Data requirements

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

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Data requirements

  1. 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. 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.infoSolar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011
  3. 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 CentreSolar 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. 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 assessmentSolar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011
  5. 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 modelingSolar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011
  6. 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 assessmentSolar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011
  7. 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. 2010Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011
  8. 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. GueymardSolar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011
  9. 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 infoSolar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011
  10. 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 measurementsSolar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011
  11. 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 globallySolar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011
  12. 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: SolarGISSolar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011
  13. 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 satelliteSolar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011
  14. 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: SolarGISSolar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011
  15. 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 assessmentSolar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011
  16. 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 sensorsSolar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011
  17. 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 statisticsSolar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011
  18. 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 0Solar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011
  19. 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. 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 assessmentSolar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011
  21. 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 latitudesSolar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011
  22. 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 mapsSolar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011
  23. 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 assessmentSolar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011
  24. 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 resourceSolar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011
  25. 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. 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 availableSolar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011
  27. 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 distributionSolar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011
  28. 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. 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, RMYSolar Atlas for the Mediterranean, Stakeholders Solar s.r.o., Cairo, Egypt, 1 Nov 2011 © 2011 GeoModel Workshop, Bratislava, Slovakia page 17 of 33
  30. 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 directionSolar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011
  31. 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 resultsSolar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011
  32. 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 assessmentSolar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011
  33. 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 dataSolar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011
  34. 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 monitoringSolar Atlas for the Mediterranean, Stakeholders Workshop, Cairo, Egypt, 1 Nov 2011
  35. 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

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