Panel degradation: a quick review 

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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. 

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The efficiency (%) of a panel is calculated by the maximum power rating (W) at STC, divided by the total panel area in metres.

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The are various measures to determine the best panel for you that include:

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• 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

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Panel degradation: a spreadsheet approach

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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.

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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
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Data: panel degradation

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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.
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First steps

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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

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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

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More information is needed

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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

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Pricing for electricity import and export

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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

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Now the nitty gritty

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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.

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Let’s see some cumulative savings

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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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If you’d like to see more of what Greenwood Solutions get up to in the real world of renewable energy, solar, battery storage and grid protection check out our industry and commercial pages:

https://www.greenwoodsolutions.com.au/industry 

https://www.greenwoodsolutions.com.au/commercial

https://www.greenwoodsolutions.com.au/news

https://www.greenwoodsolutions.com.au/commercial/customer-stories

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