The benefits of forecasting icing on wind energy produc- tion Øyvind Byrkjedal, Kjeller Vindteknikk
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Transcript of The benefits of forecasting icing on wind energy produc- tion Øyvind Byrkjedal, Kjeller Vindteknikk
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Mapping of icing in SwedenThe benefits of forecasting icing for
energy productiongy pØyvind Byrkjedal
Kjeller [email protected]
Wintewind 2012, Skellefteå 07.02.2012
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Mesoscale modelMesoscale 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 resolved by the model. The model resolves low-pressure systems and fronts.
WRF – Weather Research and ForecastingTh d l d ib h h d i ( i d The model describes the atmosphere dynamics (wind, temperature 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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Mesoscale windindex
2000
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Mesoscale windindex
2001
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Mesoscale windindex
2002
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Mesoscale windindex
2003
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Mesoscale windindex
2004
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Mesoscale windindex
2005
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Mesoscale windindex
2006
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Mesoscale windindex
2007
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Mesoscale windindex
2008
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Mesoscale windindex
2009
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Mesoscale windindex
2010
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Mesoscale windindex
2011
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Calculation of in-cloud icingCalculation of in-cloud icing We calculate icing on a standard body following
Makkonen 1m cylinder with diameter 30mm:Makkonen. 1m cylinder with diameter 30mm:
VAdMlli i ffi i f(Vd D)VAw
dt 321 α1- collision efficiency, α1=f(V,d,D)
α2- sticking efficiency, α2 ≈ 1 α3- accretion efficiency, α3= f(V,d,w,T,e,D,α1)w – cloud liquid water contentA – collision area, perpendicular to flowV – Wind speed
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Power production indexPower production index
%]
ndex
[%ow
er in
16
P
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Power production indexPower production index
%]
ndex
[%ow
er in
The winter 2008/2009:
17
P 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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Power production indexPower production index
%]
ndex
[%ow
er in
The winter 2010/2011:
18
P Large ice loads and large production losses
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Productionforecasts
ncy
The analysis is based on 3 yearsof operational data from a windfarm in Sweden fre
quen
lativ
e f
Re
19Forecast error [%]
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Productionforecasts
ncy
frequ
enThe analysis is based on 3 yearsof operational data from a windfarm in Sweden
lativ
e f
The overall forecast errors
Reare reduced by incluing
forecasts of ice
20Forecast error [%]
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Productionforecasts
ncy
frequ
enThe analysis is based on 3 yearsof operational data from a windfarm in Sweden
lativ
e f
With the current model we
Reparticulary reduce the
number of cases when theproduction is overpredicted
21Forecast error [%]
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Productionforecasts
ncy
frequ
enThe analysis is based on 3 yearsof operational data from a windfarm in Sweden
lativ
e f
We get an increase of the
Renumber of cases when we
underpredict production
22Forecast error [%]
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ConclusionsConclusions For a large number of sites the variability in icing from
year to year will dominate over the variability in windin the production index.
An improvement in the energy production forecastscan be achieved by including the effects of icingcan be achieved by including the effects of icing
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Thank you!Thank you!Wind and icing map are now available for Sweden:
www.vindteknikk.no
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