Wind Resource Estimation Using a Fine-Scale Weather Model
often not available, which is part of the reason for producing the hindcast in the first place; but in some cases there may be meteorological stations or other wind farm projects from which measurements can be obtained.
At our company we have developed a method that adjusts the hindcasted winds at any point in the hindcast domain based on the degree of match between the measured and hindcasted values at the measurement points. The true terrain height and the height of
hindcast are valid in the long term. For this, long-term series from ordinary meteorological stations could be used; however, in remote mountainous regions the nearest conventional station may be located 100km or more away, and often in a totally different wind climate than at the site being studied. Using data from these stations for wind resource assessment could lead to very uncertain or unrealistic results.
Another option is to make use of larger-scale gridded data that cover a long time period. In 2009, the ECMWF released a new data set, the so-called ‘ERA Interim archive’.3
This dataset spans more
Mesoscale modelling is gradually gaining acceptance in the industry as a useful and necessary tool for wind resource estimation in complex terrain.
the hindcast model terrain go into the adjustment formula. Applying this correction to the hindcasted winds can reduce the error levels by 50% in complex mountainous areas.
Figure 3 shows results from a comparison between measured and hindcasted wind speeds at a 50m tower in a mountainous area in southwest Norway. The data period is September 2007, a month with variable and at times strong wind. The comparison was made after the adjustment to measurements had been performed. Data from five measuring masts in the domain were used to set up the adjustment model. Both the time series plot and the wind distributions and wind roses show a very good match between hindcasts and observations. The average wind speed error of this sample was 0.5 metres per second.
Table 1 shows results of the verification of a hindcast for a mountainous area in western Norway, in fact the most challenging area that we have worked with so far. The hindcasts have been compared with measurements from five 50m masts after performing the adjustment based on measurements from the same masts. The verification period is 12 months and the time resolution is one hour. It is seen that the error level varies somewhat from one station to the other, but generally a good match is found, with error levels less than half a metre per second.
Correcting for the Long Term
As mentioned earlier, the typical time period covered by a hindcast – 12–24 months – is short compared with the lifetime of a wind farm. The main reason is the heavy computational resources that are required to run the hindcast model.
This means that, just as with ordinary measurement campaigns, an effort has to be made to ensure that the estimates based on a
1.
www.ecmwf.int 2.
www.wrf-model.org
than 20 years and covers the whole globe in a resolution of about 70km. This is still a coarse resolution, but much better than has previously been available. Our tests have shown that in mountainous regions in Scandinavia, interpolated wind data from the ERA archive generally match the wind speeds measured at wind farm locations much better than data from the nearest conventional stations. Consequently, we now use ERA Interim data extensively for long-term correction of our hindcast data, using advanced statistical methods to obtain a time series of more than 20 years’ length that is representative of the location being studied. This approach has proved highly successful.
The Future of Wind Resource Estimation
Mesoscale modelling is opening new paths in the field of wind resource estimation. Mesoscale models are able to resolve the variations over a region of complex terrain, and even across the area
Wind measurement campaigns are costly and take time, but thanks to the hindcasts the developer can make sure that the time and money invested in the campaign is not wasted.
of a proposed wind farm, in ways that have hitherto not been possible. In early project phases when no local measurements are available, mesoscale modelling provides a solid background for deciding whether to invest further in a project.
The calculation methods and analysis tools for resource estimation based on mesoscale modelling are under rapid development, as is the availability of computer resources. Over the next few years, mesoscale methods will continue to make resource estimation ever more detailed and accurate, thereby reducing the risks of wind farm developers, wind farm owners and financial institutions. We are convinced that the future of wind resource estimation lies in the weather models, and that they will play a key role in the exploitation of the good wind resources in mountainous regions and along the coast. n
3.
http://www.ecmwf.int/research/era/do/get/era-interim
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MODERN ENERGY REVIEW – VOLUME 2 ISSUE 2
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