Solar Resource Measurements and Satellite Data, Norbert Geuder

Solar Resource Measurementsand Satellite Data

4th Sfera Summer School 2013

Hornberg Castle, Germany

Dr. Norbert Geuder CSP Services – Almería – Cologne

4th Sfera Summer School, Hornberg Castle, 2013 - 2

1) Introduction

2) Fundamentals on Solar Irradiation

3) Variability of Irradiation

4) Irradiation Sensors

5) Sensor calibration and measurement accuracy enhancement

6) Satellite Based Assessment

7) Outlook / Summary

Outline

Selection of promising sites

Long-term time series(>10 years)

Meteorological Station:Accurate irradiation data

+Irradiation map: spatial distribution Geographical data: land use, etc.

Plant design, inter-annual variability, uncertainty analysis, financing, …

GIS analysis

General Procedure at Solar Resource Assessment


Solar Resource Assessment for CSP Plants

• Direct Beam Irradiation data required for CSP Applications

• Usually not available in suitable sunny regions in the world

• Accessible via measurements or derived from satellite data

Restrictions:

– Measurements: expensive, long duration, not for the past

– Satellite data: high uncertainty (≈ 10 % or more)

• High accuracy required for DNI with long-term performance


not global irradiation – this is quite a difference!

Impact of Solar Resource Uncertaintyon CSP Plant rentability

Annual DNI

Annual expenses

(redemption, O&M, …)

Electricityproduction

DNI uncertainty(e.g. ±10 %)

Earningsexpected long-term value (100%)

Losses

Irradiation uncertainty decides over project realization !

With thoroughly performed measurements an accuracy of approximately 2 % is achievable.


Fundamentals on Solar Irradiation


Solar Constant

Mean solar irradiance (flux density in W/m²) at normal incidence outside the atmosphere at the mean sun-earth distance r.

1367 W/m²

Calculation:

Luminosity of the sun: L = 3.86 x 1026 WAstronomical unit: r = 149.60×109 m

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radi

ance

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Day of Year

Annual Variation of Solar Constant

22 13674 m

Wr

L


Source: DLR

Path of Solar Radiation through the atmosphere

Rayleigh scattering and absorption (ca. 15%)

Absorption (ca. 1%)

Scatter and Absorption ( ca. 15%, max. 100%)

Reflection, Scatter, Absorption (max. 100%)

Absorption (ca. 15%)

Ozone.……….…....

Aerosol…….………..…...……

Water Vapor…….……...………

Clouds………….………..

Air molecules..……

Direct normal irradiance at ground

Radiation at the top of atmosphere


Source: DLR

Radiative Transfer in the Atmosphere

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Extraterrestrial

O2 and CO2

Ozone

Rayleigh

Water Vapor

Aerosol

Clouds


Source: DLR

Air Mass

AM = 0 (outside of atmosphere)


Solar Spectrum and Atmospheric Influence

1 Planck curve T=5780 K at mean sun-

earth distance

2 extraterrestrial solar spectrum

3 absorption by 03

4 scattering by 02 und N2

5 scattering by aerosols

6 absorption by H2O vapor

7 absorption by aerosols

UV radiation: 0.01 - 0.39 µm, ~ 7 %

Visible Spectrum: 0.39 – 0.75 µm, ~ 46 %

Near infrared: 0.75 – 2.5 µm, ~ 47 %

www.volker-quaschning.de/articles/fundamentals1/index.php


Characteristics of solar irradiation data• Component:

– DNI (Direct-Normal Irradiation)– DHI (Diffus-Horizontal Irradiation) – GHI (Global-Horizontal Irradiation)

• Source:– ground measurements:

• precise thermal sensors (thermopiles)• Rotating Shadowband Irradiometers (RSI)

– satellite data

• Properties of irradiation:– spatial variability– inter-annual variability– long-term drifts


Direct, Diffuse and Global Irradiance

When measuring solar irradiance, the following components are of particular interest:

Direct normal irradiance (DNI)(also: beam irradiance)

Diffuse horizontal irradiance (DHI)(also: diffuse sky radiation)

Global horizontal irradiance (GHI)(also: total solar irradiance)

GHI = DHI + DNI * sin ()

Zenith angle θ,solar elevation


DNI = BHI / sin

Example:

= 50°BHI = 600W/m²

DNI = 848W/m²

DNI > BHI

Direct-Normal- Irradiation (DNI)

direct

Direct Normal Irradiation (DNI)

with: BHI = Beam Horizontal Irradiation


GHI = BHI + DIF

Example:

DIF = 150W/m²BHI = 600W/m²

GHI = 750W/m²

Global-Horizontal-Irradiation (GHI)

diffuse

direct

diffuse

Global Horizontal Irradiation (GHI)


Variability of irradiation

Long-term variability of solar irradiance

GHI from Potsdam, Germany


Source: DLR

Long-term variability of solar irradiance

7 to 10 years of measurement to get long-term mean within 5%


Source: DLR

Inter-annual variability

Strong inter-annual and regional variations

1999

2001

2000

2002

2003

deviationto mean

kWh/m²a

Average of the direct normal irradiance from 1999 to 2003


Irradiation sensors


Absolute Cavity Radiometer

Principle of Measurements:

- Possibility to measure absolute irradiancevalues. All other irradiance measurement devices need to be calibrated using an absolutecavity radiometer

- Its principle of operation is based on thesubstitution of radiative power by electrical(heating) power

- Measurement in intervals with minimal lengthof 45 s. Constant irradiation required for measurement campain

- Tracking device required

- No continuous measurement (!)

ftp.pmodwrc.ch/pub/pmo6-cc/user_guide_11.pdf

Valid for calibration purposes


Thermal sensors Semiconductor sensorRotating Shadowband Irradiometer,

RSI

(photodiode)

Suitable equipment for irradiance measurements forConcentrating Solar Power (CSP)

Pyranometer, pyrheliometer

(thermopiles)


Thermopile Sensors

CMP21 Pyranometer (GHI, DHI shaded) with ventilation unit CVF3

CHP1 Pyrheliometer (DNI)

Shading assembly with shading ball

Solys 2 sun tracker

Sun sensor


Thermopile Sensors – Pyrheliometer

Principle of Measurement:

- Pyrheliometer = radiometer suitable tomeasure direct normal irradiance

- Highly transparent window 97 – 98 % transmission of solar radiation

- Housing geomerty with 200 mm absorber tuberestricting acceptance angle to 5°

- Sensing element with black coating and built-in termopile device

- Pt-100 temperature sensor for temperaturecorrections

www.kippzonen.com/?product/18172/CHP+1.aspx


Pyrheliometer Specifications

Kipp&Zonen CHP 1 Specifications:

Spectral range: 200 to 4000 nm

Sensitivity: 7 to 14 µV/W/m² (mV/kW/m²)

Response time: < 5 s

Expected daily uncertainty: ± 1 %

Full opening view angle: 5° ± 0.2°

Required tracking accuracy: ± 0.5° from ideal



Thermopile Sensors – Pyranometer


- Pyrheliometer = radiometer suitable to measure short-wave (0.2 - 4 µm) global or diffuse radiation

- Highly transparent glass dome 97 – 98 % transmission of solar radiation

- Full view on 2π hemisphere(horizontal levelling required)

- Sensing element with black coating and built-in termopile

- Pt-100 temperature sensor for temperaturecorrections



Thermopile Sensors – Pyranometer

Kipp&Zonen CMP21 Specifications:

Spectral range: 285 to 2800 nm

Sensitivity: 7 to 14 µV/W/m² (mV/kW/m²)

Response time: 5 s www.kippzonen.com/?product/1491/CMP+21.aspx


Rotating Shadowband Pyranometer (RSP)

Rotating shadowband

Licor silicon photodiode

Housing of mechanics


RSI – Principle of Measurement

Source: Solar Millennium AG

Simplified sensor signal during shadow band rotation: once per minute, rotation lasts about 1.5 seconds


Licor Li-200 Pyranometer Sensor

Specifications:

Sensitivity: Typically 90 µA per 1000 W/m²

Response time: 10 µs.

Spectral range: 0.4 – 1.1 µm

Calibration: Calibrated against an Eppley Precision Spectral Pyranometer under natural daylight conditions. Typical error under these conditions is ±3% up to ±5%.

www.licor.com/env/Products/Sensors/200/li200_description.jsp


Precise thermal sensors:Pyrheliometer and Pyranometer on sun tracker

Advantages:

+ high accuracy (1 to 2%)

+ separate sensors for GHI, DNI and DHI (cross-check through redundancy)

Disadvantages:

- high acquisition costs

- high maintenance costs

- high susceptibility for soiling

- high power demand (grid connection required)

-GHI -DNI-DHI


Sensor with photo diode: Rotating Shadowband Irradiometer, RSI

Advantages:

+ fair acquisition costs

+ low maintenance

+ low susceptibility for soiling

+ low power demand (PV-Panel)

Disadvantage:

- reduced accuracy due to systematic deviations of the photodiode sensor response:

primordial DNI: ≈ 6 to 10 % (or even higher)


Measurement uncertaintyPrecise instruments (HP) versus RSI

Error source: Pyrheliometer: RSI:

Calibration < ±1.1% ±3% (... ±5%)

Temperature < ±0.5% 0% ... ±5%

Linearity < ±0.2% ±1%

Stability < ±0.5%/a < ±2%/a

Spectral dependence <±0.1% 0% ... ±8%

Sensor soiling -0.7% per day -0.07% per day

systematic errors

can be corrected!!4th Sfera Summer School, Hornberg Castle, 2013 - 33

Choice of Measurement Equipment

High Precision sensors (thermopiles) Rotating Shadowband Irradiometer: RSI

?

Which equipment is suitable for measurements in Solar Resource Assessment?


Objectives for Irradiance Measurements

Solar Resource Assessment

• at remote site

• no qualified staff

• no electric grid

• often dusty and arid areas

Power Plant Monitoring

• always qualified staff on site

• electric power available


Pyrheliometer soiling in southern Spain


Plataforma Solar de Almería

Comparison of sensor soiling


University of Almería

Soiling characteristics of pyrheliometers and RSI‘s

RSI sensor headPyrheliometer

Solar Irradiation


direct sunlight

glass plate

tube with 200 mm length

absorber

diffusor disk over

photodiode

Choice of the adequate equipment

For Solar Resource Assessment • at remote sites and • daily maintenance not feasible

an RSI is the premium choice for DNI measurements.

However:• Proper calibration• Corrections of systematic signal response• regular maintenance inspections (2 to 4 weeks)

are indispensable for reliable measurements.


Sensor calibration and measurement accuracy enhancement


Sensor Calibration – Fundamentals I

- The World Standard Group (WSG) is an assembly of highly precise absolute cavity radiometers.

- The measured mean value (World Radiometric Reference) is the measurement standard representing the SI unit of irradiance with an estimated accuracy of 0.3 %.

- All other short wave irradiation measurement systems are calibrated against this single value.

www.pmodwrc.ch/pmod.php?topic=wrc

Precision measurement at the WorldRadiation Center (WRC)


RSI sensor calibration by DLR on PSA

2-monthly calibration of each RSI against high-precision instruments

at Plataforma Solar de Almería (PSA)(recommended every 2 years)


RSI sensor calibration durationVariations of the Calibration Constant with calibration duration


DNI

Variability of the Correction Factors (CF)

Variability ofcorrection factors

(CF):radiation componentsneed to be correctedwith separate CFs.


Recalibration of RSIsDrift of Calibration Factor within 2 to 4 years


Correction of raw RSI measurement valuesOrigin of systematic errors of RSI response

• Temperature dependence of semiconductor sensor

• Spectrally varying irradiation– different for irradiation components (direct beam / diffuse)– depending on Air Mass

• Angle of incidence

• Pre-calibration of the sensor head (from the manufacturer)


Spectral correction of diffuse irradiation

2DHIGHIDNIΠspec

corrected

raw values

variation with air mass + altitude


Dependence of response on solar elevationBH

I ref/ BH

I RSI

solar elevation angle in degree

so called „cat-ear effect“

Correction applied only on direct beam portion of the global response


Dependence of response on solar elevationBH

I ref/ BH

I RSI

solar elevation angle in degree

corrected


Reachable accuracy for DNI with RSIs

RMSD = 13 W/m²

Accuracy of RSI measurements

as derived from a comparisonof the data from 23 RSIs with precise thermopile

measurementswithin the courseof a whole year

RSP GHI DHI DNI reference

(CHP 1) unit raw cor raw cor raw cor

average MB -10.3 ± 4.0 0.3 ± 1.3 -17.3 ± 1.6 -0.4 ± 0.7 24.6 ± 10.5 1.0 ± 0.5 1.0 ± 3.9 W/m²

RMSD 14.2 7.6 18.9 4.5 33.3 13.0 5.3 W/m²

Annual sum up to -2.5 < ±1 up to -15 < ±3.5 up to +7 < ±1 up to 1.3 %


10 min time resolution

Transferability of the results?

The reachable accuracy of the measured beam irradiance data for the client at his prospected sites depends on 2 crucial points:

• Stability of the sensor sensitivity

• Transferability of the results to other sites

• Regular inspections and data controlling


Transferability to other sites and climates

Parallel measurement campaigns in UAE:

• Comparision of 6 RSIs to high-precision thermal sensors

• Measurement periods >3 weeks

• in summer and winter


Relative deviation of DNI sum within measurement campaign


only summer:DNI < 730 W/m²

summer + winter:DNI until 1000 W/m²

Satellite Based Assessment


Satellite Derived Data


Analyze satellite data in two steps:

1. Atmosphere: Gather satellite information of atmospheric composition (ozone, water vapor and aerosols) and apply the ‘clear sky model’ to calculate the fractions of direct and diffuse irradiance

2. Clouds: Calculate the cloud index as thedifference between actual reflectivity of theearth as it is seen by the satellite and areference image which only includes reflectance of the ground

www.solemi.de/method.html

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Extraterrestrial

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Aerosol

Clouds


Scan

The Meteosat satellite is located in a geostationary orbit

The satellite scans the earth line by line every half hour

How to derive irradiance data from satellites

Source: DLR


How to derive irradiance data from satellites

Derivation of a cloud index

from the two channels Scan in infra-red spectrumScan in visible spectrum

Source: DLR


Different Cloud Transmission for GHI and DNI

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-26 °C-16 °C-6 °C4 °C

14 °C-30°C - -20°C-20°C - -10°C

-10 °C - 0°C0°C - 10 °C

>10°C

Different exponential functions for varying viewing angles and

brightness temperatures

Global Irradiation

Direct Irradiation

Source: DLR


Clear sky Model input data

Aerosol optical thickness GACP Resolution 4°x5°, monthly data MATCH Resolution 1.9°x1.9°, daily data

Water Vapor: NCAR/NCEP ReanalysisResolution 1.125°x1.125°, daily values

Ozone: TOMS sensorResolution 1.25°x1.25°, monthly values

Source: DLR


Uncertainty in AerosolsAll graphs are for JulyScales are the same!(0 – 1.5)Large differences in Aerosol values and distribution

GADS

Toms

GOCART

NASA GISS v1 / GACP

NASA GISS v2 1990

AeroCom Linke TurbiditySource: DLR


satellite pixel( 3x4 km²)

ground measurement instrument (2x2 cm²)

solar thermal power plant (200MW 2x2 km²

Comparing ground and satellite data: “sensor size”

Source: DLR


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general difficulties: • point versus area• time integrated versus

area integrated

Source: DLR

Comparing ground and satellite data: accuracy


12:45 13:00 13:15 13:30 13:45 14:00 14:15

Hi-res satellite pixel in Europe

Comparing ground and satellite data: time scales

Ground measurements are typically pin point measurements which are temporally integrated

Satellite measurements are instantaneous spatial averages

Hourly values are calculated from temporal and spatial averaging (cloud movement)

Hourly average Meteosat image Measurement

Source: DLR


Source: DLR

Temporal resolution of input data: 1 hourSpatial resolution of digital map: 1 km x 1 km per PixelLong term analysis: up to 20 years of data

data produced by (DLR, 2004) for MED-CSP

The original digital maps can be navigated and zoomed with Geographical Informations Systems like ArcView or Idrisi.

Results of the satellite-based solar assessment

Digital maps: e.g. annual sum of direct normal irradiation in 2002 in the Mediterranean Region


Source: DLR

Hourly monthly mean of DNI in Wh/m², Solar Village 2000

hour

Annual sums of DNI [kWh/m²] for one site in Spain

Monthly sums of DNI [kWh/m²] for one site in SpainHourly DNI [Wh/m²] for one site in Spain

Results of the satellite-based solar assessment

Time series: for single sites, e.g. hourly, monthly or annual


Satellite data and nearest neighbour stations

Satellite derived data fit better to a selected

site than ground

measurements from a site

farther than 25 km away.

Source: DLR


Ground measurements vs. satellite derived data

Ground measurementsAdvantages+ high accuracy (depending on sensors)

+ high time resolution

Disadvantages- high costs for installation and O&M- soiling of the sensors- possible sensor failures- no possibility to gain data of the past

Satellite dataAdvantages+ spatial resolution+ long-term data (more than 20 years)

+ effectively no failures+ no soiling+ no ground site necessary+ low costs

Disadvantages- lower time resolution- low accuracy at high time resolution


Combining Ground and Satellite Assessments

• Satellite data– Long-term average– Year to year variability– Regional assessment

• Ground data– High Precision (if measurements taken thoroughly)– High temporal resolution possible

(up to 1 min to model transient effects)– Good distribution function– Site specific


Procedure for Matching Ground and Satellite Data

Satellite assessment

Groundmeasurements

ComparisonSeparation of clear sky

and cloud conditions

Recalculationwith alternative

atmospheric input

Selection of best atmospheric input

Recalculationwith alternative cloud transmission tables

Selection of best cloud

transmission table

Transfer MSG to MFGRecalculation of

long term time seriesBest fit

satellite data

clea

r sky

cor

rect

ion

clou

d co

rrec

tion


What you should care for in good Solar Resource Assessment


Procedure to Follow for Proper Solar ResourceAssessment

• Find a good location: close to site, safe, suitable for collocation of Weather Station

• Clarify the ground property conditions

• Check/define the budget for: instrumentation, maintenance and measurement related services

• Select the appropriate measurement equipment and provider(based on budget considerations, local conditions on site and maintenance possibilities)

• Find local maintenance personnel

• Prepare the measurement site according to the supplier’s specifications (foundations, fencing, etc.)

• Installation and commissioning of the measurement equipment

• Steady monitoring of the measurement data, duration minimum 1 year


Procedure to Follow for Proper Solar ResourceAssessment

• Documenting the selection of instruments

• Choosing a renowned company or institution to conduct or assist the measurement campaign

• Documenting sensor calibration with proper calibration certificates

• Meticolously documenting the instrument installation and alignment

• Performing and documenting regular sensor cleaning, maintenance and verification of alignment

• Cautiously and continuously checking data for errors and outliers

• Flagging suspect data, and applying corrections if possible, during and after the measurement campaign

• Stating and justifying the uncertainty estimate in a detailed report after the measurement campaign.


Delivery of hardware Installation & commissioning Operational supervision and

control Equipment monitoring

with inspection visits on site

Daily data retrieval via modem (GSM/GPRS)

Data collection and processing: • accuracy enhancement

(correction) • quality and functionality check• graphical visualization

Expert office

Daily, monthly, annual report withgood quality data

to client (via e-mail)

On site

Client

Usual Expert Service for Solar Resource Assessment


Quality Control of Measurement Data

Are values physically possible ?Measurement values must met physical limits

Are they reasonable?e.g. comparison to a clear sky model (Bird) or in kd-kt-space

Are they consistent?Comparison of redundant information

Visual inspection by an expert


Summary

• Knowledge of accurate irradiation data is indispensable for CSP projects(→ proper plant design, financial calculation, efficient plant operation)

• site selection, pre‐feasibility with satellite data

• colocation of a measurement station, taking care on thorough operation

• match long‐term satellite data with good quality measurement data from ground

• monitor the operating plant efficiency thoroughly with high‐precision data


Thank youvery much

for your attention!


Documents

Solar Resource Measurements and Satellite Data, Norbert Geuder