1. EMS 2016 Dublin Garrett H. Good & Jonas Berndt
Anticipating Extreme Wind and PV Power
Forecast Errors Using Ultra-Large Ensembles
Garrett H. Good & Jonas Berndt

2. The EoCoE Consortium

3. Other Thematic Pillars
Computational materials modelling for
1. morphology, electronic structure and transport properties of energy-related materials for PV, batteries and supercapacitors;
2. screening methodology for designing materials for PV, rechargeable batteries and supercapacitors
High-resolution long-term predictions of streamflow for
Geothermal (define residential area heat and electrical needs => plant size, form a 3D geophysical reservoir model, )
Hydro (simulate future hydrological states given climate change, interfaces between energy, population, biodiversity, population and macroeconomic models for integrated assessments, new storage technologies)
Fusion for Energy
First principle 5D geokinetic, 3d fluid and magneto hydrodynamic modelling for improving the lifetime of the diverter, quality of energy confinement, and preserving the purity of the fuel.

4. Our Thematic Pillar
Improving the predictability of renewables with a focus on low-probability extreme-error events using ultra-large meteorological ensemble forecasts with particle filtering.
Post-processing ultra-large ensemble forecasts to improve meteorological forecasts.
Solar energy prediction for optimal power generation of concentrated solar power.
Short-term forecasts of global irradiation from ground-based and satellite images.
Next-generation simulation tools to predict wind behavior over complex terrain.

5. EoCoE Task
Software development for generating ultra-large meteorological ensembles on high-performance computers (JUQUEEN / Jülich Blue Gene).
The evaluation of power forecast ensembles from meteorological data for extreme error events.
Development of a demonstrator warning system for low probability, high impact events in the energy sector.

6. initial and boundary conditions (GFS ensemble)
Ultra-Large Meteorological Ensemble
High-resolution regional ensemble with Weather Research and Forecasting (WRF) Model.
Multi-resolution ensemble (nesting with target horizontal resolution of 4 km).
From O(10) => O(1000) ensemble members.
Various state-of-the-art model uncertainty representation (SKEBS, SPPT, SPP, surface parameter).
d01
d02
Boundary Conditions
(First Grid)
d02
initial and boundary conditions (GFS ensemble)
Inner and outer domains each have ~5.5 million points (12 km and 4 km resolution, respectively).

7. Test case selection
Simulated days chosen from analysis of day-ahead TSO power forecast errors. (Seasonal trends adjusted for solar).
Higher order moments help distinguish days with the highest instantaneous errors.
Days typical of extreme error cases had unexpectedly strong winds or unexpected cloud cover.

8. Wind Test Case (9/8/2014)
Strong cyclogonesis over North Atlantic (NAO+)
Limited predictability of associated cut-off low
Failure of multiple weather models to predict location and shape of associated surface low
Underestimation of wind
power by 7.8 GW

9. Wind test case 9/8/2014
At midday, the ensembles match the DaF mean, while outliers capture the extreme event.
We note a nocturnal wind bias from the WRF model.
Figure: 1024 Power Ensembles, TSO power estimate (solid line), TSO multi-model day-ahead forecast (dashed line)

10. Wind Ensembles PDF
The PDF view, blue shows a long tail towards the extreme event.
The peak of the PDF is qualitatively similar to the multi-model TSO forecast.
Figure: Ensemble pdf (log color scale), median (dotted line), TSO power estimate (solid line), TSO multi-model day-ahead forecast (dashed line)

11. Wind Ensemble Moments
Figure (left): Root mean moments 𝑛 𝜇 𝑛 of ensemble group,
where 𝜇 𝑛 ≡ 𝑋−𝜇 𝑛 .

12. Wind Ensemble 3rd Moment Confidence Intervals
Figure: 95% confidence intervals for the root mean skewness based on different (bootstrap sample) ensemble sizes.

13. Solar Test Case (28/11/2014) Clear-sky predicted
South-East Anticylone (NAO-)
Winterly temperature inversion
Failure of multiple weather models to predict persistent fog and high fog throughout the day
Overestimation of solar power by over a factor of 2 (~5 MW error).
Clear-sky predicted

14. Solar Test Case 11/11/2014
For the solar test case, no ensemble captures the extreme event.
Two outliers however tend in this direction.
Figure: 1024 Power Ensembles, TSO power estimate (solid line), TSO multi-model day-ahead forecast (dashed line)

15. Solar Ensemble PDF
In the solar test case we also note asymetric tails towards the extreme error.
While having reproduced the multi-model result for the mean forecast.
Figure: Ensemble pdf (log color scale), TSO power estimate (solid line), TSO multi-model day-ahead forecast (dashed line)

16. Solar Ensemble Moments
Figure (left): Root mean moments 𝑛 𝜇 𝑛 of ensemble group, where 𝜇 𝑛 ≡ 𝑋−𝜇 𝑛 .

17. Solar Ensemble 3rd Moment Confidence Intervals
Figure: 95% confidence intervals for the root mean skewness based on different (bootstrap sample) ensemble sizes.

18. What have we shown so far?
Summary
What have we shown so far?
Higher-order statistics from ultra-large ensembles forewarned two extreme wind and solar error test cases.
If these are reliable indicators, operational ensemble sizes in the hundreds rather than tens may be adequate.

19. Thank You!
This work was supported by the Energy oriented Centre of Excellence (EoCoE),
grant agreement number , funded within the Horizon2020 framework of the European Union.