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Published March 2014 | Published
Journal Article Open

Consideration of Wind Speed Variability in Creating a Regional Aggregate Wind Power Time Series

Abstract

For the purposes of understanding the impacts on the electricity network, estimates of hourly aggregate wind power generation for a region are required. However, the availability of wind production data for the UK is limited, and studies often rely on measured wind speeds from a network of meteorological (met) stations. Another option is to use historical wind speeds from a reanalysis dataset, with a resolution of around 40–50 km. Mesoscale models offer a potentially more desirable solution, with a homogeneous set of wind speeds covering a wide area at resolutions of 1–50 km, but they are computationally expensive to run at high resolution. An understanding of the most appropriate choice of data requires knowledge of the variability in time and space and how well that is represented by the choice of model. Here it is demonstrated that in regions offshore, or in relatively smooth terrain where variability in wind speeds is smaller, lower resolution models or single point records may suffice to represent aggregate power generation in a sub-region. The need for high resolution modelling in areas of complex terrain where spatial and temporal variability is higher is emphasised, particularly when the distribution of wind generation capacity is uneven over the region.

Additional Information

© 2014 by the authors; licensee MDPI, Basel, Switzerland. This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Received: 29 November 2013 / Revised: 29 January 2014 / Accepted: 18 February 2014 / Published: 27 February 2014. (This article belongs to the Special Issue Spatial and Temporal Variation of the Wind Resource) This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) via the Adaptation and Resilience in Energy Systems (ARIES) project, reference number EP/I035773/1. The authors declare no conflict of interest.

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August 22, 2023
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