Bankable solar resource assessment and risk management in planning - - PowerPoint PPT Presentation

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Bankable solar resource assessment and risk management in planning - - PowerPoint PPT Presentation

Oct 23, 2023 254 likes •532 views

Bankable solar resource assessment and risk management in planning and operation of Solar Energy Projects Marcel Suri, PhD GeoModel Solar s.r.o., Bratislava, Slovakia marcel.suri@geomodel.eu http://solargis.info http://geomodelsolar.eu

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Workshop for Solar Energy and Smart Grid Development, Asian Development Bank, Regional Task Force [1] 13-15 September 2011, Jodhpur, Rajasthan, India

Bankable solar resource assessment and risk management in planning and operation of Solar Energy Projects

Marcel Suri, PhD GeoModel Solar s.r.o., Bratislava, Slovakia marcel.suri@geomodel.eu http://solargis.info http://geomodelsolar.eu

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Workshop for Solar Energy and Smart Grid Development, Asian Development Bank, Regional Task Force [2] 13-15 September 2011, Jodhpur, Rajasthan, India

About GeoModel Solar

Expert consultancy:

  • Solar resource assessment and PV yield prediction
  • Performance characterization
  • Country optimization potential
  • Grid integration studies

SolarGIS: 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

European Commission PVGIS 2001-2008 SolarGIS from 2008

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Workshop for Solar Energy and Smart Grid Development, Asian Development Bank, Regional Task Force [3] 13-15 September 2011, Jodhpur, Rajasthan, India

International collaboration

International Energy Agency, Solar Heating and Cooling Program:

  • Task 36 Solar Resource Knowledge Management
  • Task 46 Solar Resource Assessment and Forecasting
  • EU COST Action Weather Intelligence for Renewable Energies
  • EU project Management and Exploitation of Solar Resource Knowledge (finished)
  • National Renewable Energy Laboratory (NREL, US)
  • SUNY (US)
  • DLR (DE)
  • Fraunhofer ISE (DE)
  • Stellenbosch University (ZA)
  • University of Geneva (CH)
  • European Commission JRC (IT)
  • CENER (ES)
  • SUPSI ISAAC (CH)
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Workshop for Solar Energy and Smart Grid Development, Asian Development Bank, Regional Task Force [4] 13-15 September 2011, Jodhpur, Rajasthan, India

Bankable data = low uncertainty, high reliability

Solar resource estimate

  • High quality ground measurements of solar radiation missing
  • Diverse results from the existing databases
  • Poor understanding of the potential of the modern satellite-derived data

Weather interannual variability

  • Long and continuous record of data is needed (10+ years)
  • Changing weather (natural and human induced) and extreme events

(e.g. volcanoes) to be considered

  • In the recent history
  • In the future

Uncertainty in solar resource assessment

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Workshop for Solar Energy and Smart Grid Development, Asian Development Bank, Regional Task Force [5] 13-15 September 2011, Jodhpur, Rajasthan, India

Solar resource – requirements for solar projects

Data available at any location Long-climate record (10 years minimum) Cleaned, validated, harmonized and without gaps High accuracy, low uncertainty (no systematic errors, good representation) High level of detail (temporal, spatial) Modern data products (time series, TMY, long-term averages) Standardized data formats Real-time data supply:

  • historical
  • monitoring
  • nowcasting
  • forecasting

+ Meteo and other geodata for energy modeling (temperature, wind, humidity)

All this is possible with satellite-based data, supported by high-quality ground measurements!

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Workshop for Solar Energy and Smart Grid Development, Asian Development Bank, Regional Task Force [6] 13-15 September 2011, Jodhpur, Rajasthan, India

Solar resource – how to obtain site-specific information

Ground instruments (interpolation/extrapolation) Satellite-based solar data (solar radiation models & atmospheric data)

WRDC network (~1200 archive stations) sources: NASA, EUMETSAT, Stoffel et al. 2010 sources: NREL, WRDC

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Workshop for Solar Energy and Smart Grid Development, Asian Development Bank, Regional Task Force [7] 13-15 September 2011, Jodhpur, Rajasthan, India

Available solar databases - Gujarat

The databases differ in many aspects:

  • Input data (satellite/ground)
  • Applied methods/models
  • Time coverage (period)
  • Time and spatial resolutions

GHI >10% more for DNI!

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Workshop for Solar Energy and Smart Grid Development, Asian Development Bank, Regional Task Force [8] 13-15 September 2011, Jodhpur, Rajasthan, India

Ground instruments

ADVANTAGES LIMITATIONS High accuracy at the point of measurement High frequency measurements (sec. to min.) High-quality data THIS APPLIES ONLY IN THE CONTROLLED AND RIGORIUSLY MANAGED CONDITIONS Historical data: Limited time of measurement Limited number of sites Unknown accuracy (in historical data) Different periods of measurement … Operation of a ground station: Regular maintenance and calibration Data management Issues of aggregation statistics High costs for acquisition and operation Extrapolation/interpolation ignores site-specific info

source: Gueymard 2010AWI

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Workshop for Solar Energy and Smart Grid Development, Asian Development Bank, Regional Task Force [9] 13-15 September 2011, Jodhpur, Rajasthan, India

Uncertainty in ground observations

Issues

  • Sensors accuracy
  • Installation and maintenance routines
  • Cleaning of the sensor
  • Calibration
  • Time shifts, shading

Needed procedures

  • Data post-processing
  • Quality checking (only high-frequency data!)
  • Filling the gaps in the measurements
  • Missing data results in skewed aggregation statistics (e.g. daily and monthly sums)
  • High probability of systematic deviation (BIAS) and occurrence of extreme values
  • Uncleaned data result in unreliable values
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Workshop for Solar Energy and Smart Grid Development, Asian Development Bank, Regional Task Force [10] 13-15 September 2011, Jodhpur, Rajasthan, India

Solar radiation models: satellite-derived data

ADVANTAGES LIMITATIONS Available everywhere (continuous coverage) Spatial resolution from 3 km Frequency of measurements from 15 minutes Spatial and temporal consistency High calibration stability Availability ~99.5% History of up to 20 years Continuous geographical coverage (global) Lower instantaneous accuracy for the point estimate (when compared to high quality ground measurements)

Data sources: EUMETSAT, ECMWF Source: SolarGIS

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Workshop for Solar Energy and Smart Grid Development, Asian Development Bank, Regional Task Force [11] 13-15 September 2011, Jodhpur, Rajasthan, India

Uncertainty in satellite-derived DNI and GHI

Clouds Aerosols Water vapour Terrain

DNI 0 to 100% ±10% (up to ± 50%) ±3 to 4% 100% GHI 0 to 80% ±2 to 3% (up to ± 12%) ±0.5 to 1% 60 to 80% Highest uncertainty

Atmosperic Optical Depth Water vapour Clouds Terrain

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Workshop for Solar Energy and Smart Grid Development, Asian Development Bank, Regional Task Force [12] 13-15 September 2011, Jodhpur, Rajasthan, India

AERONET MACC GEMS

Kanpur Uncertainty of Aerosol Optical Depth (AOD)

MACC model compared to ground measured AERONET data

Critical for DNI

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Workshop for Solar Energy and Smart Grid Development, Asian Development Bank, Regional Task Force [13] 13-15 September 2011, Jodhpur, Rajasthan, India

Typical uncertainty of ground-measured vs. 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%

Bias:

  • It is natural for satellite-derived data and can be reduced/removed
  • For ground-measured data it is very challenging and costly to keep bias

close to 0

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Workshop for Solar Energy and Smart Grid Development, Asian Development Bank, Regional Task Force [14] 13-15 September 2011, Jodhpur, Rajasthan, India

Typical uncertainty of ground-measured vs. 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
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Workshop for Solar Energy and Smart Grid Development, Asian Development Bank, Regional Task Force [15] 13-15 September 2011, Jodhpur, Rajasthan, India

Typical uncertainty of ground-measured vs. 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 very challenging to keep high standard of DNI ground measurements
  • Satellite data can be correlated with ground measurements to obtain improved

site solar statistics

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Workshop for Solar Energy and Smart Grid Development, Asian Development Bank, Regional Task Force [16] 13-15 September 2011, Jodhpur, Rajasthan, India

  • 16 -
  • Four stations compared in Germany and Netherlands
  • Calibration issue identified (Ineichen 2011)

Quality checking of ground measurements using SolarGIS

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Workshop for Solar Energy and Smart Grid Development, Asian Development Bank, Regional Task Force [17] 13-15 September 2011, Jodhpur, Rajasthan, India

Accuracy and representativeness: Distribution of values

Comparison of distributon

  • f DNI clearness index:
  • measured (yellow)
  • satellite-derived (blue)

Proper distribution statistics plays key role in energy simulation

Source: IEA SHC Task 36 data inter-comparison activity, Pierre Ineichen, University of Geneva, February 2011: http://www.unige.ch/cuepe/pub/ineichen_valid-sat-2011-report .pd

SolarGIS

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Workshop for Solar Energy and Smart Grid Development, Asian Development Bank, Regional Task Force [18] 13-15 September 2011, Jodhpur, Rajasthan, India

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

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Workshop for Solar Energy and Smart Grid Development, Asian Development Bank, Regional Task Force [19] 13-15 September 2011, Jodhpur, Rajasthan, India

Annual DNI average in India source: SolarGIS

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

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Workshop for Solar Energy and Smart Grid Development, Asian Development Bank, Regional Task Force [20] 13-15 September 2011, Jodhpur, Rajasthan, India

Interannual variability: Northwest India

Interannual variability is driven by:

  • Natural climate cycles
  • Change of aerosols (human factor)
  • Climate change (long-term trends)
  • Occasional large volcanic eruptions

Assuming years 1999-2010: Average Minimum GHI: 2035 1964 (-4.5%) DNI: 1764 1621 (-8.1%)

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Workshop for Solar Energy and Smart Grid Development, Asian Development Bank, Regional Task Force [21] 13-15 September 2011, Jodhpur, Rajasthan, India

Ground measurements available for a short time period (few months, 1-2 years) They are correlated with time series of satellite-derived irradiance to:

  • Correct systematic errors (reduce bias)
  • Match data frequency distribution

Site adaptation of satellite-based time series is needed for LARGE PROJECTS

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Workshop for Solar Energy and Smart Grid Development, Asian Development Bank, Regional Task Force [22] 13-15 September 2011, Jodhpur, Rajasthan, India

Original DNI “ground – satellite” data scatterplot: Bias: -4.2% Correction of bias and frequency distribution

Example: Tamanrasset (Algeria)

Site adaptation of satellite-based time series

  • Modern high-resolution satellite-based solar models offer solar resource

information at high detail and quality

  • New ground measurements will help to reduce uncertainty
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Workshop for Solar Energy and Smart Grid Development, Asian Development Bank, Regional Task Force [23] 13-15 September 2011, Jodhpur, Rajasthan, India

Ground data

  • Important for validation, and site-adaptation of the satellite-derived data (reference data)
  • Only quality sensors and properly managed measurement campaign
  • It is challenging to achieve high quality and continuity of measurements

Satellite-based data

  • Global coverage, high frequency high detail
  • High temporal and spatial resolution
  • Harmonized, radiometricaly stable, no gaps
  • Continuous history of 12+ years in India

To reduce uncertainty, combine ground measurements with satellite data

Summary: uncertainty in solar resource data

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Workshop for Solar Energy and Smart Grid Development, Asian Development Bank, Regional Task Force [24] 13-15 September 2011, Jodhpur, Rajasthan, India

Is the solar resource prediction for 20 years right?

  • Only high quality data – averaging bad numbers cannot yield in a good assessment
  • Robust and long history for interannual variability
  • Average does not say much – go for annual and monthly P(50), P(75) and P(90)
  • Analytics of possible issues (shading, aerosols, mountains, coastal zone, desert geography, etc.)
  • Only solar resource experts

Is the solar power plant performing as expected?

  • Use recent high quality and continuous measurements
  • Cross-validated (sat-ground) data
  • Site-adapted data
  • Compare solar resource to validated performance data

How to reduce risk?

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Workshop for Solar Energy and Smart Grid Development, Asian Development Bank, Regional Task Force [25] 13-15 September 2011, Jodhpur, Rajasthan, India

SolarGIS: online system for solar energy and PV

  • Access to SolarGIS historical and real-time data (automatic and interactive)
  • Maps and prospecting tools
  • PV planning and optimization
  • PV monitoring & performance assessment
  • PV forecasting

http://solargis.info

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Workshop for Solar Energy and Smart Grid Development, Asian Development Bank, Regional Task Force [26] 13-15 September 2011, Jodhpur, Rajasthan, India

Thank you for your attention!

Marcel Suri, PhD. GeoModel Solar s.r.o., Bratislava Slovakia marcel.suri@geomodel.eu http://solargis.info http://gemodelsolar.eu

DNI (SolarGIS)