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Comparing Solar Bids - Part 3: Utility Savings Analysis


  07:02:00 am, by Jim Jenal - Founder & CEO   , 1391 words  
Categories: Utilities, Commercial Solar

Comparing Solar Bids - Part 3: Utility Savings Analysis

In Part 1 of our series on Comparing Commercial Solar Bids we looked at how to distinguish bids based on the Solar Modules proposed.  Part 2 did the same for Solar Inverters.  Now our four-part series continues, looking at what to expect from a Utility Savings Analysis.

You want to know what your savings will be from your new solar power system and this analysis should answer that question.  A proper Utility Savings Analysis must do three things: predict the amount of power the system will produce both peak and in terms of energy over time; assess the value of that production in Year 1; and apply appropriate factors to assess the change in value of that production over the lifetime of the system. Let’s break this down.

Power and Energy Production

The peak power and energy yield from the proposed system in Year 1 is a function of system and environmental factors.  The system factors include the modules and inverters chosen (including all of the variables discussed previously).

The environmental factors consist of the azimuth (orientation relative to true North), the pitch of the array and any shading factors that might be present.  If the overall array is comprised of sub-arrays with different environmental factors, then each sub-array must be assessed separately.

For a so-called “fixed-plate array” – that is a solar array that is at a set azimuth and pitch (which is typical for commercial installations) – the ideal azimuth in terms of annual energy yield is due South and the ideal pitch is the latitude of the site.  While deviations from these ideal values will result in reduced annual energy yield, in the real world such deviations, are common.  Indeed, in some settings a deviation might be desirable if, for example, summertime performance is to be maximized (perhaps to mesh with the payment profile of a feed-in tariff program), in which case a flatter array pointed more to the West might be selected.

Regardless of the azimuth and pitch, shading is to be avoided, especially if string or central inverters are used.  When a string of solar modules are wired together, shade falling on one module not only degrades the performance of that module, it will degrade the performance of the entire string.  This in turn will degrade the performance of the entire sub-array of which that string is a part.  (Microinverters overcome this problem because each module operates independently – a shaded module still sees its performance deteriorate, but  that deterioration has no effect on the adjacent, unshaded modules.)

All of these factors, as well as the geographic location of the system site, are then provided as inputs into a PV system performance model – the best known being PVWatts, created by the National Renewable Energy Laboratory (NREL), and used as the underlying mechanism of many utility rebate calculators, such as the CSI rebate calculator.  The output from the calculator will provide a value for the peak output from the system (in AC Watts) and the energy yield profile over a year – either month-by-month, or even hour-by-hour.

Savings in Year 1

Knowing the production profile for the proposed system is just the first step in the Utility Savings Analysis.  The next crucial step is to calculate the savings from that production in Year 1 – the first year the system goes live.  To do this accurately requires a detailed analysis of the relevant utility rate structure and possibly detailed information about how the existing loads at the site behave.  A simple-minded analysis that assumes that all kWh’s of energy are worth the same fails to meet this standard and will not accurately predict the savings to be achieved.

Most commercial solar customers pay for both total energy usage and peak power demand.  To accurately determine savings requires a clear understanding of how the solar power system will affect both of those components.  Unfortunately, it is not uncommon that the data necessary for such an analysis will be incomplete or missing altogether.

Savings from usage reduction, by comparison, are easy to calculate – if you have past usage data (pretty much always available except for new buildings or new utility customers) and a properly designed rate structure model, it easy to apply the energy yield profile from the performance calculator to the rate structure and determine savings.  For customers on usage only rate structures, this provides a nearly perfect estimate of annual savings as of Year 1.

It is in trying to determine how the solar power system will alter demand charges where things get complicated.  To do this accurately – and honestly – requires hour-by-hour demand data so that the client and the solar contractor know when peak demands occur.  If the peak demand occurs at high Noon and is driven by HVAC loads, the solar system will directly reduce that peak – perhaps by the full value of the solar power system’s output.  Conversely, if the peak demand occurs at eight o’clock in the morning – say, when the first shift arrives – the solar power system will have next to zero effect on peak demand.

Unfortunately, unless the potential client has been on a time-of-use based rate structure, such data is almost certainly unavailable.

Under those circumstances client and contractor have only two choices: gather the data as part of the site evaluation process (by temporarily or permanently installing data logging equipment), or make a well-documented estimate (also known as a wag) of what the effect of the solar power system will be on peak demand.  The client should insist that all of its bids use the same estimate.

Fortunately, relatively inexpensive data logging equipment is now available and it should be used whenever available decision-making timing permits.  A contractor who refuses to provide such a service – for a fee, of course – should be scratched from the list of potential candidates.

Savings Over System Lifetime

So now you have an estimate of your utility savings in Year 1 – how can you determine what your savings will be over the lifetime of the system?  After all, that is the key question in determining your ultimate Return on Investment.

To answer that question requires figuring out two more puzzle pieces – one straight-forward and the other unavoidably controversial.  The straight-forward puzzle piece is how will the system’s performance will change over time.  This is straight-forward because we have reliable data for making that prediction.  Assuming reasonable maintenance for the system – cleaning the modules occasionally, replacing broken modules or repairing faulty inverters, etc. – the performance from one year to the next is really a function of the deterioration in the performance of the solar modules installed.  That rate will be documented in the performance warranty of the module, and hence it will be easily modeled.  (For modules that guarantee 80% of nameplate power after twenty-five years, that works out to a degradation factor of ~-0.9%/year.)

The controversial puzzle piece is in guesstimating what will happen with utility rates over the lifetime of the system.  Not only is this a difficult task at best, it has even lead to class-action litigation when solar leasing giant SunRun was sued for over-estimating (according to the Plaintiff) the magnitude of utility rate increases in the future.

Over the years solar companies have used annual rate increase factors ranging from 6.8% (almost certainly too high) to 3% (almost certainly too low, at least in California).  SunRun was sued for picking 6%, and yet in 2012 SCE secured a three-year, 17.2% average rate increase – which works out to 5.7%/year!

So what is the right number to use?  At Run on Sun we generally use 4.5% for municipal utilities and 5.7% for SCE.  But in our view, the rate selected is not as important as the need to clearly disclose the rate being used in the model.  If that is done, the client is free to do their own calculation with a different rate or to insist that all potential bidders use the same rate.  At the very least, such disclosure should render lawsuits such as the one facing SunRun moot.

However the rate is determined, and ultimately disclosed, the result should be a series of values for how much savings will be generated by the system for the next twenty-five years.  Now you are ready to receive your payoff analysis – that demonstrating your anticipated Return on Investment and LCOE, the topic of our final installment.

The preceding is an excerpt from Jim Jenal’s upcoming book, “Commercial Solar Step-by-Step,” due out in July.


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Jim Jenal is the Founder & CEO of Run on Sun, Pasadena's premier installer and integrator of top-of-the-line solar power installations.
Run on Sun also offers solar consulting services, working with consumers, utilities, and municipalities to help them make solar power affordable and reliable.

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