1.
Mapping of icing in Sweden
The benefits of forecasting icing for
energy production
gy p
Øyvind Byrkjedal
Kjeller Vindteknikk
oyvind.byrkjedal@vindteknikk.no
Wintewind 2012, Skellefteå 07.02.2012

2.
Mesoscale model
 The meso-scale refers to the time and spatial scale of the features
resolved by the model. The model resolves low pressure systems and
model low-pressure
fronts.
 WRF – Weather Research and Forecasting
 The
Th model d
d l describes the atmosphere d
ib h h dynamics ( i d temperature
i (wind,
and humidity), and includes physical description of radiation, cloud
formation, precipitation, snow, surface processes, etc.
 The model performes calculations in the time domain, no steady-
state model
 Typical model resolution down to 1km x 1km
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3.
Mesoscale wind
index
2000
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4.
Mesoscale wind
index
2001
4

5.
Mesoscale wind
index
2002
5

6.
Mesoscale wind
index
2003
6

7.
Mesoscale wind
index
2004
7

8.
Mesoscale wind
index
2005
8

9.
Mesoscale wind
index
2006
9

10.
Mesoscale wind
index
2007
10

11.
Mesoscale wind
index
2008
11

12.
Mesoscale wind
index
2009
12

13.
Mesoscale wind
index
2010
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14.
Mesoscale wind
index
2011
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15.
Calculation of in-cloud icing
 We calculate icing on a standard body following
Makkonen.
Makkonen 1m cylinder with diameter 30mm:
dM
  1 2 3  w  A  V α1- collision efficiency, α1=f(V,d,D)
lli i ffi i f(V d D)
dt α2- sticking efficiency, α2 ≈ 1
α3- accretion efficiency, α3=
f(V,d,w,T,e,D,α1)
w – cloud liquid water content
A – collision area, perpendicular to flow
V – Wind speed

16.
Power production index
%]
ndex [%
Power in
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17.
Power production index
%]
ndex [%
Power in
The winter 2008/2009:
According to the wind index we would expect more production than normal.
Due to icing we would find less production than normal.
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18.
Power production index
%]
ndex [%
Power in
The winter 2010/2011:
Large ice loads and large production losses
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19.
Production
forecasts
ncy
Relative frequen
The analysis is based on 3 years
of operational data from a wind
farm in Sweden
f
Forecast error [%] 19

20.
Production
forecasts
ncy
Relative frequen
The analysis is based on 3 years
of operational data from a wind
farm in Sweden
f
The overall forecast errors
are reduced by incluing
forecasts of ice
Forecast error [%] 20

21.
Production
forecasts
ncy
Relative frequen
The analysis is based on 3 years
of operational data from a wind
farm in Sweden
f
With the current model we
particulary reduce the
number of cases when the
production is overpredicted
Forecast error [%] 21

22.
Production
forecasts
ncy
Relative frequen
The analysis is based on 3 years
of operational data from a wind
farm in Sweden
f
We get an increase of the
number of cases when we
underpredict production
Forecast error [%] 22

23.
Conclusions
 For a large number of sites the variability in icing from
year to year will dominate over the variability in wind
in the production index.
 An improvement in the energy production forecasts
can be achieved by including the effects of icing
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24.
Thank you!
Wind and icing map are now available for Sweden:
www.vindteknikk.no
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