1 Manfred Dorninger ICE-CONTROL - The challenge of reasonable icing forecasts for optimizing wind energy production
ICE-CONTROL The challenge of reasonable icing forecasts for optimizing wind energy production
Saskia Bourgeois, René Cattin Meteotest, Switzerland
Thomas Burchhart, Martin Fink VERBUND Hydro Power, Austria
Manfred Dorninger, Lukas Strauss, Stefano Serafin University of Vienna, Austria
Alexander Beck (PI), Christoph Wittmann, Florian Weidle, Florian Meier Zentralanstalt für Meteorologie und Geodynamik, Austria
Project duration: 04/2016-03/2019
2 Manfred Dorninger ICE-CONTROL - The challenge of reasonable icing forecasts for optimizing wind energy production
Challenges ? • Building-up a model chain including model uncertainties
• Measurement of relevant parameters for each model
including icing on the blades for verification • Demonstrate that proposed approach outperforms systems
on the market (benchmark test)
3 Manfred Dorninger ICE-CONTROL - The challenge of reasonable icing forecasts for optimizing wind energy production
Numerical weather prediction model
Model chain
Production loss model
GM: ECMWF
MM: AROME,
WRF
Icing model
Makkonen model Ice-blade model
Verification Verification Verification
Empirical model
4 Manfred Dorninger ICE-CONTROL - The challenge of reasonable icing forecasts for optimizing wind energy production
Initial conditions Operational requirements
Initial conditions Process representation Parameter uncertainty Conversion to rotor blade
Model uncertainties Probabilistic forecasts
Icing model Production loss model
MEASUREMENTS VERIFICATION
MEASUREMENTS VERIFICATION
MEASMNTS VERIF
Initial conditions Model physics Model topography
Numerical weather prediction model
5 Manfred Dorninger ICE-CONTROL - The challenge of reasonable icing forecasts for optimizing wind energy production
Wind farm site Ellern, Rhineland-Palatinate, Germany Wind farm owned by VERBUND
Hilly terrain in the Hunsrück area Up to 350 m above the surroundings (649-780m above sea level)
6 Enercon E-101 5 Enercon E-126 Production loss by 5-10% due to icing
Total nominal capacity 55 MW
6 Manfred Dorninger ICE-CONTROL - The challenge of reasonable icing forecasts for optimizing wind energy production
Measurement campaigns Oct 2016 – Mar 2017 Oct 2017 – Mar 2018
Additional to SCADA data • T/RH-Sensor (Rotronic) • Laser Distrometer (Thies)
(0.125 mm – 8 mm) • Fog-Monitor FM-120
(2 μm – 50 μm) • PWD 12 (Vaisala) • IceMonitor (CombiTech) • eologix sensors
2 on gondola 26 on rotor blades
• 3 web cams • Icing Detection Sensor
(IDS, Sommer) • IceTroll (Kjeller) • Windlidar (windcube)
VERBUND
7 Manfred Dorninger ICE-CONTROL - The challenge of reasonable icing forecasts for optimizing wind energy production
Evaluation of measurement data (an example)
Instrumental icg. (~520 h)
Meteorological icg. (~200 h)
8 Manfred Dorninger ICE-CONTROL - The challenge of reasonable icing forecasts for optimizing wind energy production
Evaluation of measurement data (an example)
9 Manfred Dorninger ICE-CONTROL - The challenge of reasonable icing forecasts for optimizing wind energy production
2017-01-04 13:00 UTC 2017-01-03 13:00 UTC 2017-01-04 03:50 UTC
CASE STUDY: 3-4 Jan 2017
(kg)
10 Manfred Dorninger ICE-CONTROL - The challenge of reasonable icing forecasts for optimizing wind energy production
Numerical weather prediction model
Icing model
FORECASTS
Are model physics uncertainties important for icing forecasts?
• Land-surface • Surface layer • Boundary layer • Microphysics • Convection • Cloud fraction • ... Challenge to select useful combinations
WRF
Initial conditions Model physics Model topography
11 Manfred Dorninger ICE-CONTROL - The challenge of reasonable icing forecasts for optimizing wind energy production
WRF
FORECASTS
• 10-member ensemble • 2-domain configuration
12.5 km, 2.5 km • 50 vertical levels
• dz≈20 m on first 300 m AGL • ICs from ECMWF EPS members (10 out of 50, how to choose? See poster P23: Serafin et al.) DYN • Various combinations of physics
schemes PHYS
dx = dy = 12.5 km
2.5 km
D01
D02
12 Manfred Dorninger ICE-CONTROL - The challenge of reasonable icing forecasts for optimizing wind energy production
CASE STUDY: 3-4 Jan 2017
DYN
780 m turbine
hub
D01
D01
PHYS
OBS
OBS <-> MODEL • Final load fits
well • Different
icing rates (strength, duration)
WRF – Makkonen model
13 Manfred Dorninger ICE-CONTROL - The challenge of reasonable icing forecasts for optimizing wind energy production
CASE STUDY: 3-4 Jan 2017
DYN +NEIGH
PHYS +NEIGH
D01
D01
780 m turbine
hub
OBS
WRF – Makkonen model
14 Manfred Dorninger ICE-CONTROL - The challenge of reasonable icing forecasts for optimizing wind energy production
• The ICE CONTROL project aims at improving ice forecasts through • probabilistic forecasts, using several approaches for the generation
of meteorological ensembles (ICs, physics parameterizations, DA) • two-winter field campaigns on site • verification at all steps of the “model chain”
• Results from the project will point to the complexity of mesoscale
ensemble prediction systems required for reliable icing forecasts.
• Preliminary results suggest that uncertainties from physics parameterizations are substantial.
SUMMARY
15 Manfred Dorninger ICE-CONTROL - The challenge of reasonable icing forecasts for optimizing wind energy production
• Evaluate measurement data • meteorological instruments • on-blade icing detectors (eologix)
• Prepare measurement campaign 2017/2018
• Further improve the WRF PHYS ensemble configurations
• Verify and calibrate (-> statistical significance!)
• Study icing models
• Cylinder vs. blade icing models (Makkonen, iceBlade, ...) • Explore parameter uncertainties
• Develop Cost-Loss model for production model
OUTLOOK
16 Manfred Dorninger ICE-CONTROL - The challenge of reasonable icing forecasts for optimizing wind energy production