By Samuel Adeyemo, COO at Aurora Solar

The Levelized Cost of Energy (LCOE) is one of the residential solar industry’s most commonly used metrics. However, it is also one of the industry’s most poorly understood and incorrectly calculated metrics.

If you’re like most solar professionals, you kind of understand LCOE. You may have referred to it as the “solar rate” or the “solar cost of energy.” However, do you truly know the components of the metric you are quoting and how important they are to designing the optimal solar project?

In our last article, we showed that the highest possible return on a solar project is realized when the project’s LCOE is equal to the prevailing utility rate[1]. That means that if you incorrectly calculate the LCOE, you could be designing a sub-optimal solar project. It also just so happens that most conventional LCOE calculations incorrectly calculate too high a number, meaning that you are making solar seem less compelling than it should be. Whether you design a sub-optimal project, or you under-sell what you are designing, you are leaving money on the table. It is my hope that this article will help boost your understanding of this four-letter acronym.

At its most basic level, the Levelized Cost of Energy is the lifetime cost of a solar installation, divided by the amount of energy the installation generates.

By taking into account the upfront cash payment, as well as lifetime O&M and financing costs, the LCOE is supposed to give an “apples to apples” comparison of going solar versus staying 100% on the grid. To explain this better, let us expand the numerator of the LCOE equation below:

So far, most of this should be fairly intuitive: The more expensive the project (PC) and the higher your costs to maintain (O&M) and finance (LP) it, the higher your LCOE. Your ITC reduces your project cost, so that reduces your LCOE.

However, I’m guessing you may not be familiar with the term “Present Value of Performance Benefit Incentives.” Excluding this term could have grave consequences: miscalculating this metric could cause money to leak out of your, and your client’s, pocket. It could contribute to increased carbon emissions by causing you to undersize your solar project, and by reducing your chances of convincing your customer to go solar. I am clearly exaggerating, but now that I have your attention, let me explain the term.

Performance Based Incentives (PBIs) are payments that are tied to your solar project’s energy production. In the U.S., the most common form of these are Solar Renewable Energy Credits (SRECs). At the time of writing, 10 states had active SREC programs or markets, and there are often talks of expanding the program. Carbon credits are another type of PBI. Most home and business owners monetize their PBIs by selling them[2]. The actual value the homeowner receives will reflect the present value of the expected PBIs.

The effects this will have on your project’s LCOE varies based on the SREC program. Let us examine a homeowner that consumes 10,900 kWh. Using Aurora, I designed a 9.9-kW system in a state where SRECs are currently priced at $0.27/kWh. Let us assume that this 9.9-kW installation has a Total Solar Resource Factor[3] of 71%.

Performing high accuracy remote shading analysis is easy and accurate using software like Aurora.

Aurora’s NREL-validated performance simulation engine estimates that this design produces 10,400 kWh per year, and it will produce 320.5 MWh over the project life[4].

Let us assume further that the homeowner purchased the system (so we don’t have to worry about loan payments) at a price of $44,460 ($4.5/W). Finally, let us assume an inverter replacement cost of $0.4/W, and the inverter is replaced once over the life of the system. The assumptions are listed below for convenience:

Aurora includes a full financial analysis engine so you can easily perform detailed financial analysis.

With those assumptions, the project’s LCOE excluding PVPBI is $0.11/kWh, while the LCOE including PVPBI is $0.09. This is almost a 20% difference, which in and of itself makes a big difference in how attractive solar will appear to the homeowner. (Did you hear the coin slip out your pocket?)

Let us examine what effects this difference in LCOE has on the optimal system size. In part one of this series we learned an optimal system size is realized when the project’s LCOE is equal to the prevailing utility rate. The relevant utility in this case, National Grid, has a tiered residential rate that increases with your net energy consumption. This means that by having a lower LCOE, you can have a bigger system since you can afford to offset both the highest and lowest electricity tiers.

For the example above, a 9.9-kW system offsets almost 100% of the homeowner’s energy consumption. This is not a coincidence; when your LCOE is less than your marginal utility rate, you want to offset as much of the homeowner’s energy consumption you can(review part 1 of this series if you want to know why). In this case, at a LCOE of $0.11, this project would not be economically feasible (hence the incentive program).

Despite its simplifying assumptions, this example illustrates how understanding all the aspects of a complete LCOE calculation will aid you in sizing solar installations. It also shows the importance of including the present value of performance based incentives in your LCOE calculation.

Don’t leave money on the table. Aurora calculates LCOE and other financial metrics correctly, and you should ensure whichever software package you use does the same.

One more thing: For the math geeks among you, you can find below the unabridged LCOE formula Aurora uses:

Notes:

[1] This assumes a Net Energy Metering regime.

[2] Often times the solar installer or financier will do this for the homeowner.

[3] Total Solar Resource Factor is the ratio of how much irradiance hits the roof surface, as compared to how much irradiance the roof surface would receive in optimal conditions (perfect orientation and no shading).

[4] This assumes a degradation rate of 0.5%.