Wind Energy Handbook. Michael Barton Graham
communities throughout the world, but with only limited success. This class of installation has its own particular characteristics, and, given the limited size of the market at present, this specialist area is not dealt with in this book.
References
1 European Wind Energy Association (2009). Wind Energy – The Facts. London: Earthscan.
2 Golding, E.W. (1955). The Generation of Electricity from Wind Power. London: E. & F.N. Spon (reprinted R.I. Harris, London 1976).
3 Hau, E. (2010). Wind Turbines: Fundamentals, Technologies, Application, Economics, 2e. Heidelberg: Springer.
4 Jamieson, P. (2018). Innovation in Wind Turbine Design, 2e. Chichester: Wiley.
5 Molly, J.P., Keuper, A., and Veltrup, M. (1993). Statistical WEC design and cost trends. Proceedings of the European Wind Energy Conference, Travemunde (8–12 March 1993), pp. 57–59.
6 Musgrove, P. (2010). Wind Power. Cambridge: Cambridge University Press.
7 Putnam, G.C. (1948). Power from the Wind. New York: Van Nostrand Reinhold.
8 Roberts C. (2018) Review of international grid codes, Lawrence Berkeley National Laboratory file:///Users/scenj/Downloads/eScholarship%20UC%20item%205sv540qx.pdf (accessed 7 December 2019).
9 Serrano‐González, J. and Lacal‐Arántegui, R. (2016). Technological evolution of onshore wind turbines – a market‐based analysis. Wind Energy 19: 2171–2187.
10 Spera, D.A. (1994). Wind Turbine Technology: Fundamental Concepts of Wind Turbine Engineering. New York: ASME Press.
Websites
1 Global Wind Energy Council (2020). Global wind report 2019. https://gwec.net/global-wind-report-2019/ (accessed 30 July 2020).
2 REN21 (2020). 2020 Global status report. https://www.ren21.net/gsr-2020/ (accessed 31 July 2020).
3 US Energy Information Administration, (2019). International data. https://www.eia.gov/beta/international/data (accessed 26 May 2019).
4 World Wind Energy Association (2020). Statistics. https://wwindea.org/blog/category/statistics/ (accessed 30 July 2020).
Further Reading
1 Anderson, C. (2020). Wind Turbines: Theory and Practice. Cambridge University Press.
2 Boyle, G. (ed.) (2017). Renewable Energy and the Grid. London: Earthscan.
3 Eggleston, D.M. and Stoddard, F.S. (1987). Wind Turbine Engineering Design. New York: Van Nostrand Reinhold.
4 Freris, L.L. (ed.) (1990). Wind Energy Conversion Systems. New York: Prentice‐Hall.
5 Harrison, R., Hau, E., and Snel, H. (2000). Large Wind Turbines, Design and Economics. Chichester: Wiley.
6 Manwell, J.W., McGown, J.G., and Rogers, A.L. (2009). Wind Energy Explained: Theory, Design and Application, 2e. Oxford: Wiley Blackwell.
7 Orkney Sustainable Energy (1955). Costa Head experimental wind turbine. www.orkneywind.co.uk/costa.html (accessed 3 January 2020).
8 Twidell, J.W. and Weir, A.D. (2015). Renewable Energy Resources, 3e. Abingdon: Routledge.
9 Twidell, J.W. and Gaudiosi, G. (eds.) (2009). Offshore Wind Power. Brentford: Multi‐science Publishing Co.
2.1 The nature of the wind
The energy available in the wind varies as the cube of the wind speed, so an understanding of the characteristics of the wind resource is critical to all aspects of wind energy exploitation, from the identification of suitable sites and predictions of the economic viability of wind farm projects through to the design of wind turbines themselves, along with understanding their effect on electricity distribution networks and consumers.
From the point of view of wind energy, the most striking characteristic of the wind resource is its variability. The wind is highly variable, both geographically and temporally. Furthermore, this variability persists over a very wide range of scales, both in space and time. The importance of this is amplified by the cubic relationship to available energy.
On a large scale, spatial variability describes the fact that there are many different climatic regions in the world, some much windier than others. These regions are largely dictated by the latitude, which affects the amount of insolation. Within any one climatic region, there is a great deal of variation on a smaller scale, largely dictated by physical geography – the proportion of land and sea, the size of land masses, and the presence of mountains or plains, for example. The type of vegetation may also have a significant influence through its effects on the absorption or reflection of solar radiation, affecting surface temperatures, and on humidity.
More locally, the topography has a major effect on the wind climate. More wind is experienced on the tops of hills and mountains than in the lee of high ground or in sheltered valleys, for instance. More locally still, wind velocities are significantly reduced by obstacles such as trees or buildings.
At a given location, temporal variability on a large scale means that the amount of wind may vary from one year to the next, with even longer‐scale variations on a scale of decades or more. These long‐term variations are not well understood and may make it difficult to make accurate predictions of the economic viability of particular wind farm projects, for instance.
On timescales shorter than a year, seasonal variations are much more predictable, although there are large variations on shorter timescales still, which although reasonably well understood are often not very predictable more than a few days ahead. Depending on location, there may also be considerable variations with the time of day (diurnal variations), which again are usually fairly predictable. On these timescales, the predictability of the wind is important for integrating large amounts of wind power into the electricity network, to allow the other generating plant supplying the network to be organised appropriately.
On still shorter timescales of minutes down to seconds or less, wind speed variations known as turbulence can have a very significant impact on the design and performance of the individual wind turbines as well as on the quality of power delivered to the network and its effect on consumers.
Van der Hoven (1957) constructed a wind speed spectrum from long‐ and short‐term records at Brookhaven, New York, showing clear peaks corresponding to the synoptic, diurnal, and turbulent effects just referred to (Figure 2.1). This spectrum shows a distinct ‘spectral gap’ between the diurnal and turbulent peaks, showing that the synoptic and diurnal variations can be treated as quite distinct from the higher frequency fluctuations of turbulence, with very little energy in the spectrum in the region between 2 hours and 10 minutes. As indicated in the next section, however, the nature of the wind regime in different geographical locations can vary widely, so the Van der Hoven spectrum cannot be assumed to be universally applicable, and the spectral gap may not always be so distinct. Nevertheless, the concept of a spectral gap at frequencies below 10 minutes is very often used, often implicitly, when making assumptions about the wind regime; for example, when defining turbulence intensity.