Abstract
Synthetic wind-speed generators can provide a detailed characterisation of the wind variability at different time scales. A keen interest in the availability of synthetic wind speeds has recently risen in wind power modelling applications. In particular, a proper simulation of the diurnal and annual variability of the wind speed is sought that can lead to a more efficient grid integration of this renewable source. This paper proposes a statistical model for generating synthetic wind speeds consistent with both the probability density function and the spectral density function of a measured wind-speed dataset and that simulates accurately its average diurnal variation. To test the proposed methodology, multiple synthetic time series are generated using three long-term wind-speed time series recorded at a meteorological site in the Netherlands. The accuracy in terms of the statistical descriptors of the generated time series and their average diurnal variation is assessed with respect to the target data. We show that the average diurnal cycles present in all the three measured time series are always reproduced accurately, and that the statistical descriptors of the target dataset are constantly matched with high accuracy. Possible advantages of the present approach in terms of power system modelling are discussed.
Original language | English |
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Publication status | Unpublished - 2020 |
Event | TORQUE 2020: The Science of Making Torque from Wind - TU Delft (online), Delft , Netherlands Duration: 28 Sept 2020 → 2 Oct 2020 https://www.torque2020.org |
Conference
Conference | TORQUE 2020 |
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Country/Territory | Netherlands |
City | Delft |
Period | 28/09/20 → 2/10/20 |
Internet address |
Bibliographical note
Publisher Copyright:© Published under licence by IOP Publishing Ltd.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.