Germany, like many countries, has plans for a massive expansion in wind energy, in particular into inland areas. But how can planners, operators and investors identify suitable wind farm sites and reliably predict yields and break-even periods? On behalf of Baden-Wuerttemberg’s State Ministry of the Economy, TàƒÅ“V SàƒÅ“D has developed a wind atlas for identifying ideal wind sites.
Peter-Herbert Meier, TàƒÅ“V SàƒÅ“D Industrie Service, GmbH, Germany
A current study carried out by the Fraunhofer Institute proves that sites suitable for harnessing wind power exist not only in the northern part of Germany, but across the entire country. According to this study, allocating only 2 per cent of its total land area for onshore wind power use would cover 65 per cent of Germany’s electricity demand. The European Wind Energy Association (EWEA) also assumes that by 2020 onshore wind power will provide the majority of energy from renewable sources in Europe and continue to be cost-effective.
To realize these goals, we not only need to engage in further offshore and repowering projects but also develop new inland wind sites. In this context, an attempt must be made to identify sites that offer high wind speeds and environmentally compatible yet profitable realization.
Practice has shown that standardized and high-quality systematic analyses of wind speeds are lacking for many sites. However, these data are important tools, helping regional associations to identify new highly suitable wind farm sites. The results of these analyses can further be used to address concerns among investors and operators on the suitable siting of future wind farms.
Against this backdrop, Baden-Wuerttemberg’s State Ministry of the Economy commissioned TàƒÅ“V SàƒÅ“D’s experts to draw up a wind atlas. In March this year, the environmental engineers presented their results after a project term of only five and a half months. Offering a resolution of 50 x 50 metres for high-yield sites and wind speeds calculated at 80, 100, 120, 140 and 160 metres of height, the wind atlas takes the quality of wind farm site data to new levels. So far, Baden-Wuerttemberg is the first German state that has mapped its wind data to this level of detail, facilitating the identification of profitable wind sites.
IMPORTANCE OF COLLABORATION
Close collaboration with regional operators, turbine manufacturers, associations and ministries is important to ensure premium data quality. Within the scope of this cooperation, in the run-up to the project experts must clarify which data will be required and who can supply them. The wind atlas in this case study was to be based on a larger data set than previous calculations.
Landscape models, topographical models and maps are some of the methods used for modelling the topography of the land. The higher the data density, the more precise the calculations.
For the wind atlas of Baden-Wuerttemberg, the State Institute of the Environment, Measurements and Nature Conservation supplied digital landscape and topography models, topographical maps and information about district boundaries. Using these data, the TàƒÅ“V SàƒÅ“D experts drew up particularly detailed models of both the roughness of the terrain surface and its relief structure.
Generally, meteorological measurement stations have long-term meteorological data that provide valuable information for wind mapping. State institutes, independent organizations and private companies can also provide wind measurement data. The context in which these data were originally collected is of minor importance. The data are used as input data for calculations used in wind mapping.
Wind turbine operators can supply statistics of the energy yield of existing wind turbines, which provide an important data set for wind mapping. These statistics can be used firstly to validate the data determined for the wind atlas by recalculating the model applied to the existing wind sites. Secondly, these statistics, if offering adequate data density, may also be used for generating additional input data.
For the Baden-Wuerttemberg wind atlas, the TàƒÅ“V SàƒÅ“D experts sent an enquiry form to wind farm operators in consultation with the German Wind Energy Association (Bundesverband fàƒ¼r Windenergie, BWE). The wind-farm operators not only supplied wind statistics, but also the type and power curve of their turbines, the site co-ordinates, monthly energy yields and availability, as well as time resolved data on wind speed, wind direction and output.
On-site assessment enables experts to verify the available information about a turbine or wind farm and to review the data for their suitability to be used for validation purposes. To do so, the experts use photographic documentation and detailed descriptions of the environment, identify site co-ordinates by means of GPS, and document available measurement systems.
EFFICIENT TWO-PHASE MODELLING
Wind mapping of inland areas involves special challenges. In contrast with coastal areas, relief structures strongly influence the near-ground wind field, rendering the spatial modelling of onshore wind speeds more complex.
TàƒÅ“V SàƒÅ“D’s wind professionals calculated the wind atlas for Baden-Wuerttemberg in a two-phase approach. In phase 1 they prepared a wind map with a resolution of 250 x 250 metres at a height of 100 metres. This map was designed to identify areas exceeding the wind-speed threshold of 5.3 m/s. At a later stage, the experts then intended to filter out the potential high-yield sites with average wind speeds of over 6 m/s. In phase 2, these high wind areas were then calculated in detail to a more precise resolution of 50 x 50 metres and heights of up to 160 metres. The experts also used different software models in these two phases. Figure 1 gives an example of the differences between phases 1 and 2.
The wind atlas demonstrates that Baden-Wuerttemberg offers wind speeds of over 5.3 m/s at a height of 140 metres over an area of 18,050 mà‚², which equates to 50 per cent of the entire area of this German state. A comparison of the results at different heights reveals that as little as 40 metres in extra altitude doubles the number of sites suitable for harnessing wind power. Baden-Wuerttemberg plans to build approximately 150 wind turbines, providing a total capacity of 350 MW, by 2020. This expansion is necessary to increase annual wind power capacity from 0.6 TWh in 2009 to at least 1.2 TWh in 2020, as planned by the state’s energy scheme for 2020.
|Figure 1: A comparison of the Batten-Wuerttemberg maps created in phase 1 and phase 2|
HARNESSING WIND IN FOREST AREAS
Forest areas are of particular significance when it comes to wind atlas accuracy and informative value. As forests are complex and inhomogeneous, they cause massive swirling winds and turbulences, resulting in changes to wind speeds that cannot be captured by the mathematical models used for the preparation of the wind atlas. These areas require a site-specific analysis. Generally a comprehensive wind map also cannot replace site-specific accredited wind expert reports.
Forest areas also play a special role in the general context of energy politics. Commercial forests characterized by monoculture forestry are particularly interesting. Within the scope of option contracts, experts are currently assessing around 100 locations on land owned by the Bavarian State Forest Enterprise (Bayerischen Staatsforsten) for their suitability as wind farm sites.
This intense interest can be attributed to the fact that state-of-the-art turbines have paved the way for profitable options. Modern turbines, reaching the high-wind but low-turbulence layers of air high above the tree crowns, enable wind farms located on inland sites to generate yields which previously were only possible in coastal regions.
High-wind regions in commercial forests are also often far from housing estates, keeping problems for nearby residents caused by noise and shadow-flicker to a minimum. Another advantage is that these sites offer an existing network of forestry roads that often can be used for site development. Last but not least, these decentralized locations involve low connection costs to the regional grid and take some pressure off the urgency of expanding and extending cross-regional grids.
One example of a successful forest wind farm project can be found in the Upper Franconian region of Bavaria, in Gattendorf and Regnitzlosau near Hof. The Fasanerie wind farm launched operations in late 2010. For more specific information on this project please see the April 2011 issue of Power Engineering International magazine or go online (powerengineeringint.com).
|Table 1: The annual power yields and renumeration from two fictious wind farm sites|
RELIABLE ENERGY YIELD ASSESSMENTS
Systematic wind mapping may supply regions with new economic impetus and provide regional associations with a standardised planning basis to make full use of existing wind potential. However, the suitability of a location cannot be determined by wind speed alone.
A wind atlas provides information about the mean annual wind speeds at a certain site. But the mean value does not include any information about the frequency of different wind speeds ” and this frequency distribution provides key information for assessing the profitability of a site.
Energy yield increases proportionally to the third power of wind speed. As a consequence of this, a minor rise in wind speed may bring a major increase in yield. The frequency distribution of wind speeds illustrates this correlation mathematically, making a vital contribution to an accurate prediction of energy yield.
The ‘shape parameter’ of frequency distribution is of critical importance in this context. For instance, if at one site the wind blows at a constant speed of 5 m/s, while at another site the wind blows at 0 m/s and at 10 m/s, each for 50 per cent of the year, both sites will have a mean wind speed of 5 m/s. Yet the wind turbine at the second site would deliver a much higher energy yield. The shape parameter is used to express this difference.
To calculate the annual energy yield, the experts need a ‘power curve’. The power curve is turbine-specific, plotting the energy output of a turbine in relation to the wind speed. The power curve can either be calculated theoretically or measured in accordance with the IEC 61400-12 standard. In general, a power curve based on measurements is preferred as calculated power curves do not provide any information about the turbine’s real performance in operation.
Key factors influencing the power curve include the rotor diameter and the efficiency of the wind turbine. The larger the rotor diameter and the greater the efficiency, the higher the energy output delivered by the wind turbine. Rotor diameters of more than 80 metres are already common and multi-megawatt turbines with rotor diameters of more than 100 metres are now redefining the state of the art.
|Table 2: Comparison of the break-even time of two fictious wind farm sites|
Hub heights of about 100 metres dominated the market for almost a decade. But for some years manufacturers have also been offering turbines with hub heights of up to 160 metres. Depending on the site and framework conditions, the assessment of break-even time and profitability should take into account the additional costs of greater hub heights compared on a case-by-case basis with the higher yields that can be achieved.
For many locations, the wind speeds defined in the wind atlas offer a sufficient level of accuracy. However, in more complex areas including forest sites, local effects may require on-site assessment of the free-stream conditions by a wind expert, which permits highly precise correction of wind speed. In addition, third-party expert opinions provide the cornerstones of the safety concept and are also critical for lending.
In accordance with the IEC 61400-12 standard, the power curve must be adjusted for mean annual temperature and atmospheric pressure. Both variables are location-specific, depending in particular on altitude. By way of approximation, the density of air may be used as a substitute as it is directly proportionate to wind power.
Table 1 provides an example of yield prediction for two sites. For the power curve, we assume a commercial turbine of the 2 MW-type. Financial remuneration is governed by the EEG and currently amounts to €0.0902/kWh (as of February 2011). The sample calculation shows how annual yield is calculated from wind speed, topography, power curve, hub height, location and altitude.
In assessing the profitability of a site, the income generated from feed-in tariffs must be compared with expenses. The total cost of ownership of a wind turbine comprises the purchase price of the turbine, the costs of planning, expert reports and management, the expenses incurred for the foundation, cable installation, grid connection and development of the site and the costs of wind-turbine operation, maintenance and the site.
The total cost of ownership varies from site to site. Site development in lowland areas, for example, is less expensive than in low mountain ranges. Nearby grid connections and favourable soil conditions also may save costs. Many individual cost items also decrease when an entire wind farm rather than a single wind turbine is set up. This applies particularly to development costs.
Starting from the annual yields achieved at the three sample sites, Table 2 shows the different break-even times. In the first ten years, the operating costs of a wind turbine in the 2 MW category add up to approximately 1.5 per cent of the total annual investment costs. In the second decade, the operating costs rise to around 2.5 per cent. Another aspect that must be taken into account is that the EEG provides for higher initial remuneration. The length of time for which these higher tariffs are paid depends on the achievement of the reference value.
Third-party due diligence then offers the possibility of determining whether a project is profitable and, if so, when. Wind farm due diligence assesses the economic framework conditions and calculates the costs of wind-farm operation and the wind-farm layout required for maximum yield. The wind experts also review pollution forecasts, approvals and permits, contracts and supply commitments.
An increasing number of German states are opting to produce a comprehensive inventory of their potential wind-power areas. Systematic wind mapping offers a standardized data foundation for regional planning, taking the identification of areas that are particularly suitable for wind-farm projects to a new level. At the same time, it supports investors, planners and operators in identifying attractive sites and estimating break-even times.
The wind atlas for the German state of Baden-Wuerttemberg, drawn up by TàƒÅ“V SàƒÅ“D, is available as a free download at www.windatlas-bw.de.