Available for download at http://solarreference.com/solar-resource-assessment-how-to-get-bankable-meteo-data/ This presentation from DLR (German Aerospace Center) explains. 1. Solar radiation data characteristics 2. How radiation data is gathered from ground measurements and derived from satellite data 3. Comparison of the two, and some important factors to be weighed in when deciding what to use This presentation can also be downloaded at NREL

1. How to get bankable meteo data?
DLR solar resource assessment
Robert Pitz-Paal,
Norbert Geuder, Carsten Hoyer-Klick, Christoph Schillings

2. Solar resource assessment for solar power plants
Why solar resource assessment ?
Characteristics of solar irradiation data
DLR solar resource assessment:
ground measurements
satellite data
How to get bankable meteo data?
Summary
Slide 2 > Solar resource assessment at DLR

3. Why is exact knowledge of the solar resource
so important?
High impact of the annual
50 MW Parabolic Trough solar thermal power plant
Irradiation is a
crucial parameter for
site selection and
plant design and
economics of plant
Levelized Electricity Costs (LEC)
Annual electricity generation in GW
on the LEC
Mean system efficiency
Annual electricity generation
Mean system efficiency and
Levelized Electricity Costs in €/kWh
irradiation
Solar field size (aperture) in 1000 m²
Why solar resource assessment?
Slide 3 > Solar resource assessment at DLR

4. Characteristics of solar irradiation data
What kind of irradiation data is needed?
Type:
DNI (Direct-Normal Irradiation)
GHI (Global-Horizontal Irradiation)
DHI (Diffus-Horizontal Irradiation)
Source:
ground measurements
satellite data
Properties of irradiation:
spatial variability
inter annual variability
long-term drifts
Characteristics of solar irradiation data
Slide 4 > Solar resource assessment at DLR

5. Ground measurements vs. satellite derived data
Ground measurements
Satellite data
Advantages
Advantages
+ high accuracy (depending on sensors)
+ spatial resolution
+ high time resolution
+ long-term data (more than 20 years)
+ effectively no failures
+ no soiling
+ no ground site necessary
+ low costs
Disadvantages
- high costs for installation and O&M
Disadvantages
- soiling of the sensors
- lower time resolution
- sometimes sensor failure
- low accuracy in particular at high time
- no possibility to gain data of the past
resolution
Characteristics of solar irradiation data
Slide 5 > Solar resource assessment at DLR

6. Inter annual variability
Strong inter annual and regional
variations
1999
deviation
to mean
2000
kWh/m²a
2001
Average of the direct normal
irradiance from 1999-2003
2002
2003
Characteristics of solar irradiation data
Slide 6 > Solar resource assessment at DLR

7. Long-term variability of solar irradiance
7 to 10 years of measurement to get long-term mean within 5%
If you have only one year of
measured data, banks have to
assume, that this it is here
Characteristics of solar irradiation data
Slide 7 > Solar resource assessment at DLR

8. Time series of annual direct normal irradiance
Long-term tendency
may exist and
differs from site to site
linear regression:
+2.16 W/(m² year)
linear regression:
-0.93 W/(m² year)
Spain
Australia
with stratospheric aerosol
Characteristics of solar irradiation data
Slide 8 > Solar resource assessment at DLR

9. How to get bankable Meteo Data?
Quality checked ground
measurements to gain
highly accurate data
Derivation of irradiation from satellite
data to get
ƒ
spatial distribution and
ƒ
long-term time series
Validation of the satellite data with accurate
ground data
Result: accurate hourly time series, irradiation maps and long-term annual mean
DLR solar resource assessment
Slide 9 > Solar resource assessment at DLR

10. Instrumentation for unattended abroad sites:
Rotating Shadowband Pyranometer (RSP)
pyra
nom
eter
sen
sor
sh
ad
ow
Sensor: Si photodiode
ba
nd
Advantages:
+ fairly acquisition costs
+ small maintenance costs
+ low susceptibility for soiling
+ low power supply
Disadvantage:
diffus
horizontal
Global HorizontaI Irradiation (GHI)
measurement
- special correction for good accuracy
necessary
Ground measurements
(established by DLR)
Slide 10 > Solar resource assessment at DLR

11. Precise sensors (also for calibration of RSP):
GHI
Thermal sensors:
pyranometer and pyrheliometer,
precise 2-axis tracking
DNI
DHI
Advantage:
+ high accuracy
+ separate GHI, DNI and DHI sensors
(cross-check through redundant measurements)
Disadvantages:
- high acquisition and O&M costs
- high susceptibility for soiling
- high power supply
Ground measurements
Slide 11 > Solar resource assessment at DLR

12. Satellite data: SOLEMI – Solar Energy Mining
Meteosat Prime
Meteosat East
SOLEMI is a service for high resolution and high quality data
Coverage: Meteosat Prime up to 22 years, Meteosat East 10 years (in 2008)
Satellite data
Slide 12 > Solar resource assessment at DLR

13. Radiative Transfer in the Atmosphere
1400
Extraterrestrial
O2 and CO2
1000
Ozone
800
Rayleigh
600
Water Vapor
400
Aerosol
200
Clouds
00:00
22:00
20:00
18:00
16:00
14:00
12:00
10:00
08:00
06:00
04:00
02:00
0
00:00
Direct Normal Irradiation
(W/m²)
1200
Hour of Day
Satellite data
Slide 13 > Solar resource assessment at DLR

14. How two derive irradiance data from satellites
The Meteosat
satellite is located in
a geostationary orbit
The satellite scans
the earth line by line
every half hour
Satellite data
Slide 14 > Solar resource assessment at DLR

15. How two derive irradiance data from satellites
The Meteosat
satellite is located in
a geostationary orbit
The satellite scans
the earth line by line
every half hour
The earth is scanned
in the visible …
Satellite data
Slide 15 > Solar resource assessment at DLR

16. How two derive irradiance data from satellites
The Meteosat
satellite is located in
a geostationary orbit
The satellite scans
the earth line by line
every half hour
The earth is scanned
in the visible and
infra red spectrum
Satellite data
Slide 16 > Solar resource assessment at DLR

17. How two derive irradiance data from satellites
The Meteosat
satellite is located in
a geostationary orbit
The satellite scans
the earth line by line
every half hour
The earth is scanned
in the visible and
infra red spectrum
A cloud index is
composed from the
two channels
Satellite data
Slide 17 > Solar resource assessment at DLR

18. How two derive irradiance data from satellites
Atmospheric
transmission is
calculated from
global data sets
elevation
Satellite data
Slide 18 > Solar resource assessment at DLR

19. How two derive irradiance data from satellites
Atmospheric
transmission is
calculated from
global data sets
elevation
ozone
Satellite data
Slide 19 > Solar resource assessment at DLR

20. How two derive irradiance data from satellites
Atmospheric
transmission is
calculated from
global data sets
elevation
ozone
water vapor
Satellite data
Slide 20 > Solar resource assessment at DLR

21. How two derive irradiance data from satellites
Atmospheric
transmission is
calculated from
global data sets
elevation
ozone
water vapor
aerosols
Satellite data
Slide 21 > Solar resource assessment at DLR

22. How two derive irradiance data from satellites
Atmospheric
transmission is
calculated from
global data sets
elevation
ozone
water vapor
aerosols
The cloud index is
added for cloud
transmission
Satellite data
Slide 22 > Solar resource assessment at DLR

23. How two derive irradiance data from satellites
Atmospheric
transmission is
calculated from
global data sets
elevation
ozone
water vapor
aerosols
The cloud index is
added for cloud
transmission
All components are
cut out to the region
of interest
Satellite data
Slide 23 > Solar resource assessment at DLR

24. How two derive irradiance data from satellites
Finally the irradiance
is calculated for
every hour
Satellite data
Slide 24 > Solar resource assessment at DLR

25. Example for hourly time series
for Plataforma Solar de Almería (Spain)
1200
ground
1000
satellite
W/m²
800
600
400
200
0
13
14
15
16
17
18
day in march, 2001
Validation of the data
Slide 25 > Solar resource assessment at DLR

26. Comparing ground and satellite data: time scales
12:45 13:00 13:15 13:30 13:45 14:00 14:15
Hourly average
Meteosat image
Measurement
Ground measurements are typically
pin point measurements which are
temporally integrated
Hi-res satellite pixel in Europe
Satellite measurements are
instantaneous spatial averages
Hourly values are calculated from
temporal and spatial averaging
(cloud movement)
Slide 26 > Solar resource assessment at DLR

27. Comparing ground and satellite data: “sensor size”
solar thermal
power plant
(200MW ≈ 2x2
km²
satellite pixel
(∼ 3x4 km²)
ground
measurement
instrument
(∼2x2 cm²)
Slide 27 > Solar resource assessment at DLR

28. Comparison with ground measurements and accuracy
general difficulties: point versus area and
time integrated versus area integrated
1200
ground
sate llite
1000
W/m²
800
600
400
200
DNI time serie for 1.11.2001, Almería
Partially cloudy conditions, cumulus humilis
0
0
6
12
18
Slide 28 > Solar resource assessment at DLR
hour of day
24

29. Comparison with ground measurements and accuracy
general difficulties: point versus area and
time integrated versus area integrated
1200
ground
sate llite
1000
W/m²
800
600
400
200
DNI time serie for 30.4.2000, Almería
overcast conditions, strato cumulus
0
0
6
12
18
Slide 29 > Solar resource assessment at DLR
hour of day
24

30. Validation in Tabouk, Saudi Arabia
clear-sky
all-sky
Clear sky only
All Sky
Validation of the data

31. Validation results
RMSE (Root Mean Square Error)
Decreasing deviation between
ground and satellite derived
data with increasing duration
of the integration time
Site
Bias
Variing within different sites
bias
Measurement Network Saudi Arabia
Several Sites in Spain
Morocco
Algeria
+4.3%
-6.4% to +0.7%
-2.0%
-4.8%
Validation of the data
Slide 31 > Solar resource assessment at DLR

32. Monthly ground and satellite derived irradiation data
hourly monthly mean (DNI_ground) in Wh/m², Solar Village 2000
hourly monthly mean (DNI_satellite) in Wh/m², Solar Village 2000
Validation of the data
Slide 32 > Solar resource assessment at DLR

33. Adjusting Ground and Satellite Data
Simple Method: Scaling with the Bias
E.g. with a Bias of -5%, every value is multiplied with 1.05
+ Very easy to apply
Modification of frequency distribution at the extreme end
a factor > 1 may produce unrealistic high values
a factor < 1 may omit high values
Suitable for average values
Slide 33 > Solar resource assessment at DLR

34. Adjusting Ground and Satellite Data
Advanced Method: Error analysis and correction functions
Analysis of the Deviations:
At clear sky? (e.g. due to incorrect atmospheric data)
During cloud situations? (e.g. incorrect cloud modelling)
Development of a correction function dependent e.g. on the cloud
index.
7
c(x)
6
Korrekturfaktor
5
4
3
2
1
0
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
Wolkenindex
Bias before
Correction
Bias after
Slide 34 > Solar resource assessment at DLR

35. Summary
Ground measurements are accurate but expensive and in suitable regions
mostly rare (especially DNI)
New ground measurements do not deliver time series for the past
Satellite data offers spatial resolution and long-term time series of
more than 20 years into the past
Combination of ground and satellite date yields: irradiation maps,
long term annual means and time series – all with good accuracy
Realistic long term meteo data helps to avoid risk surcharges of banks due to
conservative assumptions and increase the financial viability of your project
Summary
Slide 35 > Solar resource assessment at DLR

36. Thank you for your attention !
Acknowledgements
Richard Meyer, Institute for Physics of the Atmosphere,
DLR Oberpfaffenhofen
Sina Lohmann, Institute for Physics of the atmosphere,
DLR Oberpfaffenhofen
Antonie Zelenka, MeteoSwiss, Zürich
Volker Quaschning, FHTW Berlin
Acknowledgements
Slide 36 > Solar resource assessment at DLR

37. Slide 37 > Solar resource assessment at DLR

38. 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.
Validation of the data
Slide 38 > Solar resource assessment at DLR

39. Derivation of the Cloud-Index from satellite images
VIS-channel
(0.5 – 0.9µm)
IR-channel
(10.5 –12.5 µm)
Original-image
Meteosat-7
Reference-image
Derived cloud
information
(Cloud-Index)

40. Time series of direct normal irradiance
linear regression:
2.16 W/(m² year)
1.56 W/(m² year)
aerosol opt. thickness
Pinatubo
El Chichon
with
without stratospheric aerosol
Pinatubo
El Chichon
aerosol optical thickness
Spain
with
without
stratospheric aerosol
Australia
linear regression:
- 0.93 W/(m² year)
- 1.22 W/(m² year)
Slide 40 > Solar resource assessment at DLR