Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

03 solargis uncertainty_albuquerque_pvp_mws_2017-05_final

455 views

Published on

8th PVPMC Workshop, May 9-10 2017

Published in: Technology
  • Be the first to comment

  • Be the first to like this

03 solargis uncertainty_albuquerque_pvp_mws_2017-05_final

  1. 1. Update on the uncertainty map of Solargis solar radiation database PV Performance Modeling and Monitoring Workshop Albuquerque, 9 - 10 May 2017 Marcel Suri, Tomas Cebecauer, Jose A. Ruiz-Arias, Juraj Betak and Artur Skoczek Solargis, Slovakia
  2. 2. PV Performance Modeling and Monitoring Workshop, Albuquerque, 9 - 10 May 2017 2 About Solargis Solar resource, meteorological and photovoltaic simulation data, software and expert services • Prospection • Project development • Monitoring • Forecasting historical and recent forecast NWP modelnowcast
  3. 3. PV Performance Modeling and Monitoring Workshop, Albuquerque, 9 - 10 May 2017 3 Customers: Commercial organizations • International technical consultancy advisors • Project developers • Finance • Asset management and monitoring companies • EPC and manufacturers • Utilities 3 Data delivered for commercial projects - last 12 months: • 2500+ sites served on-request • 1500+ sites regularly served via API
  4. 4. PV Performance Modeling and Monitoring Workshop, Albuquerque, 9 - 10 May 2017 4 Customers: Public organizations • World Bank Group • GIZ: India, South Africa, Nigeria, Pakistan, etc. • MASEN: Moroccan Agency for Sustainable Energy • KACARE Saudi Arabia • Ministry of energy of the UAE • Palestinian energy and natural resources authority • Secretariat of the Pacific Community, Micronesia • ESKOM South Africa • NamPower Namibia 4 Saudi Arabia Morocco UAE The World Bank Group Solargis data integrated in online solar atlases
  5. 5. PV Performance Modeling and Monitoring Workshop, Albuquerque, 9 - 10 May 2017 5 What we do Solargis solar and meteo database • Historical data (project evaluation) • Real-time data (monitoring and forecasting) Solargis online software services • Online apps • API Expert consultancy • Regional evaluation for countries • Site specific evaluation for solar project development 5
  6. 6. PV Performance Modeling and Monitoring Workshop, Albuquerque, 9 - 10 May 2017 6 How to measure/model solar radiation Meteorological measurements Satellite-based solar models (historical and recent data) Numerical Weather-Prediction models (Forecasts) (forecast data) Source: JMA Source: NOAA Source: CSP Services
  7. 7. PV Performance Modeling and Monitoring Workshop, Albuquerque, 9 - 10 May 2017 7 We focus on models Meteorological measurements Satellite-based solar models Numerical Weather-Prediction models Source: NOAA Source: CSP Services
  8. 8. PV Performance Modeling and Monitoring Workshop, Albuquerque, 9 - 10 May 2017 8 Solargis model
  9. 9. PV Performance Modeling and Monitoring Workshop, Albuquerque, 9 - 10 May 2017 9 Solargis:solar resource data Atmospheric models (aerosols, water vapor) Meteorological satellitesTerrain and other data GTI Global Tilted Irradiance (PV power plants) GHI DNI DIF DNI Direct Normal Irradiance (CSP power plants) Validation by solar measurements Source:: EUMETSAT, ECMWF, NOAA, SRTM, Solargis
  10. 10. PV Performance Modeling and Monitoring Workshop, Albuquerque, 9 - 10 May 2017 10 Data inputs: Solargis Optimised models Adapted to all geographies Output data: time series • Time step 10/15/30 minutes (depends on the satellite) • Spatial resolution: up to 250 m • History of last 10/18/23+ years • Real time update • Global coverage (latitudes 60N to 55S) • High accuracy (validated) Solargis:solar resource data Continuity • Historical data • Real-time data for monitoring, nowcasting and forecasting
  11. 11. Factors that determine the difference between the model and measurements Models • Mathematical and algorithmic formulation of models • Input data sets (satellite, weather models, etc.) Solar monitoring instruments* • Accuracy of sensors • Maintenance and calibration of the instruments • Quality control of the measured data *For validation, in major cases, we use high quality measurements Differencemodel - measurements
  12. 12. PV Performance Modeling and Monitoring Workshop, Albuquerque, 9 - 10 May 2017 12 Solargis GHI uncertainty map • Uncertainty of yearly GHI value is considered only • Uncertainty = 1.28155 * STDEV of yearly biases • Uncertainty map is based on: • 220+ GHI validation sites • 200+ AERONET sites • Experience and knowledge based on solar resource assessment expert studies at 600+ commercial assignments, worldwide • Analysis of multiple drivers prepared as map layers • First version in 10/2016 • New (second) version includes better evaluation of cloud effects • Final version to be published in Q3
  13. 13. PV Performance Modeling and Monitoring Workshop, Albuquerque, 9 - 10 May 2017 13 Solargis uncertainty of yearly estimates GHI: ±4% to ±8% ±3.9%** ±7.6%** * 68.27% occurrence: standard deviation (STDEV) assuming simplified assumption of normal distribution ** 80% occurrence: calculated as 1.28155 STDEV − can be used for an estimate of P90 values DNI: ±8% to ±16% Source: SolarGIS
  14. 14. PV Performance Modeling and Monitoring Workshop, Albuquerque, 9 - 10 May 2017 14 Solargis uncertainty: focus on yearly GHI Model deviation yearly GHI Distance to validation sites GHI: ±4% to ±8%
  15. 15. PV Performance Modeling and Monitoring Workshop, Albuquerque, 9 - 10 May 2017 15 Solar radiation model Inputsandoutputs Solargis inputs (Americas) Source of input data Time representation Original time step Approx. grid resolution Cloud index GOES (NOAA) 1999 to date 30 minutes 3 km Atmospheric Optical Depth (aerosols) MACC-II (ECMWF) 2003 to date 6 hours (monthly until 2002) 85 and 125 km Water vapor CFSR/GFS (NOAA) 1999 to date 1 and 3 hours 35 and 55 km Elevation and horizon SRTM-3 (SRTM) - - 250 m Solargis primary data outputs (GHI and DNI) - 1999 to date 30 minutes 250 m Source: Solargis Example region: Peru
  16. 16. Errors of model values The total error of the model values is a combination of: 1.Errors associated to the representation of solar radiation with physical formulation (the model) 2.Errors related to the model inputs: satellite data features, clouds, aerosols, water vapor, etc.
  17. 17. Methodology: Uncertainty drivers The uncertainty drivers: • Clouds persistence • Clouds variability • Atmospheric Optical Depth (Aerosols) • Total water vapor • Snow coverage • Terrain variability • Distance to water surface • Anthropogenic pollution • Satellite pixel distortion For each uncertainty driver, the corresponding sensitivity function has been empirically determined
  18. 18. Methodology: Simplified framework Total uncertainty σε 2 is considered the sum of individual contributions by several environmental factors (hereinafter referred to as uncertainty drivers) that add up to total uncertainty. Mathematically: • σm 2 represents the background uncertainty of the model (constant) • Φi(xi) are sensitivity functions which determine the individual contribution of each uncertainty driver xi. Note, it is assumed that φi is only a function of the uncertainty driver xi.
  19. 19. Methodology: evaluation of the uncertainty driver Satellite pixel distortion For each driver the following is evaluated: • Uncertainty model • Distribution of values sensitivity function global distribution
  20. 20. Uncertainty drivers: Clouds (persistenceand variability) Computed as as P99(GHI)/GHICS Key issue: high variability and persistence of clouds in tropics
  21. 21. Uncertainty drivers: Aerosol optical depth Computed from weather reanalysis as AOD550nm x AODerror (normalized)* Key issue: High atmospheric turbidity and high variability * Ruiz-Arias et al. 2013, doi: 10.5194/acp-13-675-2013
  22. 22. Uncertainty drivers: Water vapour Precipitable water computed from weather reanalysis data Key issue: high precipitable water in equatorial tropics
  23. 23. Uncertainty drivers: Snow coverage Computed from snow depth water equivalent (weather reanalysis) Key issue: Ability of satellites to discern snow/ice from clouds
  24. 24. Uncertainty drivers: Terrain Terrain variability computed as standard deviation of elevation from DEM Key issue: Mountains with complex and fast changing patterns of clouds, shadows, elevation and albedo
  25. 25. Uncertainty drivers: Distance to water surfaces Computed as/from: GIS layers representing water surfaces Key issues: mixed pixels on the overlap of land and water vs. geometric instability of satellites
  26. 26. Uncertainty drivers: Anthropogenic pollution Estimated from total SO2 emissions from EDGAR HTAP database Key issue: Solar radiation attenuation in high polluted areas
  27. 27. Uncertainty drivers: Satellite pixel distortion Computed as change of pixel diagonal length Shows magnitude of errors due to geometrical distortions in satellite data
  28. 28. Results (preliminary): Total GHI uncertainty Long-termvalue,in% Background error ≈ 3.5% • Compared to high quality measurements only • The value represents 80% probability of occurrence, i.e. 90% probability of exceedance
  29. 29. Comparing evidencewith the uncertainty estimates
  30. 30. Comparing model with measurements Factors that determine the difference between the model and measurements Models • Mathematical and algorithmic formulation of models • Input data sets (satellite, weather models, etc.) Solar monitoring instruments* • Accuracy of sensors • Maintenance and calibration of the instruments • Quality control of the measured data
  31. 31. PV Performance Modeling and Monitoring Workshop, Albuquerque, 9 - 10 May 2017 31 Reducing uncertainty of yearly estimates by ground measurements DNI: ±3.5% GHI: ±2.5% Best achievable uncertainty 0 2 4 6 8 10 12 14 16 0 12 24 36 48 Achievableuncertainty(%) Period of ground measurements (months) DNI P99.5 DNI P90 GHI P99.5 GHI P90 Running ground-monitoring campaign and combining measurements with satellite data is the way to maintain low uncertainty of solar resource data in a longterm Values are indicative
  32. 32. Next steps • This is improved version of the Solargis GHI uncertainty map • Further reduction of uncertainty expected with new empirical evidence • Results will be incorporated into the uncertainty processing chain • Uncertainty map for DNI under development • New uncertainty drivers and time aggregations will be considered Thank you! Artur Skoczek solargis.com contact@solargis.com

×