[RE-wrenches] apollo charge controllers

[Peter Parrish]

Esteemed wrenches,
 
I have been wrestling with this concept about as long as we have been in
business. How to estimate how much a pv system will reduce the demand charge
for a customer.
 
I know the "worst case" goes as follows: 
 
(1)     Demand is based on measuring the consumption every 15 minutes and
keeping track of those numbers for the entire billing period.
(2)     The customer gets socked with a demand charge that is based on the
highest 15 minute consumption for the entire billing period.
(3)     The customer also gets soaked with a "facilities charge" that is
equal to the greatest monthly demand number for the trailing 12 months.
(4)     Now you have a solar system pumping out Wac varying over the
familiar bell-shaped curve during the day.
(5)     In the southwest US, peak demand typically occurs early in the
afternoon in the summer, during the week. Our LADWP has a mantra that goes
something like this, "Peak demand occurs at 3pm PDT on the third Thursday in
August!" I believe them.
(6)     So one would expect something like 40% of the peak Wac to offset the
peak demand, but what happened if the sun goes behind a cloud for those 15
minutes? Answer, "Bad luck. Your demand is back to what it was before you
bought your solar system.
(7)     It is actually worse than that. Peak demand recurs with
approximately with the same value with some regularity for an extended
period of time, so the sun will have to shine with full intensity every day
when peak demand is expected to occur, which in LA could be every day (M-F)
of the 30 day billing period.
 
I have always taken the position that we can't guarantee that any of the
demand charge will be reduced with a solar system. But what do other PV
integrators tell there customers? Better yet is there any actual data on
demand reduction with PV systems? It seems to me that occasionally the
monthly peak demand will in fact be shaved by PV production, the question is
how often in practice?
 
I once thought of taking actual insolation data and comparing it with actual
demand data and doing a Monte Carlo simulation (throwing the dice = randomly
matching up demand data with solar production data) - but I haven't retired
yet.
 
I would love to hear what others are doing about this.
 
- Peter
Peter T. Parrish, Ph.D., President
California Solar Engineering, Inc.
820 Cynthia Ave., Los Angeles, CA 90065
CA Lic. 854779, NABCEP Cert. 031806-26
 <mailto:peter.parrish at calsolareng.com> peter.parrish at calsolareng.com  
Ph 323-258-8883, Mobile 323-839-6108, Fax 323-258-8885

 
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