Replicating SAM production projections in spreadsheet

Since 2005, we've provided a spreadsheet to solar professionals for electric bill modeling (called "The OnGrid Tool"). We're now working to improve the spreadsheet's backend solar production projections and would appreciate your advice.

The spreadsheet currently has kWh/m²/day data for the 239 TMY2 locations (based on the Redbook). It also has hourly distributions for different azimuth/tilt combinations at about 15 different nationally-distributed TMY2 sites, which we got from PVWatts 1.

We recently ran a SAM script to get hourly distributions for all 239 locations. This is a very large dataset and somewhat bloats the file. Is this the best approach? Is it possible to derive production for a given location/azimuth/tilt with just the TMY2 file data and an elegant formula? Or something like that. I'm interested in the SDK option, but would prefer to stay purely in Excel for now to keep this relatively simple.

I really appreciate any thoughts on this.

I'd really appreciate hearing back on this. Please let me know if this isn't an appropriate way to use SAM.

Hi Michael,

I do not know of a way to estimate hourly production distribution for a PV system using only weather data from a TMY2 file and a simple equation. That kind of approach works better for annual or monthly estimates, where you just need a very rough estimate of a system's total annual output. For hourly predictions with a minimum of calculations, I think the PVWatts hourly simulation is a good way to go.

In the latest update to SAM 2016.3.14 (Revision 4), we snuck in a new capability that might be hepful for you. On the case menu, you can use the new "Generate code" command to create VBA code that will run SAM from an Excel workbook. That would allow you to run SAM directly from your workbook. You could even dynamically download a weather file from the NSRDB database for a specific location. This approach would eliminate the need to maintain a large database.

Best regards,
Paul.

Got it Paul, thanks so much!