S8 Marcel Suri (GeoModel Solar) - Bankable Solar Resource Assessment and Risk Management

Workshop “Workshop for Solar Energy and Smart Grid Development” 13-15 September 2011, The Gateway Hotel, Jodhpur, Rajasthan, Ind

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

15 September 2011, The Gateway Hotel, Jodhpur, Rajasthan, India [1]

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

Marcel Suri, PhD

GeoModel Solar s.r.o., Bratislava, [email protected]

http://solargis.infohttp://geomodelsolar.eu

Workshop “Workshop for Solar Energy and Smart Grid Development” 13-15 September 2011, The Gateway Hotel, Jodhpur, Rajasthan, Ind26th European Photovoltaics Solar Energy Conference and Exhibition, 5

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.euhttp://solargis.info

15 September 2011, The Gateway Hotel, Jodhpur, Rajasthan, India [2]26th European Photovoltaics Solar Energy Conference and Exhibition, 5-9 September 2011, Hamburg - 2 -

Solar resource assessment and PV yield prediction Performance characterizationCountry optimization potential

time solar and meteo data services for:Site selection and prefeasibilityPlanning and project designMonitoring and forecasting of solar power

European CommissionPVGIS 2001-2008

SolarGIS from 2008


International collaboration

International Energy Agency, Solar Heating and Cooling • Task 36 Solar Resource Knowledge Management • Task 46 Solar Resource Assessment and

• EU COST Action Weather Intelligence for Renewable • EU project Management and Exploitation of Solar Resource Knowledge

• 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)


Heating and Cooling Program:Solar Resource Knowledge Management Solar Resource Assessment and Forecasting

Intelligence for Renewable EnergiesManagement and Exploitation of Solar Resource Knowledge (finished)

National Renewable Energy Laboratory (NREL, US)


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

Weather interannual variability• Long and continuous record of data is needed (10+ years)• Changing weather (natural and human induced

(e.g. volcanoes) to be considered• In the recent history• In the future

Uncertainty in solar resource assessment


Bankable data = low uncertainty, high reliability

High quality ground measurements of solar radiation missingDiverse results from the existing databasesPoor understanding of the potential of the modern satellite-derived data

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

Uncertainty in solar resource assessment


Solar resource – requirements for solar projects

Data available at any locationLong-climate record (10 years minimum)Cleaned, validated, harmonized and without gapsHigh accuracy, low uncertainty (no systematic errors, good representation)High level of detail (temporal, spatial)Modern data products (time series, TMY, longStandardized data formatsReal-time data supply:

• historical• monitoring• nowcasting• forecasting

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

All this is possiblesupported


requirements for solar projects

and without gaps(no systematic errors, good representation)

(time series, TMY, long-term averages)

modeling (temperature, wind, humidity)

possible with satellite-based data, by high-quality ground measurements!


Solar resource – how to obtain site

Ground instruments(interpolation/extrapolation)

WRDC network (~1200 archive stations)

sources: NREL, WRDC


how to obtain site-specific information

Satellite-based solar data (solar radiation models & atmospheric data)

sources: NASA, EUMETSAT, Stoffel et al. 2010


Available solar databases - Gujarat

The databases differ in many aspects:• Input data (satellite/ground)• Applied methods/models


Gujarat

• Time coverage (period)• Time and spatial resolutions

GHI>10%

more for DNI!


Ground instruments

ADVANTAGES

High accuracy at the point of measurementHigh frequency measurements (sec. to min.)High-quality data

THIS APPLIES ONLY IN THE CONTROLLED AND RIGORIUSLY MANAGED CONDITIONS

source: Gueymard 2010AWI


LIMITATIONS

Historical data:Limited time of measurementLimited number of sitesUnknown accuracy (in historical data)Different periods of measurement…

Operation of a ground station:Regular maintenance and calibrationData managementIssues of aggregation statisticsHigh costs for acquisition and operation

Extrapolation/interpolation ignoressite-specific info


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 • High probability of systematic deviation (BIAS) and occurrence of extreme values• Uncleaned data result in unreliable values


observations

aggregation statistics (e.g. daily and monthly sums)High probability of systematic deviation (BIAS) and occurrence of extreme values

data result in unreliable values


Solar radiation models: satellite

ADVANTAGES

Available everywhere (continuous coverage)Spatial resolution from 3 kmFrequency of measurements from 15 minutes

Spatial and temporal consistencyHigh calibration stabilityAvailability ~99.5%History of up to 20 years

Continuous geographical coverage (global)

Source: SolarGIS


Solar radiation models: satellite-derived data

LIMITATIONS

Lower instantaneous accuracy for the point estimate (when compared to high qualityground measurements)

Data sources: EUMETSAT, ECMWF


Uncertainty in satellite-derived DNI

Clouds Aerosols

DNI 0 to 100%±10%

(up to ± 50%)

GHI 0 to 80%±2 to 3%

(up to ± 12%)

Atmosperic Optical

Clouds


derived DNI and GHI

Aerosols Water vapour Terrain

50%) ±3 to 4% 100%2 to 3%

12%) ±0.5 to 1% 60 to 80%

Highest uncertainty

Depth

Water vapour

Terrain


AERONET MACC GEMS

Uncertainty of Aerosol Optical Depth (AOD)

MACC model compared to ground measured


Kanpur

Uncertainty of Aerosol Optical Depth (AOD)

measured AERONET data

Critical for DNI


Typical uncertainty of ground-measured vs. satellitesolar data

GHI Thermopile pyranometerISO Classification Secondary Standard First ClassWMO Classification High Quality Good QualityHourly uncertainty 3%

Daily uncertainty 2%

bias depends on the calibration and maintenance

DNI Thermopile pyrheliometerWMO Classification High quality Good qualityHourly uncertainty 0.7%

Daily uncertainty 0.5%

Bias:• It is natural for satellite-derived data and• For ground-measured data it is very challenging

close to 015 September 2011, The Gateway Hotel, Jodhpur, Rajasthan, India [13]

measured vs. satellite-derived

Thermopile pyranometer SatelliteFirst Class Second Class

Good Quality Mod. Quality RMSD Bias8% 20% 9-20% ±2-7%

5% 10% 4-12%


Thermopile pyrheliometer RSR SatelliteGood quality RMSD Bias

1.5% 2-4% 24-60% ±4-12%

1.0% 1.5% 15-25%

and can be reduced/removedchallenging and costly to keep bias








GHI: • satellite already competitive in RMSD





5% 10% 4-12%



1.5% 2-4% 24-60% ±4-12%

1.0% 1.5% 15-25%

RMSD with good-quality sensors








DNI: • It is very challenging to keep high standard• Satellite data can be correlated with ground

site solar statistics15 September 2011, The Gateway Hotel, Jodhpur, Rajasthan, India [15]




5% 10% 4-12%



1.5% 2-4% 24-35% ±4-12%

1.0% 1.5% 15-25%

standard of DNI ground measurementsground measurements to obtain improved


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

Quality checking of ground measurements using SolarGIS

15 September 2011, The Gateway Hotel, Jodhpur, Rajasthan, India [16] - 16 -

Four stations compared in Germany and NetherlandsCalibration issue identified (Ineichen 2011)

Quality checking of ground measurements using SolarGIS


Accuracy and representativeness:

SolarGIS


representativeness: Distribution of values

Comparison of distributonof DNI clearness index: • measured (yellow)• satellite-derived (blue)

Proper distribution statisticsplays key role in energysimulation

Source: IEA SHC Task 36 data inter-comparisonactivity, Pierre Ineichen, University of Geneva,

February 2011:http://www.unige.ch/cuepe/pub/ineichen_valid-sat-2011-report .pd


Ground-measured vs. satellite-

Distance to the nearest meteo stations – interpolation gives only approximate estimate

Source: SolarGIS


-derived

Resolution of the input data used in the SolarGIS model:AOD: Atmospheric Optical DepthWV: Water VapourMFG/MSG: Meteosat First/Second Generation


SUMMARY: Ground vs. satellite

• 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: SolarGIS

vs. satellite-based solar data

geographical and time diversity of solar climate


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


Northwest India

Assuming years 1999-2010:

Average MinimumGHI: 2035 1964 (-4.5%)DNI: 1764 1621 (-8.1%)


Ground measurements available for a short time period (few months, 1They are correlated with time series of satellite

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

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


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)

based time series is needed for LARGE PROJECTS


Original DNI “ground – satellite” data scatterplot:Bias: -4.2%

Example: Tamanrasset (Algeria)

Site adaptation of satellite-based time series

• Modern high-resolution satellite-based solar models offer solar resourceinformation at high detail and quality

• New ground measurements will help to reduce uncertainty


Correction of bias and frequency distribution

based time series

based solar models offer solar resource

New ground measurements will help to reduce uncertainty


Ground data• Important for validation, and site-adaptation of the satellite• 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


adaptation of the satellite-derived data (reference data)Only quality sensors and properly managed measurement campaignIt is challenging to achieve high quality and continuity of measurements

Global coverage, high frequency high detail

Harmonized, radiometricaly stable, no gaps

To reduce uncertainty, combine ground measurements with satellite data

Summary: uncertainty in solar resource data


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?


Is the solar resource prediction for 20 years right?averaging bad numbers cannot yield in a good assessment

and long history for interannual variabilitygo for annual and monthly P(50), P(75) and P(90)

Analytics of possible issues (shading, aerosols, mountains, coastal zone, desert geography, etc.)

Is the solar power plant performing as expected?Use recent high quality and continuous measurements

Compare solar resource to validated performance data


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

15 September 2011, The Gateway Hotel, Jodhpur, Rajasthan, India [25] - 25 -

for solar energy and PV

time data (automatic and interactive)

PV monitoring & performance assessment


Thank you for your attention!

Marcel Suri, PhD.

GeoModel Solar s.r.o., BratislavaSlovakia

[email protected]://solargis.infohttp://gemodelsolar.eu


DNI (SolarGIS)

Documents

S8 Marcel Suri (GeoModel Solar) - Bankable Solar Resource Assessment and Risk Management