gueymard-variability 3tier webinar web 2010

8/3/2019 Gueymard-Variability 3Tier Webinar Web 2010

1/15

Christian A. Gueymard


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)


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


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/


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.


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


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).


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!


8/15

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.


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!


9/15

Main Causes Consequences


Cloud climatology Emissions of black

carbon (BC) and other

aerosols

Humidity patterns


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.


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


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.


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)


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.



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


15/15

Thank you!

http://www.SolarConsultingServices.com

gueymard-variability 3tier webinar web 2010

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