1. Solar Radiation Measurement, Modeling, and Performance Prediction Prepared for Solar Minnesota by Darryl Thayer

2. Solar Performance
Simply put, solar PV performance is predicted by using the data from TMY 2 or TMY 3.
All approaches use the principals of the PV photo response and apply in either an empirical method (PVWatts, Retscreen, Pathfinder) or simulation routine (SAM, Homer, PVsys, PVsol, Polysun).
But all are based on TMY Data Files (Typical Meteorological Year)

3. What is TMY? The term TMY is from Typical Meteorological Year.
It is a data file consisting of a full year of data constructed from twelve months chosen as most typical months from all the years that made up the data file.
Example March maybe from 1973, April from 1989 etc.
Because March 1973 is the most typical March of all Marchs in the data file.

4. TMY files Where do the TMY’s Data Files come from?
TMY(1) 239 stations some with measured data shown here using stars.
TMY 2-3 are 1,456 locations, of differing quality. There are 40 stations with actual solar radiation measurements indicated by circles

5. TMY File data points

6. TMY Files What was considered in selecting the most typical months?
The original TMY(1) data files were created by Sandia National Laboratory using a method in which a typical month was selected based on nine daily indices.
(such as max, min, temp, humidity, mean wind velocity; and the total global horizontal irradiance.)
The data set used measured and calculated solar data for 239 cities, compiled from the period 1952 to 1975.
The original TMY data files were created from measured GHI SOLMET data and modeled ERSATZ data.

7. Why was TMY1 created?
The reason the TMY(1) was created was for building science, to predict building performance where outdoor temp, humidity, wind speed and solar radiation play the major roles.

8. Why was TMY2 created?
TMY 2 files were created to better serve the solar thermal industry.
TMY2 data files were created from the 1961 to 1990 NSRDB where 93% of the values were modeled data.
However the weighting of daily indices were changed and DNI (Direct Normal Irradiation) was added to reflect the greater importance of solar radiation.
The months May 1982 through December 1984 were excluded because of the eruption of El Chichón, Mexico.
Climatologist require a 30 year data set for significance.
The direct normal irradiance (DNI) was added to the weighting indices.

9. Why was TMY3 created?
TMY 3 files use the same daily indices as TMY 2 but are from 1991 to 2005 data set.
The months of June 1991 to December 1994 were excluded because of the eruption of Mount Pinatubo, Philippines.
As a result of the exclusion, the TMY3 files were derived using 11.5 years of data.

10. TMY 3.1 data
TMY 3.1 files soon to be released (July 1) add to the TMY 3 files the years of 2006-( ?)
This data is resulting in recalculation of TMY in some cases the selected month will change.
This expands the record, and includes more recent data.
The 3.1 data will be accessible on the NREL web site
(A recent report indicates Minnesota has warmed 2.5 degrees in the last 40 years.)

11. Table of Daily Indices for determining “Typical”:
TMY (1) %
TMY (2-3) %
Max Temp DB 7% 5%
Min Temp DB
Mean Temp DB
15%
10%
Min Dew Pt
Max Dew Pt
Mean Dew Pt
14%
Max Wind Spd
Mean Wind Spd
GHI (global horiz)
25%
DNI (direct normal) 0%

12. Summary of above table Indices TMY(1) TMY 2-3 DB Temp 28% 20% Dew Pt
Wind
10%
Solar
14%
50%

13. Limitations of the TMY2 and TMY3 Files
The TMY files were created to represent typical meteorological years and not typical solar years.
Due to the limited number of years in TMY3 data files, there is no guarantee that the TMY3 file will be an accurate representation of the average GHI or DNI for the entire historical data set.
Examples where the GHI and DNI TMY annual average are different from the average are shown in Figs. 1-2.

14. An Example:
At Groton-New London, CT, the annual TMY GHI is below the yearly average GHI for every single year in the NSRDB.
A moving average was used and no 12-month period has an annual GHI as low as the TMY3 file.

16. TMY 3 over predicts

17. Eppley Black & White Pyranometer

18. Licor Silicon Radiometer

19. Comparison testing

20. Eppley NIP

21. Rotating Shadowband Radiometer

22. Rotating Shadowband Radiometer

23. Licor

24. Satellite Data
Starting 1998 the Data from the GOES Weather Satellite was available.
The Satellite data is measured once per hour, on a 0.1 degree grid.
This is about 100,000 data points, across the US and is available at NREL and SUNY and NCDC
Satellite data is best near ground based stations.
Modeling selects the same TMY 3 months as the ground based stations.

25. Solar Radiation Modeling
So far all we have done is build a TMY set of GHI and DNI
Data that reflects the solar radiation on a horizontal surface.
Of course we want the solar radiation on a tilted surface
Richard Perez developed the standard technique and is accepted world wide, for the calculation of radiation on other surfaces.

26. Emperical based programs
Programs like RetScreen, Pathfinder, Suneye Companion, take TMY 3 GHI and DNI and complied a set of numbers for each month for each location.
A set of 5 numbers for each month for 1000 locations is only 60,000 data points
This set of numbers allows reconstruction of monthly radiation and application of the Pérez model for change of orientation to selected orentation

27. Simulation Based Programs
Programs like PV designer, Homer, SAM, Polysun etc. go back to the TMY 3 data set and recover the hourly data
These programs take the Hourly GHI and DNI and apply the Perez model to determine the radiation on a tilted surface.
The value of returning to the TMY-3 hourly is not for total annual performance, but for timing critical performance
Shading analysis
Time of Day pricing
Time critical LOL calculations
Demand reduction
Utility interconnect considerations

28. How to test for module and array performance
Because TMY-3 data is meant to be Typical Year data and is complied over 11.5 years….
A one month set of averages is not representative of the record. (energy collected over one month, is just that and not indicative of a long term average
Even a One year record is not indicative of a long term record.
So is there a way to test for Solar performance to compare with expected performance?

29. Testing to determine system performance
Since most of the radiation is DNI most of the comparison should be on DNI
Suggested Methods:
Measure on clear days
Compare with a standard source
Such as radiometer at the same angle as your collectors
Compare with DC value .
Caveats
The sun is not 1000 Watts / M^2
The sun varies over time differently a different locations
Correct for Cos Angle factor if arrays are at different angles.

30. Example of measured Performance

31. Minneapolis, MN