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

Lucy C. Cradden, Francesco Restuccia, Samuel L. Hawkins and Gareth P. Harrison
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Lucy C. Cradden: School of Engineering, University of Edinburgh, Kings Buildings, Mayfield Road, Edinburgh EH9 3JL, UK
Francesco Restuccia: Department of Mechanical and Civil Engineering, California Institute of Technology, 1200 E California Blvd, Pasadena, CA 91125, USA
Samuel L. Hawkins: Vattenfall Wind Power, The Tun Building, 4 Jackson's Entry, Holyrood Road, Edinburgh EH8 8PJ, UK
Gareth P. Harrison: School of Engineering, University of Edinburgh, Kings Buildings, Mayfield Road, Edinburgh EH9 3JL, UK

Resources, 2014, vol. 3, issue 1, 1-20

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.

Keywords: wind; resource; variability; mesoscale; methodologies (search for similar items in EconPapers)
JEL-codes: Q1 Q2 Q3 Q4 Q5 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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