Diese Präsentation wurde erfolgreich gemeldet.
Die SlideShare-Präsentation wird heruntergeladen. ×

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

Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Wird geladen in …3
×

Hier ansehen

1 von 24 Anzeige
Anzeige

Weitere Verwandte Inhalte

Ähnlich wie The benefits of forecasting icing on wind energy produc- tion Øyvind Byrkjedal, Kjeller Vindteknikk (20)

Anzeige

Aktuellste (20)

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

  1. 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. 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 2
  3. 3. Mesoscale wind index 2000 3
  4. 4. Mesoscale wind index 2001 4
  5. 5. Mesoscale wind index 2002 5
  6. 6. Mesoscale wind index 2003 6
  7. 7. Mesoscale wind index 2004 7
  8. 8. Mesoscale wind index 2005 8
  9. 9. Mesoscale wind index 2006 9
  10. 10. Mesoscale wind index 2007 10
  11. 11. Mesoscale wind index 2008 11
  12. 12. Mesoscale wind index 2009 12
  13. 13. Mesoscale wind index 2010 13
  14. 14. Mesoscale wind index 2011 14
  15. 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. 16. Power production index %] ndex [% Power in 16
  17. 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. 17
  18. 18. Power production index %] ndex [% Power in The winter 2010/2011: Large ice loads and large production losses 18
  19. 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. 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. 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. 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. 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 23
  24. 24. Thank you! Wind and icing map are now available for Sweden: www.vindteknikk.no 24

×