New Energy Economy: An Exercise in Magic Thinking: Part 4 Ensuring Energy Availability and Grid Parity

This part is a little longer than the previous posting but I think believe it conveys the message.



Continuing the serialization of Mark Mills’ report New Energy Economy: An Exercise in Magic Thinking. 


The high cost of ensuring  energy availability

Availability is the single most critical feature of any energy infrastructure, followed by price, followed by the eternal search for decreasing costs without affecting availability. Until the modern energy era, economic and social progress had been hobbled by the episodic nature of energy availability. That’s why, so far, more than 90% of America’s electricity, and 99% of the power used in transportation, comes from sources that can easily supply energy any time on demand.18

 In our data-centric, increasingly electrified, society, always-available power is vital. But, as with all things, physics constrains the technologies and the costs for supplying availability.19 For hydrocarbon-based systems, availability is dominated by the cost of equipment that can convert fuel-to-power continuously for at least 8,000 hours a year, for decades. Meanwhile, it’s inherently easy to store the associated fuel to meet expected or unexpected surges in demand, or delivery failures in the supply chain caused by weather or accidents.

It costs less than $1 a barrel to store oil or natural gas (in oil-energy equivalent terms) for a couple of months.20 Storing coal is even cheaper. Thus, unsurprisingly, the U.S., on average, has about one to two months’ worth of national demand in storage for each kind of hydrocarbon at any given time.21

Meanwhile, with batteries, it costs roughly $200 to store the energy equivalent to one barrel of oil.22 Thus, instead of months, barely two hours of national electricity demand can be stored in the combined total of all the utility-scale batteries on the grid plus all the batteries in the 1 million electric cars that exist today in America.23

For wind/solar, the features that dominate cost of availability are inverted, compared with hydrocarbons. While solar arrays and wind turbines do wear out and require maintenance as well, the physics and thus additional costs of that wear-and-tear are less challenging than with combustion turbines. But the complex and comparatively unstable electrochemistry of batteries makes for an inherently more expensive and less efficient way to store energy and ensure its availability.

Since hydrocarbons are so easily stored, idle conventional power plants can be dispatched—ramped up and down—to follow cyclical demand for electricity. Wind turbines and solar arrays cannot be dispatched when there’s no wind or sun. As a matter of geophysics, both wind-powered and sunlight-energized machines produce energy, averaged over a year, about 25%–30% of the time, often less.24 Conventional power plants, however, have very high “availability,” in the 80%–95% range, and often higher.25

 A wind/solar grid would need to be sized to meet both peak demand and to have enough extra capacity beyond peak needs in order to produce and store additional electricity when sun and wind are available. This means, on average, that a pure wind/solar system would necessarily have to be about threefold the capacity of a hydrocarbon grid: i.e., one needs to build 3 kW of wind/solar equipment for every 1 kW of combustion equipment eliminated. That directly translates into a threefold cost disadvantage, even if the per-kW costs were all the same.26

Even this necessary extra capacity would not suffice. Meteorological and operating data show that average monthly wind and solar electricity output can drop as much as twofold during each source’s respective “low” season.27

The myth of grid parity  

How do these capacity and cost disadvantages square with claims that wind and solar are already at or near “grid parity” with conventional sources of electricity? The U.S. Energy Information Agency (EIA) and other similar analyses report a “levelized cost of energy” (LCOE) for all types of electric power technologies. In the EIA’s LCOE calculations, electricity from a wind turbine or solar array is calculated as 36% and 46%, respectively, more expensive than from a natural-gas turbine—i.e., approaching parity.28 But in a critical and rarely noted caveat, EIA states: “The LCOE values for dispatchable and non-dispatchable technologies are listed separately in the tables because comparing them must be done carefully”29 (emphasis added). 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—or, in particular, a grid that used only wind/solar.

 The LCOE considers the hardware in isolation while ignoring real-world system costs essential to supply 24/7 power. Equally misleading, an LCOE calculation, despite its illusion of precision, relies on a variety of assumptions and guesses subject to dispute, if not bias.

 For example, 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.30 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.31

A high discount rate effectively assumes that a future society will be far richer than today (not to mention have better technology).32 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%.33 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.

U.S. wind-farm capacity factors have been getting better but at a slow rate of about 0.7% per year over the past two decades.34 Notably, this gain was achieved mainly by reducing the number of turbines per acre trying to scavenge moving air—resulting in average land used per unit of wind energy increasing by some 50%.

 LCOE calculations do reasonably include costs for such things as taxes, the cost of borrowing, and maintenance. But here, too, mathematical outcomes give the appearance of precision while hiding assumptions. For example, assumptions about maintenance costs and performance of wind turbines over the long term may be overly optimistic. Data from the U.K., which is further down the wind-favored path than the U.S., point to far faster degradation (less electricity per turbine) than originally forecast.35

 To address at least one issue with using LCOE as a tool, the International Energy Agency (IEA) recently proposed the idea of a “value-adjusted” LCOE, or VALCOE, to include the elements of flexibility and incorporate the economic implications of dispatchability. IEA calculations using a VALCOE method yielded coal power, for example, far cheaper than solar, with a cost penalty widening as a grid’s share of solar generation rises.36

One would expect that, long before a grid is 100% wind/solar, the kinds of real costs outlined above should already be visible. As it happens, regardless of putative LCOEs, we do have evidence of the economic impact that arises from increasing the use of wind and solar energy.


Part 5 will be The Hidden Costs of a “Green” Grid.


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