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Using reanalysis data to quantify extreme wind power generation statistics : a 33 year case study in Great Britain

Cannon, D.J., Brayshaw, D.J., Methven, J., Coker, P.J. and Lenaghan, D. (2015) Using reanalysis data to quantify extreme wind power generation statistics : a 33 year case study in Great Britain. Renewable Energy, 75. pp. 767-778. ISSN 0960-1481

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To link to this article DOI: 10.1016/j.renene.2014.10.024

Abstract/Summary

With a rapidly increasing fraction of electricity generation being sourced from wind, extreme wind power generation events such as prolonged periods of low (or high) generation and ramps in generation, are a growing concern for the efficient and secure operation of national power systems. As extreme events occur infrequently, long and reliable meteorological records are required to accurately estimate their characteristics. Recent publications have begun to investigate the use of global meteorological “reanalysis” data sets for power system applications, many of which focus on long-term average statistics such as monthly-mean generation. Here we demonstrate that reanalysis data can also be used to estimate the frequency of relatively short-lived extreme events (including ramping on sub-daily time scales). Verification against 328 surface observation stations across the United Kingdom suggests that near-surface wind variability over spatiotemporal scales greater than around 300 km and 6 h can be faithfully reproduced using reanalysis, with no need for costly dynamical downscaling. A case study is presented in which a state-of-the-art, 33 year reanalysis data set (MERRA, from NASA-GMAO), is used to construct an hourly time series of nationally-aggregated wind power generation in Great Britain (GB), assuming a fixed, modern distribution of wind farms. The resultant generation estimates are highly correlated with recorded data from National Grid in the recent period, both for instantaneous hourly values and for variability over time intervals greater than around 6 h. This 33 year time series is then used to quantify the frequency with which different extreme GB-wide wind power generation events occur, as well as their seasonal and inter-annual variability. Several novel insights into the nature of extreme wind power generation events are described, including (i) that the number of prolonged low or high generation events is well approximated by a Poission-like random process, and (ii) whilst in general there is large seasonal variability, the magnitude of the most extreme ramps is similar in both summer and winter. An up-to-date version of the GB case study data as well as the underlying model are freely available for download from our website: http://www.met.reading.ac.uk/~energymet/data/Cannon2014/.

Item Type:Article
Refereed:Yes
Divisions:Interdisciplinary centres and themes > Walker Institute
Faculty of Science > School of Construction Management and Engineering > Innovative and Sustainable Technologies
Interdisciplinary centres and themes > Centre for Technologies for Sustainable Built Environments (TSBE)
Interdisciplinary centres and themes > Energy Research
Faculty of Science > School of Mathematical and Physical Sciences > NCAS
Faculty of Science > School of Mathematical and Physical Sciences > Department of Meteorology
ID Code:38448
Uncontrolled Keywords:Wind power extremes Persistent low wind Wind power ramping Inter-annual/seasonal variability Climatology Wind integration
Additional Information:This article is currently published online, but I received an email today stating that it is "about to be published in Renewable Energy". I guess this means that the full published version will be available very soon.
Publisher:Elsevier

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