The benefits of forecasting icing on wind energy produc- tion Øyvind Byrkjedal, Kjeller Vindteknikk

Mapping of icing in SwedenThe benefits of forecasting icing for

energy productiongy pØyvind Byrkjedal

Kjeller [email protected]

Wintewind 2012, Skellefteå 07.02.2012

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

2

Mesoscale windindex

2000

3

Mesoscale windindex

2001

4

Mesoscale windindex

2002

5

Mesoscale windindex

2003

6

Mesoscale windindex

2004

7

Mesoscale windindex

2005

8

Mesoscale windindex

2006

9

Mesoscale windindex

2007

10

Mesoscale windindex

2008

11

Mesoscale windindex

2009

12

Mesoscale windindex

2010

13

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

Power production indexPower production index

%]

ndex

[%ow

er in

16

P

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.

Power production indexPower production index

%]

ndex

[%ow

er in

The winter 2010/2011:

18

P Large ice loads and large production losses

Productionforecasts

ncy

The analysis is based on 3 yearsof operational data from a windfarm in Sweden fre

quen

lativ

e f

Re

19Forecast error [%]

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 [%]

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 [%]

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 [%]

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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