2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar

1. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [1]
SolarGIS
New generation solar resource
database and PV online assessment tools
geomodelsolar.eu
solargis.info
Artur Skoczek
Branislav Schnierer
GeoModel Solar, Slovakia
3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA

2. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [2]
About GeoModel Solar
Development and operation of SolarGIS online system
• Solar resource and meteo database
• PV simulation software
• Data services for solar energy and PV:
Consultancy and expert services
geomodelsolar.eu
solargis.info

3. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [3]
PVGIS
Research and demonstration project
Promotion of PV in Europe
by European Commission,
Joint Research Centre
SolarGIS
Commercial database and software
Focus on industry needs
by GeoModel Solar
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
GeoModel Solar: history
2012 20142013

4. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [4]
1. Solar and meteo database
2. Interactive services
• Solar prospection
• PV prefeasibility and planning
• PV performance monitoring
3. Computer-to-computer services
• Web services and regular data supply
• Solar and PV forecasting
http://solargis.info
SolarGIS platform

5. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [5]
Topics
SolarGIS
1. Solar and meteo database
2. PV simulation tools
3. Online applications
4. Summary

6. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [6]
Photovoltaics (PV)
What is required?
Concentrated Solar Power (CSP)
Concentrated Photovoltaics (CPV)
GHI (Global Horizontal Irradiation) DNI (Direct Normal Irradiation)
Solar resource

7. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [7]
What data sources are available for Japan
Free infor sources:
• NASA SSE
• NREL
• PVGIS
• ...
Commercial suppliers
Source: NASA/SWERA, Meteonorm , 3Tier, SolarGIS
SolarGIS database

8. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [8]
Data issues:
• Limited accuracy
=> difference between data sets can be seen
• Some databases are static
What data source are available for Japan
Input data sources
Data resolution
Methods
Level of validation
SolarGIS database

9. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [9]
Requirements for solar resource data
• Data to be available at any location (continuous coverage)
• Longer climate record
• High accuracy (validated)
• High level of detailed (temporal, spatial)
• Continuous:
• Historical data
• Data for monitoring, nowcasting
• Data for forecasting
This is available with satellite-based data,
supported by high-quality ground measurements
SolarGIS database

10. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [10]
Solar radiation – sources of information
1. Ground sensors
• Pyranometers or photo cells
• Installed on the site
2. Satellite-based solar models
• Input: satellite & atmospheric data
• Data are available globally

11. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [11]
Solar radiation – sources of information
1. Ground sensors
• Pyranometers or photo cells
• Installed on the site
2. Satellite-based solar models
• Input: satellite & atmospheric data
• Data are available globally

12. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [12]
Option 1: Ground (on-site) measurements
ADVANTAGES LIMITATIONS
High frequency measurements (sec. to min.)
Higher accuracy, if properly managed and
controlled
Historical data
Meteo stations are irregularly distributed
Limited time availability
Sensor accuracy
Recent data
Costs for acquisition and operation
Regular maintenance and calibration
Data quality checking

13. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [13]
Issues in ground measurements
Quality-control procedures
Missing data
Time shift
Unrealistic values
Shading
Misaligned and miscalibrated sensors

14. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [14]
Solar radiation – sources of information
1. Ground sensors
• Pyranometers or photo cells
• Installed on the site
2. Satellite-based solar models
• Input: satellite & atmospheric data
• Data are available globally

15. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [15]
ADVANTAGES CHALLENGES
Available everywhere
Spatial and temporal consistency
Calibration stability
High availability >99% (gaps are filled)
History of up to 20+ years
Lower instantaneous accuracy
(spatial resolution approx. 3.5 km)
Lower frequency of measurements
(15 and 30 minutes)
Source: EUMETSAT, ECMWF,
NOAA, SRTM-3, SolarGIS
Option 2: Satellite data
SolarGIS database

16. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [16]
Ground-measured vs. satellite-derived
Distance to the nearest meteo
stations – interpolation gives only
approximate estimate
Source: SolarGIS
Resolution of the input data used in
the SolarGIS model:
AOD: Atmospheric Optical Depth
WV: Water Vapour
MFG/MSG: Meteosat First/Second Generation

17. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [17]
SolarGIS: innovation in satellite-based solar modeling
Solar resource data
• Geography-adapted models
with numerous improvements
• New cloud model
• New-generation atmospheric data
• High level of detail
• Extensive validation
• Online global services, fast availability
• Customized services
SolarGIS database
Source: SolarGIS

18. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [18]
Corrected geometric and radiometric
distortions
Multi-spectral analysis of satellite data
• 2 to 4 channels
Multi-temporal analysis
SolarGIS: improved use of satellite data
Source: MTSAT (JMA)
SolarGIS database

19. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [19]
SolarGIS: improved use of satellite data
Source: NOAA
SolarGIS database

20. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [20]
• Snow/ice/fog conditions
• Tropical clouds
• High mountains
• Deserts (reflecting surfaces,
high clouds, dust)
• Coastal zones
SolarGIS models: adapted to different geographies
Source: EUMETSAT
SolarGIS database

21. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [21]
Newapproach:
DailyAOD
Traditionalapproach:
MonthlyaveragedAOD
SolarGIS: improved identification of aerosols
Source: ECMWF
SolarGIS database

22. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [22]
Ilorin, Nigeria
Tamanrasset, Algeria
Riyadh, Saudi Arabia
DNI
SolarGIS: improved identification of aerosols
Atmospheric pollution changes rapidly
Source: AERONET, ECMWF
SolarGIS database

23. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [23]
SolarGIS innovation: detailed terrain modelling
Source: SolarGIS
(c) 2014 Google
Global coverage (iMaps): 250 metres
GHI in Central Japan
Regional maps: 90 metres
GHI in Kosrae, Micronesia
• Primary data (satellite): 3 to 5 km
• Terrain postprocessing: data available
at resolution up to 90 metres
SolarGIS database

24. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [24]
GHI solar resource in the world context
Source: SolarGIS
SolarGIS database

25. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [25]
Uncertainty of satellite-based solar resource: SolarGIS
Typical range of uncertainty
(annual values at 80% occurrence):
• Global Horizontal Irradiation (GHI): ±4%
• Direct Normal Irradiation (DNI): ±8%
180+ GHI & DNI measurements
230+ aerosol measurements (AERONET)
Theoretical uncertainty of the best ground sensors:
• ±2% for GHI
• ±1% for DNI
-> In real conditions difficult to achieve
Conference SolarPACES 2012, 13 September 2012, Marrakech, Morocco [18]
Use of AOD correction for improvement of SolarGIS database
- Regional adaptation of the AOD database used in SolarGIS model
- Based on the AERONET data and ground measurements
- Aim: identify and remove regional bias of the MACC AOD database, reduce DNI
uncertainty
SolarGIS database
Source: AERONET stations

26. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [26]
Uncertainty of satellite-based solar resource: SolarGIS
SolarGIS database
SolarGIS GHI validation data
Typical range of uncertainty
(annual values at 80% occurrence):
• Global Horizontal Irradiation (GHI): ±4%
• Direct Normal Irradiation (DNI): ±8%
180+ GHI & DNI measurements
230+ aerosol measurements (AERONET)
Theoretical uncertainty of the best ground sensors:
• ±2% for GHI
• ±1% for DNI
-> In real conditions difficult
to achieve

27. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [27]
Typical uncertainty of satellite-derived SolarGIS data
Global Horizontal Irradiation
Hourly: ±12% to 45% Daily: ±5% to 23% Monthly: ±4% to 14%
Annual: ±3% to 7%
80% probability of occurrence (example of Almeria, Spain)
Uncertainty of Direct Normal Irradiation is about 1.5 to 2x higher
SolarGIS database

28. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [28]
Hourlyvalues Daily Monthly Yearly
SolarGIS: Uncertainty of Global Horizontal Irradiance
The uncertainty for ground sensors considers that they are well maintained, calibrated and data are quality controlled
±4 to ±8%
SolarGIS high uncertainty
• high latitudes
• high mountains
• high and changing aerosols
• reflecting desert surfaces
• snow and ice
SolarGIS low uncertainty
• arid and semiarid regions
• low and medium aerosols
SolarGIS database

29. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [29]
Hourlyvalues Daily Monthly Yearly
±8 to 15%
SolarGIS high uncertainty
• high latitudes
• high mountains
• high and changing aerosols
• reflecting desert surfaces
• snow and ice
SolarGIS low uncertainty
• arid and semiarid regions
• low and medium aerosols
SolarGIS: Uncertainty of Direct Normal Irradiance
SolarGIS database

30. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [30]
Summary: How SolarGIS compares to ground measurements
Limits
• Accuracy lower than best sensors
• Inherent discrepancy of high frequencey measurements (e.g. hourly)
Advantages
• Comparative accuracy with good quality sensors in many regions
• Better than low quality sensors
• Radiometric stability and continuity
• Easy calculation of solar radiation for any PV surface (fixed or suntracking)
• Historical data available (up to 20+ years)
• Can be correlated (site-adapted) by local measurements
SolarGIS database

31. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [31]
• Derived from meteorological models
• Validated
• Air temperature
• Ancillary data: Wind speed, Relative humidity…
SolarGIS meteo parameters
Source: SolarGIS, Google, NOAA
Air temperature

32. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [32]
Topics
SolarGIS
1. Solar and meteo database
2. PV simulation tools
3. Online applications
4. Summary

33. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [33]
PV performance in Standard Test Conditions: 2308 kWh/kWp
PV annual output: 1782 kWh/kWp, losses 22.8% (PR=77.2%), uncertainty: 5.5%
PV simulation chain
example Cairo (SolarGIS)
Global irradiation
(module surface)
Mismatch and cable losses
Inter-row shading losses
Angular reflectivity
Shading by terrain
Losses in the conversion of
irradiance into DC in modules
Transformers and AC losses
Technical availability
-1.2% ±0.7%
Dirt, dust and soiling
-2.0% ±0.8%
-2.5% ±0.6%
-1.0% ±0.7%
±4.5%
-2.6% ±0.5%
-2.5% ±2.0%
-11.7% ±2.0%
Losses in the inverters
-1.5% ±0.5%
LOSSES UNCERTAINTY
-0.0% ±0.0%
Irradiation
received by
PV modules
DC power
in PV modules
Conversion
to AC, transformation
and feed to 22 kV
Air temperature
SolarGIS PV tools

34. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [34]
Scatergram of SolarGIS satelite derived
Global Horizontal Irradiation vs. ground
measured data
Scatergram of postprocessed
SolarGIS air temperature vs. ground
measured data
Match between ground measured GHI and temperature
with SolarGIS values
In collaboration with SUPSI, Switzerland
SolarGIS PV tools

35. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [35]
PV simulation algorithms (SolarGIS)
Simulation of energy yield and performance ratio of triple junction roof-integrated and
free-standing amorphous silicon modules mounted horizontally
Red: measured PV data
Black: simulated data In collaboration with SUPSI Switzerland
SolarGIS PV tools

36. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [36]
Topics
SolarGIS
1. Solar and meteo database
2. PV simulation tools
3. Online applications
4. Summary

37. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [37]
Fast access to data
• Interactive access
• Web services, FTP
Coverage: world
Availability: within few hours for any location
• Historical: from 1994/1999/2006 up to yesterday
• Nowcast: daily update
• Forecast: up to 48 hours ahead
SolarGIS data services
SolarGIS database

38. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [38]
Data products are tuned to save time and money
Prefeasibility and prospection
• Long-term monthly averages
 iMaps
 pvPlanner
Project development and operation
• Time series
• Typical Meteorological Year
• 15 and 30-minute, hourly monthly
 climData
 Automatic data services
SolarGIS data services
SolarGIS database
• Annual
subscription
• Aggregated data
Lower price
• Data to be
purchased per site
• High information
content
Higher price

39. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [39]
Prospecting and site evaluation
iMaps: High-resolution satellite-based data and maps
• Online and fast access to long-term annual and monthly averages
• Detailed and accurate maps
Source: SolarGIS
SolarGIS online applications

40. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [40]
Prospecting and site evaluation
pvPlanner: PV planning tool
• Easy search of site
• Accurate simulation
• High-resolution data
• Technology options
• Access to data (xls, csv and pdf)
Source: SolarGIS
SolarGIS online applications

41. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [41]
High resolution data
climData: Purchase site-specific data:
• Time series
• Typical Meteorological Year (TMY)
Where to use
• Project development
• Site adaptation
• Performance assessment
of power plants
• Quality control of ground measurements
SolarGIS online applications

42. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [42]
Regular monitoring
pvSpot: performance assessment
• Independent view on the performance of the system
• Daily update
SolarGIS online applications

43. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [43]
15-minute profile of PV power generation
SolarGIS database

44. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [44]
Topics
SolarGIS
1. Solar and meteo database
2. PV simulation tools
3. Online applications
4. Summary

45. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [45]
Summary 1: SolarGIS database
Solar and meteo data
• Low uncertainty of raw SolarGIS data
• High detail
• History of satellite-based solar radiation
and meteo data 15+ years
• Near real-time data update
Source: SolarGIS

46. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [46]
Summary 2: SolarGIS online tools
• Detailed data resolution
• Interactive maps
• Fast access (interactive and automated)
• Accurate PV simulation
• Scaled products and services
Source: SolarGIS

47. 3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [47]
Thank you!
Artur Skoczek
Branislav Schnierer
GeoModel Solar, Slovakia
http://solargis.info
http://gemodelsolar.eu