Solar Atlas for the Mediterranean - Stakeholder Workshop Cairo 1 November 2011

1. Requirements for solar
resource data
Marcel Šúri
GeoModel Solar s.r.o., Bratislava, Slovakia
marcel.suri@geomodel.eu
http://geomodelsolar.eu
http://solargis.info
http://www.solar-med-atlas.org/
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

2. About GeoModel Solar
Expert consultancy:
• Solar resource assessment and PV yield prediction
• Performance characterization in PV
• Country optimization potential
• Grid integration studies
SolarGIS online services:
Real-time solar and meteo data services for:
• Site selection and prefeasibility
• Planning and project design
• Monitoring and forecasting of solar power
• Solar data infrastructure
http://geomodelsolar.eu
http://solargis.info
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

3. Timeline
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
PVGIS SolarGIS
Research and demonstration project Commercial database,
Promotion of PV Professional software
Public awareness in Europe Industrial applications
by European Commission, by GeoModel Solar
Joint Research Centre
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011
26th European Photovoltaics Solar Energy Conference and Exhibition, 5-9 September 2011, Hamburg -3-

4. Contents
Solar Resource Data for Energy Projects:
1. Requirements
2. Ground measured and satellite-derived data
3. Accuracy and interannual variability
4. Data for prefeasibility and smaller projects
5. Bankable data
• For design optimization and financing
• For performance assessment
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

5. Solar resource information - REQUIREMENTS
• Data available at any location (global coverage)
• Long climate record - up to 15-20 years (harmonized and without gaps)
• High accuracy, low uncertainty (validated)
• High level of detail (temporal, spatial)
• Modern data products (long-term averages, TMY, time series)
• Real-time data supply (online):
• historical data
• monitoring, nowcasting
• forecasting
This is available with satellite-based data,
supported by high-quality ground measurements
+ Meteo and other geographic data for energy modeling
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

6. Contents
Solar Resource Data for Energy Projects:
1. Requirements
2. Ground measured and satellite-derived data
3. Accuracy and interannual variability
4. Data for prefeasibility and smaller projects
5. Bankable data
• For design optimization and financing
• For performance assessment
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

7. Solar resource – how to obtain site-specific information
Ground instruments Solar radiation models
(interpolation/extrapolation) (satellite & atmospheric data)
WRDC network (~1200 archive stations)
sources: Gueymard 2010, WRDC, BSRN-AWI sources: NASA, EUMETSAT, Stoffel et al. 2010
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

8. Ground-measured solar data
What determines quality:
1. Quality and accuracy of instruments
(pyranometer, photocell, RSR, pyrheliometer)
2. Operation and maintenance routines
3. Calibration
4. Quality control and post processing
High-quality data are available only for a limited number of sites
Photo: sourtesy of NREL and C. Gueymard
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

9. Ground observations
ADVANTAGES LIMITATIONS
High accuracy at the point of measurement Accessing historical data:
High frequency measurements (sec. to min.) Limited geographical coverage
High-quality data - if strictly controlled and Limited access
managed Missing time series and metadata
No standard data formats
Different time reference
Operation of a ground station:
Regular maintenance and calibration
Data management
Issues of aggregation statistics
High costs for acquisition and operation
Extrapolation/interpolation needed
to get site-specific info
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

10. Ground observations Inconsistency between GHI and DNI
Before any use – ground data have
to be quality assessed
Quality validation procedures:
• Physical limits
• GHI - DNI consistency, time drift
• Missing data
• Time shifts
• Shading, reflections, …
DNI from meteo service - 50% of data missing It needs lot of effort, dedication and resources
to have reliable ground measurements
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

11. Satellite-based solar resource data
How satellite-based solar radiation is calculated:
1. Clear-sky model, DNI model
• Aerosol Optical Depth (AOD)
• Water vapour (WV)
• Elevation
2. Cloud transmission (satellite) model
• Data from geostationary satellites
3. Terrain enhancement model
• High-resolution elevation data
Data are available globally
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

12. Solar radiation models: satellite-derived data
ADVANTAGES LIMITATIONS
More accurate for distances Lower accuracy for the point estimate
higher than 15-30 km from Lower time frequency of measurements
the nearest ground observation (15 , 30 , hourly, 3-hourly data)
Spatial and temporal consistency
High signal stability
Availability ~99.5%
Data can be disaggregated
Direct link to other models
History of up to 25 years
Continuous geographical
coverage
Data sources: EUMETSAT, ECMWF
Source: SolarGIS
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

13. Ground-measured vs. satellite-derived solar resource data
Source: SolarGIS
Resolution of the input data used in
Distance to the nearest meteo
the solar model:
stations – interpolation gives only AOD: Atmospheric Optical Depth
approximate estimate WV: Water Vapour
MFG/MSG: Meteosat satellite
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

14. SUMMARY: Ground vs. satellite-based solar data
• Solar data are site specific
• High variability and intermittency
• Ground data are not able to represent
geographical and time diversity of solar climate
• It is important to use high-quality satellite
combined with ground data
Annual DNI average in India
source: SolarGIS
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

15. Contents
Solar Resource Data for Energy Projects:
1. Requirements
2. Ground measured and satellite-derived data
3. Accuracy and interannual variability
4. Data for prefeasibility and smaller projects
5. Bankable data
• For design optimization and financing
• For performance assessment
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

16. Achievable uncertainty of ground-measured
and satellite-derived solar data
GHI Thermopile pyranometer Satellite
ISO Classification Secondary Standard First Class Second Class
WMO Classification High Quality Good Quality Mod. Quality RMSD Bias
Hourly uncertainty 3% 8% 20% 9-20% ±2-7%
Daily uncertainty 2% 5% 10% 4-12%
bias depends on the calibration and maintenance
DNI Thermopile pyrheliometer RSR Satellite
WMO Classification High quality Good quality RMSD Bias
Hourly uncertainty 0.7% 1.5% 2-4% 24-60% ±4-12%
Daily uncertainty 0.5% 1.0% 1.5% 15-25%
GHI:
• satellite already competitive in RMSD with good-quality sensors
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

17. Achievable uncertainty of ground-measured
and satellite-derived solar data
GHI Thermopile pyranometer Satellite
ISO Classification Secondary Standard First Class Second Class
WMO Classification High Quality Good Quality Mod. Quality RMSD Bias
Hourly uncertainty 3% 8% 20% 9-20% ±2-7%
Daily uncertainty 2% 5% 10% 4-12%
bias depends on the calibration and maintenance
DNI Thermopile pyrheliometer RSR Satellite
WMO Classification High quality Good quality RMSD Bias
Hourly uncertainty 0.7% 1.5% 2-4% 24-35% ±4-12%
Daily uncertainty 0.5% 1.0% 1.5% 15-25%
DNI:
• It is challenging to keep high standard of DNI ground measurements
• Satellite data correlated with ground measurements can improve site statistics
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

18. Achievable uncertainty of ground-measured
and satellite-derived solar data Bias = systematic
deviation: clouds
+ aerosols
GHI Thermopile pyranometer Satellite
ISO Classification Secondary Standard First Class Second Class
WMO Classification High Quality Good Quality Mod. Quality RMSD Bias
Hourly uncertainty 3% 8% 20% 9-20% ±2-7%
Daily uncertainty 2% 5% 10% 4-12%
bias depends on the calibration and maintenance
DNI Thermopile pyrheliometer RSR Satellite
WMO Classification High quality Good quality RMSD Bias
Hourly uncertainty 0.7% 1.5% 2-4% 24-60% ±4-12%
Daily uncertainty 0.5% 1.0% 1.5% 15-25%
Bias:
• Bias is natural for satellite-derived data and can be reduced/removed
• For ground-measured data it is challenging and costly to keep bias close to 0
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

19. Interannual variability: Gujarat, India
Assuming years 1999-2010:
Interannual variability is driven by:
Average Minimum
• Natural climate cycles GHI: 2035 1964 (-4.5%)
• Change of aerosols (human factor) DNI: 1764 1621 (-8.1%)
• Climate change (long-term trends)
• Extreme volcanic eruptions!
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

20. Contents
Solar Resource Data for Energy Projects:
1. Requirements
2. Ground measured and satellite-derived data
3. Interannual variability
4. Data for prefeasibility and smaller projects
5. Bankable data
• For design optimization and financing
• For performance assessment
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

21. Prospecting, prefeasibility and site assessment
Data needed:
• Representative and homogeneous annual long-term averages
and monthly statistics of satellite-based data at high spatial resolution
• At least 10 years of data are needed to represent climate reliably
• Other meteo and GIS data (terrain, infrastructure, population, etc.) are useful for context
Uncertainty (bias) for long-term annual values - to be typically expected in semiarid zones:
• DNI ±4 to 15%
• GHI ±2 to 7%
Uncertainty depends on geography - is higher in:
• Complex land cover (land/sea/desert/islands)
• Mountains (snow/ice)
• Regions with extreme aerosols/humidity
• High latitudes
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

22. Prospecting, prefeasibility and site assessment
Services needed:
• Fast interactive access: on-the-click information
• Monthly data
• GIS analysis - resource potential of the region
• Digital maps
• Paper maps
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

23. Contents
Solar Resource Data for Energy Projects:
1. Requirements
2. Ground measured and satellite-derived data
3. Interannual variability
4. Data for prefeasibility and smaller projects
5. Bankable data
• For design optimization and financing
• For performance assessment
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

24. Feasibility, design optimization, financing and due diligence
Data needed:
• Site-specific solar data representing long-term data record:
• Time series
• Typical Meteorological Year (TMY)
• + Ancillary meteo data (air temperature, relative humidity, wind speed and direction)
Services needed:
• Site adaptation of satellite-based data by correlating them to local solar
measurements especially for CSP asnd CPV projects
• Generation of Typical Meteorological Year
• Bankable reports: Site analysis of solar resource
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

25. Feasibility, design optimization, financing and due diligence
Time series
• Full climate statistics: average, median, percentiles, P(50), P(90) probability
expectances:
• Uncertainty of the estimate
• Uncertainty due to interannual variability
• 12 to 20+ years of high quality data are available worldwide at primary
resolution of 3 to 5 km
Quality parameters:
• Minimum bias, low RMSD
• Representative distribution statistics
OPTIMALLY, bankable satellite-based time series should be:
• Validated by ground measurements representing the climate
• Site adapted (correlated) with the local measurements (especially for CSP and
CPV)
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

26. Site adaptation of satellite-based time series
Ground measurements available for a short time period (optimally 12 months)
They are correlated with time series of satellite-derived irradiance to:
• Correct systematic errors (reduce bias)
• Match data frequency distribution
Conditions to be fulfilled for successful adaptation:
• Systematic deviation in satellite data should exist
• Magnitude of deviation is
invariant in time
• High quality hourly (or more
detailed) ground measurements
are available
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

27. Site adaptation of satellite-based time series
Example: Tamanrasset (Algeria)
Original DNI “ground – satellite” data scatterplot:
Bias: -4.2% Correction of bias and frequency distribution
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

28. Site adaptation of DNI satellite-based time series
Example: Tamanrasset (Algeria)
Results:
• Reduced BIAS
• Reduced RMSD
• Improved statistical distribution
(KSI indicator)
Bias can be reduced to the accuracy limits of the ground sensor
=> Quality of ground measurements is important
!
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

29. Assessment of Solar Resource. Upington Solar Park, South Africa.
Reference No. 58-01/2011
Typical Meteorological Year (TMY)
Two data sets are derived from site-adapted time series – one for P(50) and one for P(90). In assembling RMY,
the values of DNI, GHI and Air Temperature are only considered, where the weights are set as follows: 0.6 is
given to DNI, 0.4 to GHI, and 0.1 to Air Temperature (divided by the total of 1.1).
The P(50) RMY data set represents, for each month, the average climate conditions and the most representative
• Includes hourly data of one “representative” year derived from the time series
cumulative distribution function, therefore extreme situations (e.g. extremely cloudy weather) are not
represented in this dataset. Therefore, to capture all possible weather situations it is recommended in power
production simulations to use full (17 years) time series of the data.
The P(90) RMY data set represents for each month the climate conditions which after summarization of DNI for
• Can be constructed on a monthly basis, comparing months of individual years
the whole year result in the value close to P(90) derived by statistical analysis of uncertainties and interannual
2
variability (the conservative DNI value 2729 kWh/m , see Section 10, Tab. 16). Thus RMY for P(90) represents
the year with the lowest annual value of DNI over the period of 17 years.
with two characteristics: average and cumulative distribution function. The
Both RMY data sets include the following parameters:
• Direct Normal Irradiance, DNI [W/m ]
2
representative months are concatenated into TMY
• Global Horizontal Irradiance, GHI [W/m ]
2
2
• Diffuse Horizontal Irradiance, Diff [W/m ]
• Weighting in TMY has to meet the modeling needs of CSP (focus on DNI)
•
•
Azimuth and solar angle [°]
Air temperature at 2 metres, Temper [°C]
• Wind speed at 10 metres, Wspeed [m/s]
• Wind direction, Wdir [°]
• Relative air humidity, Rh [%]
Fig. 11: Snapshot of the P(50) Representative Meteorological Year, RMY
Solar Atlas for the Mediterranean, Stakeholders Solar s.r.o., Cairo, Egypt, 1 Nov 2011
© 2011 GeoModel Workshop, Bratislava, Slovakia page 17 of 33

30. Typical Meteorological Year (TMY)
• P(50) TMY data set represents, for each month, the average climate conditions and
the most representative cumulative distribution function; extreme weather situations
are missing.
• P(90) TMY data set represents a year with the “lowest” identified solar resource –
annual DNI after summarization results in the value close to P(90).
The P(90) annual value is derived from time series, considering:
• Uncertainty of the estimate
• Interannual variability
• Solar resource data in TMY can be supplemented by air temperature, relative
humidity, wind speed, and wind direction
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

31. Site assessment report
• Based on time series of solar and meteo data
• Includes
• Quality control and validation:
• Satellite-based data (using representative ground measurements)
• On-site measurements
• Monthly and annual probability statistics
• Interannual variability
• Combined uncertainty:
• (i) estimate
• (ii) interannual variability
• P(50) and P(90) values
• Description of the methods and discussion of the results
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

32. Contents
Solar Resource Data for Energy Projects:
1. Requirements
2. Ground measured and satellite-derived data
3. Interannual variability
4. Data for prefeasibility and smaller projects
5. Bankable data
• For design optimization and financing
• For performance assessment
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

33. Performance assessment
Satellite-derived time series have numerous advantages (compared to ground sensors):
• Good quality, stable radiometry
• Available for any location
• Time availability 99.5%, just few gaps have to be filled by intelligent algorithms
• Known quality and uncertainty over large areas
• No problems with pollution, misalignment, data cleaning, calculation of time-
integrated statistics
=> Satellite-based solar resource is used for validation and gap filling of the on-site
measurements
Comparing on-site and satellite-based solar data
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

34. Conclusions
Combination of satellite and ground measured data is optimum for achieving
high quality solar resource
Solar Mediterranean Atlas (public domain)
• Prefeasibility tools, public awareness, policy support
• Small PV and hot water projects
Bankable data (commercial services)
• Medium size and large solar power plants
• Time series – satellite data optimally correlated with local measurements
• TMY data are often used in engineering software
• Probability statistics, variability and uncertainty needed for banks and insurance
• Historical data for new projects
• Operational data for performance assessment and monitoring
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011

35. Thank you
for your attention!
Marcel Šúri
GeoModel Solar s.r.o., Bratislava
Slovakia
marcel.suri@geomodel.eu
http://gemodelsolar.eu
http://solargis.info
http://www.solar-med-atlas.org/
Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011