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The impact of large scale atmospheric circulation patterns on wind power generation and its potential predictability: a case study over the UK

Brayshaw, D. J., Troccoli, A., Fordham, R. and Methven, J. (2011) The impact of large scale atmospheric circulation patterns on wind power generation and its potential predictability: a case study over the UK. Renewable Energy, 36 (8). pp. 2087-2096. ISSN 0960-1481

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To link to this article DOI: 10.1016/j.renene.2011.01.025

Abstract/Summary

Over recent years there has been an increasing deployment of renewable energy generation technologies, particularly large-scale wind farms. As wind farm deployment increases, it is vital to gain a good understanding of how the energy produced is affected by climate variations, over a wide range of time-scales, from short (hours to weeks) to long (months to decades) periods. By relating wind speed at specific sites in the UK to a large-scale climate pattern (the North Atlantic Oscillation or "NAO"), the power generated by a modelled wind turbine under three different NAO states is calculated. It was found that the wind conditions under these NAO states may yield a difference in the mean wind power output of up to 10%. A simple model is used to demonstrate that forecasts of future NAO states can potentially be used to improve month-ahead statistical forecasts of monthly-mean wind power generation. The results confirm that the NAO has a significant impact on the hourly-, daily- and monthly-mean power output distributions from the turbine with important implications for (a) the use of meteorological data (e.g. their relationship to large scale climate patterns) in wind farm site assessment and, (b) the utilisation of seasonal-to-decadal climate forecasts to estimate future wind farm power output. This suggests that further research into the links between large-scale climate variability and wind power generation is both necessary and valuable.

Item Type:Article
Refereed:Yes
Divisions: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:19462
Uncontrolled Keywords:Climate variability, Wind power, Seasonal forecasting
Publisher:Elsevier

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