; Bladh, J.; Lönnberg, J.; Bergkvist, M. A New Approach to Obtain Synthetic Wind Power Forecasts for Integration Studies. Energies 2016, 9, 800.

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