Offshore Electricity Infrastructure in Europe - European Wind Energy ...
Offshore Electricity Infrastructure in Europe - European Wind Energy ...
Offshore Electricity Infrastructure in Europe - European Wind Energy ...
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Annex d – details of results<br />
FiGuRe 13.4: duRatiOn cuRves FOR the ‘w<strong>in</strong>d yeaR 2030’, FOR a s<strong>in</strong>Gle OFFshORe tuRb<strong>in</strong>e <strong>in</strong> FROnt OF the belGium<br />
cOast, FOR the sum OF all expected On&OFFshORe w<strong>in</strong>d FaRms <strong>in</strong> belGium <strong>in</strong> 2030 and FOR the sum OF all expected<br />
On&OFFshORe w<strong>in</strong>d FaRms <strong>in</strong> euROpe <strong>in</strong> 2030<br />
Power<br />
(% of <strong>in</strong>st. capacity)<br />
10000<br />
9000<br />
8000<br />
7000<br />
6000<br />
5000<br />
4000<br />
3000<br />
2000<br />
1000<br />
0<br />
0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000<br />
The larger the grid and the larger its transmission capacity,<br />
the larger the smooth<strong>in</strong>g effect will be of w<strong>in</strong>d<br />
energy. This effect of course also holds for aggregation<br />
of demand or other weather or resource dependent<br />
generation sources.<br />
D.I.II Visualis<strong>in</strong>g spatial power<br />
correlations: von-Bremen-Maps 63<br />
A new way of identify<strong>in</strong>g spatial correlations of<br />
w<strong>in</strong>d power production is shown <strong>in</strong> Figure 13.5 and<br />
Figure 13.6. The simple idea is to calculate the correlation<br />
between a time series of a certa<strong>in</strong> local variable,<br />
like local w<strong>in</strong>d speed at a specific po<strong>in</strong>t <strong>in</strong> space, to a<br />
prescribed reference time series which is fixed. Then,<br />
all these correlations for a high number of geographical<br />
po<strong>in</strong>ts, preferably for a high density of grid po<strong>in</strong>ts,<br />
are plotted as a map. In this way, the map shows the<br />
spatial characteristics of a specific correlation.<br />
The von-Bremen-map <strong>in</strong> Figure 68 shows the spatial<br />
correlation of local w<strong>in</strong>d power time series and the<br />
German W<strong>in</strong>d Power Generation <strong>in</strong> spr<strong>in</strong>g 2007.<br />
Figure 13.5 shows that the area which shows the highest<br />
correlation with the aggregated sum of the German<br />
Time (hours)<br />
S<strong>in</strong>gle <strong>Offshore</strong> Turb<strong>in</strong>e Total Belgium On&<strong>Offshore</strong> W<strong>in</strong>d Total EU On&<strong>Offshore</strong> W<strong>in</strong>d<br />
63 Developed by Dr. Lueder von Bremen (ForW<strong>in</strong>d) <strong>in</strong> September 2010.<br />
FiGuRe 13.5: vOn-bRemen-map shOw<strong>in</strong>G spatial<br />
cORRelatiOn between lOcal w<strong>in</strong>d pOweR time seRies<br />
and the measuRed GeRman w<strong>in</strong>d pOweR GeneRatiOn time<br />
seRies <strong>in</strong> spR<strong>in</strong>G 2007<br />
w<strong>in</strong>d power generation is the region between Bremen,<br />
Hamburg and Hannover. This plausible result is due to<br />
the simple fact that the major shares of German w<strong>in</strong>d<br />
power capacity are <strong>in</strong>stalled <strong>in</strong> the northern part of the<br />
country and the high density of turb<strong>in</strong>es obviously results<br />
<strong>in</strong> a high correlation. The more <strong>in</strong>terest<strong>in</strong>g result<br />
is that the correlation shows a clear west-east pattern:<br />
The map reflects that the w<strong>in</strong>d power production<br />
is strongly correlated from West to East (e.g. England,<br />
BeNeLux, Germany, Denmark and Poland), but less<br />
130 <strong>Offshore</strong>Grid – F<strong>in</strong>al Report