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Reexamining the Usage of LCOE for Comparison of Electricity Generation Sources

Updated: Jun 20


December 2021

The graph and report in question are from the annual Lazard report on “levelized cost of electricity” or LCOE that purports to provide an apples-to-apples comparison for different power generation sources. Such analyses from Lazard and also the Energy Information Administration (EIA) have long been used by renewable energy enthusiasts to argue that renewables are ready to replace carbon based fuels today.


However, there are important distinctions between the LCOE and the retail cost of electricity on the market, which is why we see that in countries that have aggressively adopted intermittent renewable energy sources, the cost of electricity to the consumer has increased and not decreased.



Figure courtesy of the Manhattan Institute.


The U.S. EIA defines levelized cost of electricity as the average revenue per unit of electricity generated that would be required to recover the costs of building and operating a generating plant during an assumed financial life and duty cycle. LCOE is easy to understand and easy to calculate, and hence an oft-used metric. Proponents of 100% renewable energy prefer this metric because it seemingly confirms their bias. Conventional sources such as natural gas, coal, and nuclear appear to be more expensive.


Table from U.S. EIA Annual Energy Outlook.


However, while LCOE calculations take into account the cost of the hardware used to generate electricity, and use reasonable assumptions for things like taxes, the cost of borrowing, and maintenance etc., it leaves out the real-world cost of delivering electricity. Put differently, the LCOE calculations do not take into account the array of real, if hidden, costs needed to operate a reliable 24/7 and 365-day-per-year energy infrastructure. Unfortunately, when proponents of 100% renewables use LCOE, they fail to heed the warning of the U.S. Energy Information Administration itself, which reports that:

“Actual plant investment decisions consider the specific technological and regional characteristics of a project, which involve many other factors not reflected in LCOE values. One such factor is the projected utilization rate, which depends on the varying amount of electricity required over time and the existing resource mix in an area where additional capacity is needed… Because load must be continuously balanced, generating units with the capability to vary output to follow demand (dispatchable technologies) generally have more value to a system than less flexible units (non-dispatchable technologies) that use intermittent resources to operate. The LCOE values for dispatchable and non-dispatchable technologies are listed separately in the following tables [the screenshot above] because comparing them must be done carefully.”

Furthermore:

“LCOE does not capture all of the factors that contribute to actual investment decisions, making the direct comparison of LCOE across technologies problematic and misleading as a method to assess the economic competitiveness of various generation alternatives.”

On this matter, Senior Fellow at the Manhattan Institute and their resident energy expert, Mark P. Mills, says:


“An LCOE assumes that the future cost of competing fuels—notably, natural gas—will rise significantly. But that means that the LCOE is more of a forecast than a calculation. This is important because a “levelized cost” uses such a forecast to calculate a purported average cost over a long period. The assumption that gas prices will go up is at variance with the fact that they have decreased over the past decade and the evidence that low prices are the new normal for the foreseeable future. Adjusting the LCOE calculation to reflect a future where gas prices don’t rise radically increases the LCOE cost advantage of natural gas over wind/solar."

An LCOE incorporates an even more subjective feature, called the “discount rate,” which is a way of comparing the value of money today versus the future. A low discount rate has the effect of tilting an outcome to make it more appealing to spend precious capital today to solve a future (theoretical) problem. Advocates of using low discount rates are essentially assuming slow economic growth.


A high discount rate effectively assumes that a future society will be far richer than today (not to mention have better technology). Economist William Nordhaus’s work in this field, wherein he advocates using a high discount rate, earned him a 2018 Nobel Prize.


An LCOE also requires an assumption about average multi-decade capacity factors, the share of time the equipment actually operates (i.e., the real, not theoretical, amount of time the sun shines and wind blows). EIA assumes, for example, 41% and 29% capacity factors, respectively, for wind and solar. But data collected from operating wind and solar farms reveal actual median capacity factors of 33% and 22%. The difference between assuming a 40% but experiencing a 30% capacity factor means that, over the 20-year life of a 2-MW wind turbine, $3 million of energy production assumed in the financial models won’t exist—and that’s for a turbine with an initial capital cost of about $3 million.”


In other words, the assumptions that go into calculating LCOE for renewables such as lifetime of the installed hardware and the capacity factors (time that the hardware produces energy) are much more optimistic than what real world experiences have shown thus far. For e.g., Wind turbines in the UK and Denmark have shown drastic reductions in load factor with more accelerated aging than what was anticipated when they were installed. Studies have also shown that as wind power expands, its energy density will decrease as installations will occur at less-than-ideal locations.


Furthermore, a 40% capacity factor is different for dispatchable vs non-dispatchable (intermittent) energy sources. The capacity factor is simply the actual generation divided by the maximum generation that would be supplied if the plant ran at full capacity during the entire year. For example, a wind turbine typically has a lower bound wind speed where it does not run at all (8 mph) and an upper bound wind speed (typically >50 mph) where it must be turned off to avoid damage (such as from a hurricane). So, while there are a range of wind speeds where the turbine will run at full capacity, for most of the time the wind turbine will run at less than full capacity. Whereas to say a natural gas plant is operating at 40% capacity factor would means that the generator provides reliable energy for 40% of the time and shuts off the other 60%, for a wind turbine that is not the case at all.


MIT’s Paul Joskow had the following to say regarding relying on LCOE:


“In a nutshell, electricity that can be supplied by a wind generator at a levelized cost of 6¢/KWh is not ‘cheap’ if the output is available primarily at night when the market value of electricity is only 2.5¢/KWh. Similarly, a combustion turbine with a low expected capacity factor and a levelized cost of 25¢/KWh is not necessarily ‘expensive’ if it can be called on reliably to supply electricity during all hours when the market price is greater than 25¢/KWh… Integrating differences in production profiles, the associated variations in wholesale market prices of electricity, and life-cycle costs associated with different generating technologies is necessary to provide meaningful comparisons between them.”

So, as neatly outlined in a study by Columbia University, the main issues associated with depending on LCOE as “proven facts” are the following:

(1) additional cost of integrating non-dispatchable energy sources into the grid is ignored,

(2) the economic and environmental externalities are ignored, and

(3) LCOE has an implicit focus on new energy source development and not existing source generation.


As for the anti-nuclear lobby, whose Venn diagram overlaps with the pro-renewables lobby, LCOE calculations are commonly waved around because the numbers are, more often than not, skewed against long-lived sources of energy. This is because their calculations assume cost recovery periods much shorter than the plant is likely to operate. In the case of nuclear plants, experience has shown that they have remained in safe and profitable operation north of 60 years, whereas the U.S. EIA uses recovery periods of 30 years for all generation sources in their calculations. This favors solar and wind while skewing against nuclear even though in real world application nuclear plants have remained in operation longer than anticipated. The EPA estimates that the average lifetime for a wind turbine is 20 years, and studies show that their performance degrades quite dramatically with aging. In the U.S., wind-farm capacity factors have improved but at a slow rate of about 0.7% per year over the past two decades. However, the kicker is that this gain was achieved mainly by reducing the number of turbines per acre resulting in a 50% increase in the average land used per unit of wind energy.


In order to address at least one of the aforementioned issues associated with using LCOE as a tool, the International Energy Agency (IEA) proposed the idea of a “value-adjusted” LCOE, or VALCOE, which includes elements of flexibility and incorporates economic implications of dispatchability. Using this new, slightly more holistic, methodology, IEA calculations yielded a VALCOE for coal power, far cheaper than solar, with a cost penalty widening as a grid’s share of solar generation rose (see below).



Figure source: IEA


Subsidies, tax preferences, and mandates can hide real world costs, since the intermittency of solar/wind is balanced by expensive fossil fuel generation. But when enough of these factors accumulate, the effect should be visible in overall system costs. And it is! That is what we see in the first figure in this report, whereby in Europe, the data show that the higher the share of wind/solar, the higher the average cost of grid electricity. Germany has seen average electricity rates rise >30% in just the last decade with renewable generation capacity targets reached but targets for CO2 emissions not met. The same pattern with higher electricity bills with greater wind/solar penetration is visible in Australia. Indeed, this has to do with the cost of interventions to stabilize the grid due to the intermittency of renewables.


Proponents of 100% renewables will, undoubtedly, retort with the one panacea to their problems of intermittency - batteries! However, even Lazard’s own LCOE calculations show that adding storage to the equation eliminates the advantage over conventional power sources. Moreover, the calculation used for pricing storage - levelized cost of storage (LCOS) - has its own set of issues and is not a precise metric by any means.


Consider that $200,000 worth of Tesla batteries, collectively weighing over 20,000 pounds, are needed to store the energy equivalent of one barrel of oil (which weighs around 300 pounds)! Ignoring the environmental costs of creating batteries, one ought to consider the sheer amount of battery storage that would be required to power communities when the sun does not shine or the wind stops blowing. After a blackout in South Australia in 2018, Tesla, the world’s “best” battery manufacturer, with much media fanfare, installed the world’s largest lithium battery “farm” on that grid. For context, to keep South Australia lit for 12 hours with no wind or sun would require 80 such “world’s biggest” Tesla battery farms, and that’s on a grid that serves only 2.5 million people.

Even with the overly-generous cost reductions plugged into the LCOS calculations for batteries over time, Lazard does not foresee renewables taking over conventional power sources anytime soon. They say that “although alternative energy is increasingly cost-competitive and storage technology holds great promise, alternative energy systems alone will not be capable of meeting the baseload generation needs of a developed economy for the foreseeable future.”


Talk about EROI

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