This is probably the easiest case to understand. We just treat solar like any other energy savings technology.
We take the pre-retrofit bills that did not use solar and do a regression on these bills.
Baseline kWh = Fit line of Bill kWh
Since there is no solar PV system in the base year, no kWh is contributed to the grid by the nonexistent solar PV system, so:
Bill kWh = kWh contributed by grid to building = Building kWh
We do not need to add a variable for solar insolation or percentage sunlight, because it was not an issue during the base year. There will likely be no relationship between this variable and energy usage during the base year, so the variable cannot be used.
For Actual data, we want just the energy provided by the grid, which is less than the building uses. The solar makes up the rest:
Bill kWh = kWh contributed by grid to building – kWh contributed to grid by solar PV system
Bill kWh = Grid kWh – Solar to Grid kWh
Actual kWh = Bill kWh
kWh Savings = Baseline kWh – Actual kWh
kWh Savings = Fit line of Bill kWh – Bill kWh
To do this in Metrix or Option C
- Do a regression of base year Bill kWh
- For post retrofit bills, enter in NET kWh supplied by utility to the building. That means kWh provided by utility minus kWh provided by solar PV system to the grid. I have defined this as Bill kWh.
- So basically, you are just using Bill kWh for pre and post.
In this example, we are given 2 days of usage in a month. In day 1, the solar produces more than the building uses. In day 2, the building uses more than the solar produces.
|Day||Fit line of Bill kWh||Solar Produced kWh||kWh Used at Building
(Bldg kWh = Grid kWh)
|Net kWh Supplied to Building
|ESP Savings NOT Including Solar
|ESP Savings Including Solar
Given the numbers in this table, we want our savings results to match the last column or 145 kWh.
Savings = Baseline kWh – Actual kWh
Savings = Fit line of Bill kWh – Bill kWh
Savings = 185 kWh – 40 kWh = 145 kWh
How to Handle Demand
You would treat demand exactly as you treat energy. So:
Savings = Baseline kW – Actual kW
Savings = Fit line of Bill kW – Bill kW
Another Way to Handle Solar
One problem associated with the method I described above is that your solar savings are going to be subject to changes in weather. As there was no solar during the base year, we could not use a variable to vary expected solar production. If a volcano blows up, you experience a hellish year of forest fires, or you have an unusually overcast summer, your solar system will not provide the kWh you expected. Assuming you only guaranteed 85% or 90% of what you expected the solar to provide, everything should work out. But what if the variation in solar production is greater than the 10% to 15% cushion you allowed yourself?
In general, over the life of a performance contract, weather fluctuations should even out. So you can make a good argument for not addressing the variation of solar production due to weather.
However, if you want your savings numbers to not be at the mercy of weather, but only subject to how well the project performs, you could introduce a variable for solar.
There was no solar in the base year, but there is in the performance year.
You can do a regression of solar production to solar insolation for the performance year. (You will have to do this again for every year of the performance period.) Then you apply TMY weather data to your regression equation. The result will be, “This is how much solar production we would have had in a typical year with the equipment as it is now running.”
This method is fair. But there is an implicit assumption that TMY data is accurate, and there have been studies that show that TMY data seems to change every decade.
You are measuring the solar system’s production each year. If you do this, then you would have to measure Building kWh at the building, which is Bill kWh during the base year, and it is sub-metered Building kWh during the performance period. You would have to track savings for all ECMs except for solar using this method, and then add in the solar savings using the method we are speaking of now. This can get complicated.
If you were to use Option C software, you can model this as follows:
- Add meter with baseline = 0. That is the solar production meter.
- A meter with performance period data for solar production. Enter it positive.
- You would enter a variable for solar insolation or solar irradiance
- Each year during the performance period, you would do a regression of the performance year solar production using your variable.
- You would import TMY data
- And provide a report of savings, Baseline – Actual, where Baseline = 0, and Actual is the solar production as estimated by the regression equation which was created with performance year data.
The Option C software will likely not be able to make a report that shows savings using this 2-regression method (for solar) and the traditional 1-regression method (for the other ECMs) together. You will most likely have to export the results from the software into Excel to make a report that handles both the solar and the rest of the facility.