Post on 06-Apr-2018
transcript
8/3/2019 Gueymard-Variability 3Tier Webinar Web 2010
1/15
Christian A. Gueymard
8/3/2019 Gueymard-Variability 3Tier Webinar Web 2010
2/15
Direct normal irradiance (DNI) global horizontal irradiance (GHI) vary
smoothly under clear skies, but can vary extremely fast under partly cloudy
skies, e.g., from 0 to 1000 W/m2 in a second for DNI.
These fast transient conditions did not show easily in the past, when only
hourly data were available. Time steps of 1-min become the norm for first-
class stations. Some research stations use 1-sec to 5-sec time steps.
ExampleTypical partly-cloudy day
for Oahu, HI
GHI up to 25% more than
ETHI* during lensing
effect peaks, around noon. DNI also increases by a
few %, due to large transientcircumsolar diffuse.
* Extraterrestrial horizontal
irradiance
Short-term Variability(1)
8/3/2019 Gueymard-Variability 3Tier Webinar Web 2010
3/15
Short-term variability may be a problem for PV/CPV due to peaks in power
output that need to be absorbed. CSP plants cant usually operate below
some thresholdDNI. Q: Can these transient effects be correctly accounted
for in the daily, monthly or annual solar resource if the irradiance is not
measured fast enough?
Example
Typical day in Oahu, HI
Various thresholds:
0, 100, 200 and 300 W/m2
Various measurementtime steps considered:
3 sec, 1 min, 15 min, 1 hr
Hourly time step may be
too coarse for accuratesystem simulation
No gain in accuracy
likely for steps < 1 min This topic needs further
research, toward the
definition of an optimum measurement (or modeled) data time step.
Short-term Variability(2)
0.85
0.9
0.95
1
1 100
Oahu, Hawaii5 July 2010
Relativedailyirradiation
Time step (sec)
3 sec 1 min 15 min 1 hr
Threshold (W/m2)
0100200
300
Total DNI: 7 kWh/m2
8/3/2019 Gueymard-Variability 3Tier Webinar Web 2010
4/15
There are good years and bad years in everything, like in GHI, and even more so in
DNI, due to: Climate cycles (El Nio, La Nia), changes in release of natural
aerosols, increase or decrease in pollution, volcanic eruptions, climate change
For GHI, it might take only 23 years of measurement to be within 5% of the long-term mean. For DNI, it takes much longer, up to 515 years.
Short measurement periods (e.g. 1 year) are not sufficient for serious DNI resource
assessment!
Special techniques must be used to
correct long-term modeleddata usingshort-term measureddata.
Interannual Variability(1)
Eugene data: http://solardat.uoregon.edu/
8/3/2019 Gueymard-Variability 3Tier Webinar Web 2010
5/15
Interannual variability in DNI is much higher (at least double) than that in
GHI. This variability is higher in cloudier climates (low Kn), but still
significant in clearer regions (high Kn), which are targeted by CSP/CPV.
Plots and maps provide this variability in terms ofCoefficient of Variation
(COV): COV = St. Dev. / Mean
Interannual Variability(2)
http://rredc.nrel.gov/solar/new_data/variability
S. Wilcox and C.A. Gueymard, Spatial and temporal
variability in the solar resource in the United States.
ASES Conf., 2010.
C.A. Gueymard, Fixed or tracking solar collectors?
Helping the decision process with the Solar Resource
Enhancement Factor. SPIE Conf. #7046, 2008.
Kn = DNI/ETNI
This is significant at only a 66% probability
level. For a bankable 95% probability,double the COV results.
8/3/2019 Gueymard-Variability 3Tier Webinar Web 2010
6/15
Only the past solar resource can be known with some (relative) degree of
certainty. But the goal of resource assessment is to obtain projections of 2030
years into the future. Q: How can this be done if there are unknown forcingsthat result in long-term trends?
Only a handful of stations in the world have measured radiation for more than
50 years. Long-term trends in GHI and DNI have been detected. Periods of
Brightening and Dimming are now documented.
Long-term Variability(1)
Early brightening Dimming Brightening
GHI, 19372006
Potsdam,Germany
8/3/2019 Gueymard-Variability 3Tier Webinar Web 2010
7/15
Long-term trends do not affect the world equally.
Current results indicate a brightening in most of the
NH, and a dimming in the tropical regions of theNH and SH. India and China are directly affected,
most probably because of the current increase in
coal burning and pollution (Asian Brown Cloud).
Long-term Variability(2)
M. Wild et al., J. Geophys. Res. 114D, doi:10.1029/2008JD011382, 2009
M. Wild, J. Geophys. Res. 114D, doi:10.1029/2008JD011470, 2009
Trends in GHI
(% per decade)
Good news in some
areas,bad news in others!
8/3/2019 Gueymard-Variability 3Tier Webinar Web 2010
8/15
How
Most long-term variability results are for GHI (because most available data).
One difficulty is to transform these results into DNI variability. There are
regions where DNI varies more than GHI, others where the reverse occurs.
Long-term Variability(3)
L.D. Riihimaki et al., J. Geophys. Res. 114D, doi:10.1029/2008JD010970, 2009
How the resource will vary during the next 2030
years depends on many unknowns: Air quality regulations and Kyoto-type accords
Climate change evolution Possible geoengineering (forced dimming)
Volcanic eruptions, etc.
So nobody has a definite answer!
8/3/2019 Gueymard-Variability 3Tier Webinar Web 2010
9/15
Main Causes Consequences
Long-term Variability(4)
Cloud climatology Emissions of black
carbon (BC) and other
aerosols
Humidity patterns
8/3/2019 Gueymard-Variability 3Tier Webinar Web 2010
10/15
Spatial variability is important for two reasons:
In regions of low spatial variability, use of low-res resource maps (e.g.,
100x100 km) might be OK, at least for preliminary design. Conversely, in
regions of high spatial variability, only hi-res maps (10x10 km or better)
should be used.
If variability is high, measured data from only nearby weather stations
should be trusted.
Spatial Variability
5x5 matrix
10x10 km grid cells
S. Wilcox and C.A. Gueymard, Spatial and temporal
variability in the solar resource in the United States.
ASES Conf., 2010.
8/3/2019 Gueymard-Variability 3Tier Webinar Web 2010
11/15
For decades, TMYs have been used by engineers to simulate solar systems or
building energy performance. TMYs conveniently replace
30 years of datawith a single typical year. Models of solar system power output prediction(e.g., PVWatts, http://www.nrel.gov/rredc/pvwatts/) or of performance and
economic estimates to help decision making (e.g., Solar Advisor Model,
https://www.nrel.gov/analysis/sam/) still rely heavily on TMY-type data.
To define each of the 12 months of a synthetic year, TMYs use weighting
factors to select the most typical year among a long series of available data(including modeled irradiance). In the U.S., three different series of TMY files
have been produced. The weight they all use for DNI is relatively small.
It should not be construed that TMY3 is more advanced or better than TMY2!
TMY data for
the U.S.
Typical Meteorological YearTMY(1)
TMY TMY2 TMY3
Period 19521975 1961-1990 (i) 19762005(ii) 19912005
GHI weight 12/24 5/20 5/20
DNI weight 0 5/20 5/20
# Stations 222 239 (i) 239(ii)
950
8/3/2019 Gueymard-Variability 3Tier Webinar Web 2010
12/15
Q:Are TMY data appropriate for all solar applications?
TMYs have some potential drawbacks: Solar TMY data is 100% modeled. At clear sites, the TMY2/TMY3 hourly
distributions usually show discrepancies above 500 W/m2, compared to measureddata. This is due to the use of climatological monthly values (rather than discrete
daily values) for the aerosol data.
Hourly values are used. This may not be ideal for non-linear systems with
thresholds above 150 W/m2 (due to short-term variability).
Non-typical bad years are excluded from the data pool. Using TMYs for riskassessment is risky.
Typical Meteorological YearTMY(2)
Hourly frequencies of
19912005 NSRDB data used
to obtain TMY3 for Golden, CO.Compared to measurements,
note the NSRDB and TMY3
overestimations below
900 W/m2, and
underestimations above.
0
4
8
12
16
20
0 100 200 300 400 500 600 700 800 900 1000 1100
Golden, COSunup hourly frequencies
Measured
NSRDB
TMY3
Frequen
cy%
DNI bins (W/m2)
8/3/2019 Gueymard-Variability 3Tier Webinar Web 2010
13/15
To obtain bankable data, the use of TMYs is inappropriate. The risk of bad
years cannot be assessed correctly. TMY may seriously overestimate the P90
exceedance probability. Example: For Boulder, the total annual DNI from TMY2happens to correspond to P50, but this is probably not a general rule.
Typical Meteorological YearTMY(3)
8/3/2019 Gueymard-Variability 3Tier Webinar Web 2010
14/15
Critical part of solar resource
assessment, necessary to sort
out local variability effects at
different time scales!
Two types of weather stations,
depending on radiometer
technology.
Minimum measurement period
recommended: 1 year.
Performance and prices vary
[Ask us for more details and
custom solutions!]
These short-term
measurements should then be
used to correct long-term
satellite-based modeled data
using appropriate statistical
methods.
Local Measurements
8/3/2019 Gueymard-Variability 3Tier Webinar Web 2010
15/15
Thank you!
http://www.SolarConsultingServices.com