How to select the best solar panel based on degradation rates: a spreadsheet approach
April 27, 2022
By
Lahiru Sendapperuma
Panel degradation: a quick review
Total panel efficiency is measured under standard test conditions (STC):
Based on a cell temperature of 25°C
Solar irradiance of 1000W/m2
Air Mass of 1.5.
The efficiency (%) of a panel is calculated by the maximum power rating (W) at STC, divided by the total panel area in metres.
The are various measures to determine the best panel for you that include:
Total production of the panel over its designed lifetime which relates to
The initial output degradation of the panel after the first year and
The total degradation of the panel over the designed lifetime
Panel degradation: a spreadsheet approach
In this presentation we will look at a range of panels and:
Look at their temperature coefficients
Put this information into a spreadsheet
And based on certain system sizes, compare a range of panels and look at their outputs over time.
The Data
So the area of the panel is a simple calculation, length x width
Watts per metre is panel wattage/area
Efficiency is watts per m2/1000
Data: panel degradation
With all solar panels there is degradation in output:
A certain percentage reduction in output in the first year
This is usually between 2 - 3% in most cases
Then every year after that, a smaller consistent reduction usually from 0.2- 0.6%
When looking at solar panel design many factors have to be looked at, all based around the economic reality of the proposal you are presenting:
Price per watt of the panels you are considering
The overall efficiency, really important if space is at a premium
The actual output over the lifetime ( we will look at 25 years) of the panels and this relates to degradation.
First steps
As this is a spreadsheet, we need to setup it up:
First thing is set a system price per watt ex GST
Then the ability to select a panel from your data set
Also need to put in how many panels
System price per watt: in this case have put as $0.9
Select panel: this uses data validation to access a list of panels in your data set
Panel model: here I have used an X lookup but a V lookup can also be used
Panel wattage references the model selected
No. of panels is user input
Total kW is a simple calculation
As is total system price
More information is needed
To completely determine the best panel need more information:
Average output per kW installed: user input with assumptions made concerning pitch, orientation and location
Average output per day in kWh: a simple calculation referencing total kWh x average output per kW installed
Output after one year no panel degradation: average output per kW installed x 365
Pricing for electricity import and export
Need to set some other parameters:
Price of electricity in the first year: user input
Price of electricity exported fixed: in reality this will probably decrease
Increase in price of electricity per year from the grid: user input
Percentage of solar consumed by the load and what goes to the grid
Now the nitty gritty
Now looking at 25 year output of the particular panel selected:
We know the price of electricity from the grid increases by 3% /per year ( user selected)
Price of electricity exported stays the same
The output of the panels decreases each year
We need to set up a table.
Let’s see some cumulative savings
Now looking at 25 year output of the particular panel selected:
Select a year: Data Validation drop down list
State what the output is in the year selected
States what the actual cumulative savings are in $ terms
And the actual savings in the year selected
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Lahiru's spent 15 years in the energy industry, driving outcomes and twin cabs. A good chunk of his Greenwood hours are spent onsite, overseeing the delivery of utility projects and solar farms. And when he punches the clock, Lahiru's still tackling challenges (wearing the uniform of his local NFL club).
