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