1. Validation and utilization of numerical weather model data in energy systems analysis of decentralized electricity production
Hans Schermeyer, Valentin Bertsch, Wolf Fichtner
Hans Schermeyer
Athens, October 9th 2014
7th International Scientific Conference on Energy and Climate Change

2. Context within our research
Research Group Distributed Energy Systems and Networks
Model-based energy systems analysis considering grid restrictions
Design und analysis of distributed energy systems and smart markets/grids
Evaluation of technologies, investment options and business models for political or entrepreneurial decision support
Goal of this work:
Evaluation of data from a numerical weather model for the application in energy systems analysis

3. Motivation: Market design to cope with grid constraints
High penetration of RES-E (about 10% of German wind power)
Low population and industry density  low grid capacity
Figure: Google-Earth

4. Motivation: Market design to cope with grid constraints
Simulation of renewable electricity generation with a high spatial resolution
Identify grid constraints in the distribution grid
Find a market design for an efficient allocation during congestion
Figure: EON Netz GmbH (2014): Netzübersichtkarte -Nordbereich- EisMan-Regionen -

5. Data Measurement data Global irradiation [W/m²]
Temporal resolution: hourly
Spatial resolution: 57 measurement stations in Germany (run by the German national weather service)
Model data
Global irradiation [W/m²]
Temporal resolution: up to 10 min
Spatial resolution: 20x20 km
NASA data applied to MM5 numerical weather model by PSU
Picture: NOAA Earth System Research Laboratory (ESRL)

6. Method Select and apply appropriate indicators measuring similarity
Measuring similarities between time series
Mean bias error (MBE)
Describes the general trend of deviations
Root mean square error (RMSE)
Reflects the scattering of model vs.
measurement data
Linear correlation (R)
Measures linear dependency

7. Method Select and apply appropriate indicators measuring similarity
Measuring similarities between time series
Mean bias error (MBE)
Describes the general trend of deviations
Focus on energy systems analysis
Root mean square error (RMSE)
Reflects the scattering of model vs.
measurement data
Measure the model‘s ability to produce realistic rather than real data with regard to fluctuations and extreme values
Linear correlation (R)
Measures linear dependency

8. Method Maximum amplitude of radiation supply (MARS)

9. Method Maximum gradient of radiation supply (MGRS)
Prof. Dr. Max Mustermann | Musterfakultät

10. Method Spatial volatiliy

11. Results: MGRS

12. Results: MGRS
As one would expect the MGRS within 3 hours is significantly greater than within 1 hour
 dominated by the diurnal cycle
The 3h gradients of the model data are generally higher. This leads to the conclusion that the diurnal cycle in the model data is more distinct and few cloud events are simulated (matches results of MBE and scatterplot)
MGRS shows that model data is less volatile with respect to 1h gradients of radiation supply and provides a quantification.
Altogether the difference in MGRS appears to be within acceptable limits when compared to standard deviation and RMSE as benchmark

13. Results: spatial volatility
Spatial volatility of the measured and modelled data respectively

14. Results: spatial vola
Hours during night and low data availability were excluded
The mean spatial volatility is 29% and 24% respectively
Peaks in the morning and afternoon can be explained by the time shift of sunrise at different sites
Regime-switching during the day: Model data shows higher spatial vola during morning and afternoon hours while the differences between measured sites peak during the day
The spatial volatility reveals substantial differences between modelled and measured data. But: Partly due to different data formats and their temporal resolution

15. Outlook and open questions
Fundamental attributes of a future energy system (grid capacity, demand for flexible back-up generation capacity and its distribution within the grid) caused by RES-E generation on a decentralized level can only be simulated appropriately if extreme and rare values are included in model data
In order to measure these relevant parameters we introduce the MARS, MGRS and the spatial volatility and apply these to the data set.
For future development of the MARS, MGRS and spatial volatility introduced in this paper, it might prove helpful to eliminate deterministic trends of the solar radiation time series. Thus frequent and repetitive changes in radiation supply that are easier to predict do not affect the indicators.
Moreover a higher temporal resolution will help to improve the significance of the MARS, MGRS and spatial volatility. This would enable a more accurate preprocessing of the data on the one hand and allow conclusions on the higher temporal resolution on the other hand.

16. Thank you very much. Questions?! Hans Schermeyer
Karlsruhe Institute of Technology (KIT) , Chair of energy economics
‘Distributed energy systems and networks‘
Phone:
Fax:

17. Back Up

18. Mean bias erros MBE
Root mean square error RMSE
Correlation coefficient R

19. Results MBE, RMSE, R

20. Results First order statistics
Compared to literature the model data’s MBE appears to be too large (positive biased) while the scattering measured by the RMSE is within limits of a good model performance. The average correlation of 90% suggests decent model performance.
However, these indicators by themselves appear to be insufficient to assess a model’s performance as they do not capture fundamental characteristics of the time series:
maximum amplitudes
maximum gradients
and spatial generation differences (volatility)