1. Challenges in the Assimilation of PV Power Data in the Convection-Permitting High-Resolution NWP Model COSMO-DE
Stefan Declair*, Yves-Marie Saint-Drenan, Roland Potthast
Erstellung innovativer Wetter- und Leistungsprognosemodelle für die Netzintegration wetterabhängiger Energieträger
- Eine Kooperation von Meteorologie und Energiewirtschaft -
80. Jahrestagung der DPG und DPG-Frühjahrstagung
Regensburg, March 08th 2016

2. Motivation: Low Stratus
Planetary boundary layer
height
free atmosphere
Inversion layer
Ekman layer (1000m)
Prandtl layer (100m)
laminar (mm)
temperature

3. Agenda Data Assimilation Impact Experiment Method Observation Data
Monitoring: Bias and Error
Assimilation: Impact?

4. Agenda Data Assimilation Impact Experiment Method Observation Data
Monitoring: Bias and Error
Assimilation: Impact?

5. Forecast: Can I cross the street without getting hit?
Information used:
Observations
Knowledge about cars, street, etc
Experience  statistics
Forecast errors due to:
Observation (estimation) errors
Model errors (icy street)
Case does not match statistics

6. Numerical Weather Forecast
Model: First-guess
Data assimilation tool
Observations
Analysis: Improved initial conditions for next integration step

7. Available Observation Data
Blue: large-scale PV power plants (~200)
Grey: small-scale PV power panels (~3.7k)
Meta data:
Lon/lat coordinates
Tilt angle
Azimuth angle
Degradation coefficients
Corrections (fitted 1)) for :
Temperature
Power
1) Source and Work: Yves-Marie Saint-Drenan, Fraunhofer IWES

8. Forward operator for PV module
Model Equivalent: PV Power Forward Operator
Forward operator for PV module
Model variables:
Shortwave surface irradiance (direct and diffuse downward)
Panel ambient temperature
Surface albedo
Synthetic PV power
Module meta data:
Panel azimuth/tilt angles
lon/lat
Degradation coefficients
Corrections for temperature and power
Compute angle between panel normal and sun position at current time
Transform horizontal model irradiation into tilted panel plane
Compute losses (soiling, shading, module temperature, optical losses)

9. Sources of Error Inaccurate/unknown meta data Angles
Large diversity in panel manufacturers
Local deviations (data) from
fitted corrections1)
surface albedo
Local deviations (model) from
Aerosol optical thickness 2)
Cloud positions
Radiation scheme 3)
1) Source and Work: Yves-Marie Saint-Drenan, Fraunhofer IWES
2) Tegen et al., Journal of Geophysical Research., 102, pp (1997)
3) Ritter et al., Monthly Weather Review, 120, pp (1992).

10. Agenda Data Assimilation Impact Experiment Method Observation Data
Monitoring: Bias and Error
Assimilation: Impact

11. Monitoring of Observations
Determination of PV power model equivalent bias and observation error
Cycling over 1 week, PV power data only passive in assimilation
Valid for

12. First Glimpse on First Results: Low Clouds Control
First-guess
Analysis
Difference

13. First Glimpse on First Results: Low Clouds Experiment
First-guess
Analysis
Difference

14. First Glimpse on First Results: Low Clouds
06 UTC
09 UTC
12 UTC

15. First Glimpse on First Results: Mid Clouds
06 UTC
09 UTC
12 UTC

16. Conclusion
LETKF successfully utilized to assimilate PV power obserations
Cloud cover correction pretty well for low and middle clouds despite strong non-locality in the morning
Spread increase due to PV power data assimilation  better representation of model error
Outlook
Stock up on PV power data!
Experiments with better localization lengths
Detailed analysis of increments in other atmospheric fields
Single observation experiments
Impact on forecast quality
Combi-experiment: add wind power data to assimilation cycle

17. Thank you for your attention! Now: Q & A