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Yield Index

 

The most important external influencing factor for wind turbines is wind speed. It varies constantly and is therefore one of the central technical and economic uncertainties in the use of wind energy. Apart from short-term fluctuations, there are also significant deviations of the annual values from the long-term mean values. Geographically and topographically induced regional differences also occur. The classification of the individual years into the longer-term course of wind conditions is thus inevitable. A yield index is suitable for this purpose in which the wind conditions are linked with the technical data of a wind turbine and the differences between the wind years become tangible. The following figure shows the 2018 yield for Europe in relation to the 20-year average on the basis of the yield calculation for an exemplary Vestas V90 wind turbine with a hub height of 100 metres and a rated output of 2 MW. The 2018 yield index shows slightly to significantly below-average conditions for almost the whole of Europe. Only small parts of France, Spain, Italy and Great Britain show slightly above-average conditions.

 

 

 

 

 

Wind resources in 2018 (20x20 km resolution) for Europe in relation to 20-year average based on MERRA-2-Data

Data source: [anemos GmbH]

 

 

The next figure shows an earnings index with a higher resolution for Germany. In most parts of Germany, the earnings index in 2018 was below 95 percent of the long-term average. Only in small parts of the southern federal states can above-average conditions be recorded. In particular, good yields were achieved in eastern Bavaria and the Alpine region. According to the yield index, 2018 can be classified as a year with weak winds and also as a year with weaker winds than 2017. While in 2017 the North and East German states in particular were able to benefit from good wind conditions, in 2018 it looks sobering, especially in the North.

 

 

 

 

 

Wind resources in 2018 (3x3 km resolution) for Germany in relation to 20-year average based on MERRA-2-Data

Data source: [anemos GmbH]