pv webinar

7/28/2019 pv webinar

1/27

Christian A. Gueymard


2/27

Interannual and long-term variability in DNI

Spatial variability in DNIDaily frequency distributionsTypical Meteorological Year (TMY), use and abuse

Resource assessment for large projects: localmeasurements are important!

Solar Resource Enhancement Factor (SREF)Circumsolar irradianceSpectral irradiance & SMARTSConclusions

Part 2Overview

For more information:

http://www.SolarConsultingServices.com


3/27

There are good years and bad years in everything, particularly 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 DNI Variability(1)

Eugene data: http://solardat.uoregon.edu/


4/27

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

This is significant at only a 66%

probability level. For a bankable

95% probability, double the COVresults.

Interannual DNI 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.


5/27

Only the past DNI resource can be known with some (relative) degree of

certainty. But the goal of CSP/CPV resource assessment is to obtain

projections of 2030 years into the future. Q: How can this be done if there areunknown forcings that 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 DNI Variability(1)

Early brightening Dimming Brightening

GHI, 19372006

Potsdam,Germany


6/27

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!


7/27

How

Most long-term variability results are for GHI. 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 DNI 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!


8/27

Main Causes Consequences


Cloud climatology Emissions of black

carbon (BC) and other

aerosols

Humidity patterns


9/27

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 DNI 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.


10/27

Most generally, daily frequency distributions are highly skewed. This

suggests a log-normal probability distribution, for instance. At high-DNI

sites, the most typical days of the year (modal value) provide much more

direct energy than the average (mean value) days of the year. This is

reversed at cloudy sites. Hence, the mean daily DNIshould not be the

only indicator to use when evaluating the potential of the solar resource.

Daily Frequency Distributions

0

2

4

6

8

10

12

14

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Daily frequencies

Alice Springs, avg 7.36 kWh/m2

Bermuda, avg. 3.76 kWh/m2

Frequency%

Daily DNI (kWh/m2)


11/27

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 used for DNI is relatively small.

It should not be construed that TMY3 is more advanced or better than TMY2!

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


12/27

Q:Are TMY data appropriate for CSP/CPV applications?

TMYs have some potential drawbacks: DNI in TMY data is 99% modeled. At clear sites, the TMY hourly distributions

usually show discrepancies above 500 W/m2, compared to measured data. This isdue 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 (see why in Pt. 1 of this webinar).

Non-typical low-DNI 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)


13/27

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 far from being a general rule.



14/27

An essential part of CSP/CPV

resource assessment!

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-termmeasurements should then be

used to correct long-term

satellite-based modeled data

using appropriate statistical

methods.

Local Measurements


15/27

Q: What is the average annual resource of CSP/CPV compared to that for

other solar technologies?For each type of concentrator, the Solar Resource Enhancement Factor

can help decide

Solar Resource Enhancement Factor (1)

C.A. Gueymard, Fixed or tracking solar collectors? Helping

the decision process with the Solar Resource Enhancement

Factor. SPIE Conf. #7046, 2008.


16/27

Latitude is not a good predictor for the

solar resource.Based on the 19611990 NSRDB

(excluding Alaska), the minimum U.S.

resource (measured by KT or Kn) is

found at Quillayute (northern

Washington state), whereas the

maximum is found at Daggett,California.

KT = GHI/ETHI

Kn = DNI/ETNI



17/27

Know your competition!

Flat-plate PV collectors on 2-axis trackers have a sizeable resourceadvantage over CSP/CPV.

With recent smart2-axis

trackers, the annual resource

for planar collectors may

increase another515%

(depending on cloudiness).This is severe competition


Plots based on the SREF method


18/27

Definition

Scattering is typically very strong around the sun, so the sky looks bright. This is

diffuse radiation that behaves like direct radiation, and can thus be concentrated.

Measurement

Circumsolar irradiance (CSI) is difficult to measure, but is possible with aspecially modified NIP. T.H. Jeys and L.L. Vant-Hull, Solar Energy18, 343-348, 1976.

Routine measurements of DNI actually include CSI within 2.52.9 of the

sun center. Such data slightly overestimate the true DNI that can be usedby CSP/CPV since their concentration ratio is high and the subtendedcone is smaller (usually


19/27

Modeling

The clear-sky CSI (up to 10) can be modeled with SMARTS, if the

atmospheric input data is available. Below 3, the CS effect is found

negligible under very clear conditions, but can represent up to 5% of DNI

under very hazy conditions.

Under thin cirrus clouds, the CS effect

becomes important, but its modeling is

then difficult.

A large collection of SAM

measurements would be needed to

develop simple empirical models.

We are now trying to make such a

research project possible, in collabo-

ration with SAMs manufacturer, as

well as U.S. and European institutions.

Circumsolar Irradiance (2)

C.A. Gueymard, Spectral circumsolar radiation contribution to CPV. Proc. CPV-6 Conf., Freiburg, 2010.

C.A. Gueymard, Solar Energy71, 325-346, 2001.

http://www.solarconsultingservices.com/smarts.php


20/27

Sun and Sky Radiance

The radiance of the suns disc is not constant

(limb darkening effect).

The circumsolar sky radiance decreases

exponentially with radial distance

The slope of this decrease increases with optical

depth (clear hazy thin cloud).


Linear scale

Logarithmic scale

Monument Valley

analogy

Logarithmic scale


21/27

Characteristics of CS irradiance

The CS effect is more pronounced at shorter wavelengths, since it is

caused by scattering The CS/DNI fraction increases linearly with the opening angle

It is also a function of air mass and optical depth (aerosol or cloud)

More results to be presented at the CPV-7 conference (2011).



22/27

The direct spectrum red shifts when air mass (AM) increases or when aerosol

turbidity (AOD) increases

Below 700 nm, atmospheric extinction is dominated by scattering Above 700 nm, it is dominated by absorption (water vapor, CO2)

Reference AM1.5 spectra have been standardized by ASTM: E891 (1987) and

G173 (2003). The latter was specially designed for CPV.

Spectral Irradiance (1)

C.A. Gueymard et al., Solar Energy73, 443-467, 2002.


23/27

Routine spectral measurements are difficult and costly

Spectral modeling is possible with various existing codes, e.g., SMARTS

SMARTS was used to develop ASTM G173 and other standards (IEC)

SMARTS is commonly used tool to evaluate spectral effects in PV and

CPV, and offers compatibility with current standards

All PV cells have a strong spectral selectivity. SMARTS can be used to

evaluate spectral mismatch correction factors, or the output of multijunction

(MJ) cells under variable spectral conditions.


MJ: 41% eff., for HCPV

c-Si: 22% eff., for LCPV

4 kW, 3 suns

JX Crystals


24/27

Daily-average direct spectrum:

Daily Spectral Enhancement Factor: DSEF


C.A. Gueymard, Daily spectral effects on concentrating PV solar cells as affected by realistic aerosol optical

depth and other atmospheric conditions. SPIE Conf. #7410, 2009.

A.L. Dobbin et al., How important is the resolution of atmospheric data in calculations of spectral irradianceand energy yield for (III-V) triple-junction cells? CPV-6 Conf., 2010.

Edn

= En( t)E

n(t)

t1

t2

/ En(t)t1

t2

= [Edn

1E

dnS

280

4000

d]/[Esn1

Esn

280

4000

Sd]


25/27

It is found that, for any type of solar cell, the spectral effect is a strong

function of AOD.

One goal of the current R&D is to fine tune MJ cells by optimizing their

bandgap combinations as a function of the regional average spectrum.

This might result in significant increases in the annual energy output.

Cirrus clouds appear to affect the performance of CPV modules, but it

is unclear if its because of spectral or circumsolar effect (or both).


G. Peharz et al., Evaluation of satellite cirrus data forperformance models of CPV modules. CPV-6 Conf.,

2010.


26/27

The DNI solar resource is highly variable and difficult to model using pastdata. Projecting it 2030 years into the future is even more difficult.

Local radiation measurements are still the best source of data, and arenecessary to derive the bankable data needed for big projects. However,

the type of radiometer should be selected properly, its limitations known,

and appropriate maintenance provided.

If local DNI measurements are available for only a short period (less than 5years), they should be used in conjunction with long-term modeled data toobtain locally adjusted time series spanning at least 10 years.

The use of TMY data is not recommended, particularly for a non-linearoperation (startup threshold). In that case, sub-hourly time series are ideal.

Circumsolar and spectral effects have second-order importance, but shouldstill be studied for better simulation, and possible fine tuning of CPV cells.

The benefit of a larger circumsolar contribution to LCPV systems cannot beevaluated yet.

Because of the lack of high-quality measured DNI data in the publicdomain, the science of resource assessment progresses only slowly.

Conclusions (2)


27/27

Thank you!

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