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(This article was first published on <strong><a href="http://adventuresindata.blogspot.com/2016/12/solar-pvc-growth-from-one-time.html"> AdventuresInData</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>Imagine you bought solar PVC arrays today, sold the power generated to your local utility, and used the proceeds to add new solar arrays each year.  What generating capacity would you expect in 100 years?  What would be the impact of uncertainties in inflation and power generation?  I’ve been obsessed with this idea for a while.  I developed a new Shiny App <a target="_blank" rel="nofollow" href="https://johnyagecic.shinyapps.io/SolarEnergyMC/">available here</a> to help play around with these questions.</p>
<div><a target="_blank" rel="nofollow" href="https://i2.wp.com/2.bp.blogspot.com/-Xwt3k8QUZbs/WERvswamI4I/AAAAAAAAA30/bNg4HH9lp1w0u8OBwueyZfO1jCUP21-igCLcB/s1600/SolarMC.PNG?ssl=1"><img src="https://i2.wp.com/2.bp.blogspot.com/-Xwt3k8QUZbs/WERvswamI4I/AAAAAAAAA30/bNg4HH9lp1w0u8OBwueyZfO1jCUP21-igCLcB/s640/SolarMC.PNG?resize=456%2C207&ssl=1" border="0" data-recalc-dims="1"></a></div>
<p>There is a link in the app to obtain the R script.  Since this is not my area, I am hoping that people with domain specific knowledge will tinker and improve.</p>
<p><b><u>Opportunities for Improvement</u></b>  There are several areas where I think some improvement is possible.  The app assumes a uniform distribution (defined by the user) of annual power generation over the 100 years.  Users can estimate localized power generation ranges using the <a target="_blank" rel="nofollow" href="http://pvwatts.nrel.gov/pvwatts.php">PVWatts Calculator</a>.  However,  as the <a target="_blank" rel="nofollow" href="http://www.nrel.gov/pv/assets/images/efficiency_chart.jpg">NREL PV efficiency chart</a> shows, higher efficiency systems are in the pipeline.  Some sort of forecast trajectory seems like a possible improvement.  Similar trajectories could be applied to inflation rates too.  The app only uses 100 Monte Carlo iterations, which is a very small number.  But the app also uses looping, and loops are slow.  I ignored cost associated with real estate.  My assumption here is that <a target="_blank" rel="nofollow" href="https://www.epa.gov/brownfields">brownfield sites</a> close to the grid would be available and suitable, but explicit real estate costs could be included. </p>
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