Your SlideShare is downloading. ×
0
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

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

1,462

Published on

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

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

Published in: Government & Nonprofit
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
1,462
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
66
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 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

×