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

Resources 2014, 3, 215-234; doi:10.3390/resources3010215 OPEN ACCESS resources ISSN 2079-9276 www.mdpi.com/journal/resources Article Consideration of Wind Speed Variability in Creating a Regional Aggregate Wind Power Time Series Lucy C. Cradden 1,*, Francesco Restuccia 2, Samuel L. Hawkins 3 and Gareth P. Harrison 1 1 2 3 School of Engineering, University of Edinburgh, Kings Buildings, Mayfield Road, Edinburgh EH9 3JL, UK; E-Mail: Department of Mechanical and Civil Engineering, California Institute of Technology, 1200 E California Blvd, Pasadena, CA 91125, USA; E-Mail: Vattenfall Wind Power, The Tun Building, 4 Jackson’s Entry, Holyrood Road, Edinburgh EH8 8PJ, UK; E-Mail: * Author to whom correspondence should be addressed; E-Mail: ; Tel.: +44-131-650-5612. Received: 29 November 2013; in revised form: 29 January 2014 / Accepted: 18 February 2014 / Published: 27 February 2014 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 Resources 2014, 3 216 1. Introduction The connection of large amounts of wind generation to the electricity network is frequently discussed, often focusing on aspects of the variability of wind power and the difficulty in managing less-predictable power flows. Developing a model to determine the impact of power fluctuations is crucial for electricity network operators. For a specific region of interest, the combined effects of spatial and temporal variability will affect its aggregate power production. The wind climate in the UK is affected strongly by heterogeneous terrain, leading to diverse wind speeds within relatively small areas. Wind generation capacity is not evenly distributed around the country, due to this variability, but also grid capacity, planning constraints, land availability and the different regulations and incentives provided by local governments. Temporally, different weather systems affecting different parts of the country could smooth the aggregate output, but equally, when the weather is similar everywhere, the peaks and troughs will be emphasised. The availability of suitable data to analyse the variability characteristics of UK wind generation is limited. Actual metered power is protected by commercial interests, whilst market data can be hard to process and both can be corrupted by undocumented reliability issues. The pace of wind generation development is such that only a few years of representative data would be available. Many previous studies have used some variation of a method involving transforming met station data to wind turbine hub height, applying a turbine power curve and aggregating this for a region. Sinden [1] used wind speeds measured at 10m above ground level (a.g.l) at 66 unspecified met stations over a period of 23 years and, using a wind turbine power curve, derived an annual capacity factor for each record. Wind speeds were adjusted according to their situation (coastal, inland or island) and region (south, central or north) so that the annual capacity factor for the region was considered appropriate, and the overall capacity factor for the UK would be 30%. Offshore sites were not considered, and the capacity was considered to be evenly distributed at the met station locations. In the context of analysing surface wind speed variability more generally, Earl et al. [2] describe wind power variability with a similar method to [1] using 10 m wind speed data from 40 UK met stations. As with [1], this work assumes the distribution of wind capacity matches the distribution of met stations—an assumption that could be misleading, as will be shown later. This does not detract from their overall conclusion that historical interannual variability of wind speeds is relatively large. Pöyry [3] present an analysis of aggregate power variability, which again uses met station data measured at 10 m a.g.l, in this case over 8 years from 35 onshore locations around Britain and Ireland. Offshore is considered using reanalysis model points. The sites were chosen to be representative of areas likely to see future development but their locations are not identified explicitly, nor the capacity scenario for each area. The authors state that it is not possible to verify the hourly GB output due to lack of data, but for the year 2007, a good correlation of the model data is shown for metered hourly Irish capacity factors. Met station datasets often have some periods with no records, and have limited coverage, particularly offshore. One option for addressing this involves creating a statistical time series model of aggregate power, such as that described in [4]. The model has been trained on a set of met station records, transformed into a single variable representing UK power output. The training dataset Resources 2014, 3 217 consisted of 6 years of wind speeds at 10 m a.g.l from 116 onshore met stations. The power for each of 16 regions was taken as the average power from all the stations in that region and the capacity in each region weighted according to a generation scenario proposed for 2030. A relatively simple adjustment was made to allow for the inclusion of offshore sites which typically have some different time series characteristics. An investigation by Kubik et al. [5] established a basis for using relatively low resolution reanalysis data (~50 km) as an alternative to met station datasets to derive regional aggregate power estimates. Good correspondence was found between actual power outputs and their estimate from the MERRA reanalysis [6] for Northern Ireland, but the authors note that the wind capacity and the met stations used in the comparison are quite evenly distributed across the study region. Reid and Turner (...truncated)