WIND ENERGY RESOURCE ASSESSMENT: A CASE STUDY Norman Maphosa. National university of Science and Technology, Department of Technical Teacher Education, NUST, Box AC 939, Ascot, Bulawayo, Zimbabwe. Tel: 63 9 884 Ext 355 nmaphosa@nust.ac.w Key words: Wind resource, WAsP, wind climate, geostrophic, energy production. ABSTRACT Global warming and high cost of fossil fuels dictates the exploitation of alternative sources of energy such as wind. This paper presents a background to standard methods of wind energy resource assessment using a case study of Lynch Knoll and Beaufort Court wind farms in England. The Wind atlas analysis and applications Program software, WAsP, is used to assess wind energy potential and to predict wind climate from geostrophic winds of a given area. In this paper, meteorological data from Lyneham meteorological station was used to predict the wind resource and wind turbine energy production at Lynch Knoll, while data from Heathrow meteorological station was used for similar predictions at Beaufort Court. Data from both meteorological stations were used to draw up observed wind climates at the anemometer sites. Site contour maps were digitised using the WAsP Map Editor. Observed wind climates, digitied contour maps, terrain roughness length, obstacle groups and their porosity were used as input to the WAsP model. In the Beaufort Court model, Vestas V9, 5 kw turbine was used while for Lynch Knoll, Vestas V39, 500 kw turbine was used in place of the Enercon 500 kw turbine which was not available in the WAsP model folder. WAsP predictions are highly influenced by terrain topography. Weibull probability distribution graphs of wind speed, power density and annual energy production were drawn. A directional wind rose for January 04 were drawn for each site. The predictions were close to the actual turbine output in both cases. Such validation of WAsP predictions means that WAsP can be used for wind resource assessment of any site. Other studies have shown poor predictions for rugged terrain with gradients greater than 0.3. Similar predictions can be carried out in
Zimbabwe. Small 1kW wind turbines were installed in Temaruru and Vungu in 1999 without wind resource assessment. 1.0. Introduction The use of fossil fuels has resulted in global warming caused by the greenhouse emissions associated with carbonbased fuels. Wind energy is available and is known to have been used as early as the 17 th century BC in Mesopotamia to drive windmills [1]. Today wind turbines have been developed in Europe, Asia, America and are in use in a number of African countries as well [,3]. The power potential of wind is determined by its speed and the power varies as the cube of the wind speed. Wind is defined as the movement of air caused by pressure differences in the atmosphere as a result of temperature gradients. It is greatly affected by the local physical features like the landscape, obstacles such as buildings and vegetation cover. Accuracy in measuring wind speed is of fundamental importance in the assessment of wind power potential [4]. The computer software called the Wind atlas analysis and application program, WAsP, is the standard tool for wind energy assessment [5,6]. The program has been successfully used to predict wind energy resource for both offshore and onshore wind turbine candidate sites. In this work, WAsP was used to predict wind conditions and energy production of wind turbine installations at Lynch Knoll and Beaufort Court. The author developed wind climate models using wind data from meteorological stations near the sites. Details of the terrain topography were provided in the form of digitied contour maps of the turbine locations. There were no direct measurements at the sites and WAsP was used to predict turbine output using regional wind climate and topographic features only, with details on longitude, latitude and altitude specified. Specifications of the Lynch Knoll turbine were taken from Ecotricity [7] and those for Beaufort Court from Renewable Energy Systems Limited [8]. Prediction results from each site were retrieved from WAsP, analysed and compared with actual turbine output..0. Wind Atlas Methodology.1. Summary of the Wind atlas methodology The method employed by WAsP is called the Wind atlas methodology. Long-term wind speeds and directions from a reference site, usually a meteorological station, are used to create an observed wind climate (OWC) for the site. In its analysis mode, WAsP extrapolates the wind data in the OWC into a Weibull probability distribution and
removes the effects of local obstacles, topography and terrain roughness, to form a geostrophic wind climate (GWC) also known as the regional wind atlas of the area. The geostrophic wind is characteristic of the wind flow above the inner boundary layer over 100 m above ground level. In its application mode, WAsP extrapolates down the wind atlas data at the candidate site to include local terrain effects, creating an observed wind climate of the site [9]. WAsP requires the following conditions to be satisfied for accurate predictions: both the meteorological station and turbine sites must be subjected to the same weather regime reference data from the meteorological station must be reliable atmospheric conditions at the meteorological station and turbine site should be neutrally stable. the surrounding terrain at the meteorological station and turbine site must be sufficiently gentle to avoid flow separation topographic model inputs should be adequate and reliable... Topographic models Contour maps of the two turbine sites were digitied and used as input to describe the terrain features of the sites. The maps provide information on altitude, speed-up and turning effects, and the ruggedness index (RIX). Fig.1 shows the digitied maps with turbine locations (a) Lynch Knoll, (b) Beaufort Court
Wind speed profile is affected by the roughness of the landscape which is measured by the surface roughness length 0. Roughness length is the distance in metres above the ground at which wind speed is ero. The physical maps of Lynch Knoll and Beaufort Court have moderate vegetation cover which makes the areas to fall under surface roughness class 1 with a roughness length of 0.03 m [9]. Wind speed at any height is calculated using the expression where, u wind speed at height above ground level u ref wind speed at reference height (anemometer) height above ground level ref - anemometer height at reference site 0 roughness length. u u ln 0 ref, ref ln 0
Fig. shows the wind shear profile and the speeds at different heights above ground level..3. Data collection The wind atlas methodology requires that wind data be collected for a period not less than one year [10 ]. In this work hourly wind speeds and directions were provided from Lyneham and Heathrow meteorological stations. The nearest wind data source for Lynch Knoll was Lyneham meteorological station 9 km from Lynch Knoll and for Beaufort Court data was obtained from Heathrow meteorological station 10.8 km away. The meteorological data was used to create observed wind climates (OWC) at respective stations [11]. Monthly mean wind speeds for 004 are shown in table 1 below. Table 1. Monthly wind speeds at the meteorological stations measured at 10 metres above ground level. Month Lyneham, Heathrow U m/s U m/s Jan 9.5 9.3 Feb 9.4 8.7 Mar 8.8 81 Apr 8. 7.3 May 6.5 5.9 Jun 8.1 8. Jul 7.1 7.7 Aug 7.0 7.5 Sep 10.1 9.7 Oct 9.8 9.8 Nov 7.0 6.3
Dec 7.0 7.0 Annual Mean 8. 8.0 The time-series hourly wind speed and direction readings from each station were checked for instrument reading errors according to the validation process described in the Wind Resource Assessment Handbook (NREL 1997), [10] and then used to create OWC for each month. Fig.3 shows the OWCs for January 004. In figure 3, (a) and (c) are wind roses showing the percentage variation in wind direction during the month of January over Lyneham and Heathrow respectively. Diagrams (b) and (d) show the percentage frequency of wind speed distribution with a Weibull fit, for the month of January over Lyneham and Heathrow respectively. 3.0. Results and discussion 3.1. Wind speed and power predictions In its analysis mode, WAsP uses the data from meteorological stations to produce the observed wind climate at these stations. It then removes the local topographic effects to create a regional wind atlas for a wider area which covers the turbine sites. In its application mode, WAsP combines the topographic effects from the turbine sites with the regional wind atlas data to predict the wind climate (wind speed and direction) at the turbine sites. Rotor diameter of the installed wind turbine is used to calculate the power density. The predicted wind climate is applied to the wind turbine generator characteristics of the installed turbine to predict the annual energy production (AEP). The predicted results for Beaufort Court and Lynch Knoll turbines are shown in table below. Predictions for
Lynch Knoll are at 40.5 m above ground level and those for Beaufort Court at 3.5 m above ground level. These heights correspond to the hub heights of the installed turbines. Table. Wind speed, power density and annual energy production predictions Month Lynch Knoll Predictions (40.5 m a.g.l.) Beaufort Court Predictions (3.5 m a.g.l.) U, m/s P, W/m AEP MWh U, m/s P, W/m AEP, MWh Jan 11.5 16 40 11.3 190 1175 Feb 11.4 186 533 10.5 155 111 Mar 10.8 1657 373 10.4 1670 1064 Apr 10.1 114 360 8.9 856 979 May 8. 553 1708 7.4 394 747 Jun 10.0 16 44 9.8 105 1067 Jul 9. 1045 1950 9.4 915 1067 Aug 8.8 747 1957 9. 764 1083 Sep 1.1 171 973 11.6 1645 138 Oct 1.0 1775 793 11.9 1764 1334 Nov 8.7 779 184 79 549 814 Dec 9. 973 1968 8.5 87 90 Annual Mean 10. 198 57 9.7 1170 1058 3.1.1 Wind speed
Figure 4. Wind speed at Lynch Knoll compared to that at Lyneham Figure 5. Wind speed at Beaufort Court compared to that at Heathrow The WAsP model extrapolates wind speed from 10m at the meteorological station upwards to the hub height of the turbine. Vertical wind shear follows the 1/7 th power law represented by the following formula: 1 u uref 7 h, where href height respectively; h and h u and ref u ref are wind speeds at the hub height and meteorological anemometer are the hub height and anemometer height respectively. Figures 4 and 5 above indicate similar variations in wind speed at each turbine site and its corresponding meteorological station. This satisfies WAsP s requirement for a similar weather regime at the turbine site and reference site. 3.1.. Power density Power density is the of power produced per unit area of turbine rotor swept area and is equal to 4 d P w/m, where P is power in watts and d is the turbine rotor diameter. The graphs in figures 6 and 7 below show the
predicted power density at the turbines and the power density that would be expected if the turbines were installed at the meteorological stations with hub heights at 10 metres above ground level. Increase in power output with height is evident from the graphs. Figure 7. Power density at Lynch Knoll compared to that at Lyneham. Figure 8. Power density at Beaufort Court compared to that at Heathrow 3.1.3. Annual energy production (AEP) Annual energy production is the energy in mega-watt-hours (MWh) produced per year for the total number of hours that the wind turbine was operational. In this study, WAsP predicted an annual energy production of 57 MWh and 1057 MWh for Lynch Knoll and Beaufort Court turbines respectively assuming 8 760 production hours in 365 days of the year. The prediction figures are higher than the actual production for the following reasons: availability, the turbines may have not operated for all the 8 760 hours of the year due to maintenance time or wind speed being below cut-in speed or higher than cut-out speed according to Bet law, only less than 59% of the kinetic energy in the wind can be converted to mechanical energy using a wind turbine as shown by the power ratio; power extracted from the wind 0.5m( v 1 v ) P turbine power in the undisturbed wind P wind 0.5 Av 3 1
P P turbine power ratio 0.5 1 1 0. 59. wind In the above formulae, m and v v 1 v v 1 are the mass and density respectively, of the air flowing across the turbine rotor swept area A; v 1 and v is the wind speed before and after the turbine rotor respectively. In order to compare the predictions with the actual production at the wind turbines, predicted AEP has to be reduced to at least 59% to get practical mechanical energy conversion and then further reduced by the electrical efficiency of the turbine system. The maximum practical mechanical energy conversion is 40% and a mechanical to electrical efficiency of 95% giving an overall maximum efficiency of 38% [1]. Using the maximum overall efficiency the annual energy production would be 857.7 MWh and 40 MWh for Lynch Knoll turbine and Beaufort turbine respectively. According to [13], a complete wind energy system, including rotor, transmission, generator, storage and other devices will (depending on the model) deliver up to 30% of the original energy available in the wind. This gives a maximum energy production of 677 MWh and 317 MWh for Lynch Knoll turbine and Beaufort turbine respectively. Actual annual energy production for Beaufort Court for 004 was 0.15 MWh with 600 hours of unavailability [8], giving an over prediction of 46%. High uncertainty arises from the absence of actual performance characteristics of the installed turbine and on site wind speed readings. The actual energy production for Lynch Knoll was not available. 4.0. Conclusion Wind energy resource assessment was conducted using the WAsP model for the turbine sites in Lynch Knoll and Beaufort Court using wind data from the United Kingdom meteorological office. Wind regimes at the turbine sites were found to be similar to those at the meteorological stations as is required in WAsP modelling. Power density and annual energy production were shown to increase with increase in mean wind speed, which in turn increases with altitude. Lack of site wind speed and surface roughness measurements at the turbine sites and information on mechanical efficiency of the turbines increased the uncertainties of the predictions. It is recommended that on-site measurements be carried out especially for a wind turbine candidate site in order to narrow prediction errors. WAsP modelling can be used successfully for wind energy resource assessment of any candidate site provided adequate data is given for analysis and application by the model. References *1+. Freris L.L., Wind Energy Conversion Systems, Prentice Hall, London, 1990. *+. Omer A.M., (1999), Wind energy in Sudan, Renewable Energy, vol.19, 000 pp.399 411. *3+. Gourieres, D., Wind Power Plants, Theory and Design, Pergamon Press, Oxford, 198.
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