Forecasting of icing for wind power applications. Øyvind Byrkjedal, Johan Hansson and Henrik van der Velde

Transcription

1 Forecasting of icing for wind power applications Øyvind Byrkjedal, Johan Hansson and Henrik van der Velde EWEA Wind Power Forecasting, Leuven, Belgium, 1-2 October 2015

2 Outline Icing conditions Influence on wind energy Forecasting of icing conditions Validation of icing periods Validation of the onset of icing periods Forecasting of energy loss 2

3 Icing conditions Temperatures below freezing cloud or fog containing small water droplets Something to freeze to in-cloud icing Ålvikfjellet, 420 kv Sima-Samnanger, January 2014 photo: Ole Gustav Berg, Statnett 3

4 How does icing influence wind energy production? May 2010 Nov 2009 Photo: Finish Meteorological Institute 4

5 Forecasting of icing - motivation The aim is to know when icing will occur: Power trading Blade heating systems: Start the heating before icing starts Avoid unnecessary stops during heating Risks of ice throw / ice fall Planning of maintainance Public safety Monitoring of exposed power lines Avoid damages 5

6 Operational forecasting WRF simulations at 4km x 4km resolution 4 times daily GFS 48 hour forecasts 6

7 Calculation of in-cloud icing Forecast parameters: Icing intensity (dm/dt) Ice loads Ice shedding episodes Wind energy dm dt w w liquid water content V wind speed A - collision area a 1, a 2, a 3 - coefficients A V According to ISO12494

8 Forecasting icing intensity dec :00 14:00 17:00 20:00 00:00 04:00 08:00 8

9 Validation of icing forecasts 9

10 Icing definitions Meteorological icing: When the meteorlogical conditons causes buildup of ice to occur Instrumental icing: The period of time when instruments (or wind turbines) are influenced by icing Source: IEA wind task 19 Wind Energy in Cold Climates 10

11 Identification of icing from SCADA data Icing flagged for each turbine (T4, T5, T6, T7) and for the model: Green: normal operation Blue: icing identified Red: Turbine alarm Yellow: Curtailed production 11

12 Validation of instrumental icing periods The periods with observed instrumental icing compared to modelled periods with instrumental icing for 4 wind power sites in Sweden: Site A, B, C, D Differences in ice shedding from model and observations A B C D Ratio of time when ice is detected 22 % 9 % 10 % 13 % Probability of detection 74 % 82 % 79 % 63 % Probability of false alarm 6 % 7 % 6 % 5% 12

13 Validation of meteorological icing - Timing 70 % of the observed icing episodes starts when the model indicates meteorological icing modeled icing finished before observed icing starts modeled icing starts after observed icing has started A B C D Number of icing episodes Probability of detection 67 % 70 % 71 % 70 % 13

14 Energy forecasts for wind power 14

15 Power [% of rated] IceLoss - Forecasting of power losses May 2010 Nov

16 Forecasting of power production Bias and mean absolute error (MAE) in the forecasts are reduced when we apply production losses due to icing according to the IceLoss function: Evaluation period: October 2013-march 2014 Case: One wind farm in northern Sweden 16

17 Forecasting of power Reduced number of cases with overprediction of power production in the forcast with icing Higher number of cases with error less than +/-12.5 % in the forecast with icing Higher number of cases with underprediction of the power production in the forecast with icing 17

18 Summary We carry out forecasting of icing and energy production with the WRF model running operationally Timing of icing periods are well modelled The IceLoss model improves the energy forecasts Future work: More realistic energy forecast by calulating icing on the turbine blades Validation of liquid water content (LWC) from the model Continuous work on the modelling of ice accretion in the projects FRonTLINES and WISLINE funded by the Norwegian Research Council and Statnett. 18

19 Thank you for your attention! Øyvind Byrkjedal THE WORK HAS BEEN SUPPORTED BY THE PROJECT LARGE SCALE, COST EFFECTIVE WIND ENERGY DEVELOPMENT IN ICING ENVIRONMENTS WHICH IS FINANCED THROUGH THE SWEDISH ENERGY AGENCY AND BY THE TOP-LEVEL RESEARCH INITIATIVE (TFI) PROJECT, IMPROVED FORECAST OF WIND, WAVES AND ICING (ICEWIND).

20 Extra slides for Q&A session 20

21 height Icing conditions Temperatures below freezing cloud or fog containing small water droplets Something to freeze to in-cloud icing Lifting of airmasses condensation wind west east 21

22 Identification of icing from SCADA data Data available form four wind farms: Power Nacelle wind speed Nacelle wind direction Temperature Operational state 10 minute frequency More than 2 years of data from each wind farm P low Identification of icing Davis et al. (2015) P10 treshold curve Time constraint Temperature constraints 22

23 Estimating production loss Ice load: model (blue) observed (red) 100% Production loss: model (blue) observed (red) 50% 0% 23