Did the One Child Policy Matter? Probably Not.

China’s one-child policy is ending. The policy, started in 1979-80, was aimed at slowing population growth, which was much more of a concern in the late 70s than it is now. China’s one-child policy was also horribly coercive. Men bursting in and forcing miscarriages. Forced abortions for millions. Really the stuff of dystopian nightmares.

Did that coercive policy have any impact at all on population? A look at the data suggests not, or at best, not much.

China Fertility Rate vs Hong Kong, Thailand, Singapor, Korea - One Child Policy - Demographics

You can look at the data yourself here.

Two observations:

  1. China’s birth rate was already plummeting. It fell rapidly from 1965 to 1980, when the policy went into effect. For the next decade, the first decade of the one-child policy, the birth rate stayed roughly flat.
  2. Other Asian nations saw birth rates plummet as much or more. Hong Kong, South Korea, Thailand, Vietnam, and Singapore – all rapidly developing, as China was – all saw their birth rates plummet. On a percentage basis, since 1980, Korea, Thailand, and Vietnam have all seen fertility drop more than in China

Now, all of these nations, and especially China, are dealing with a rapidly aging population, and a lack of young people. Ending the one-child policy, while good from the standpoint of freedom, is unlikely to substantially lift China’s birth rate.

The IMF agrees.

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How Cheap Can Energy Storage Get? Pretty Darn Cheap

This is part 3 of a series looking at the economic trends of new energy technologies. Part 1 looked at how cheap solar can get (very cheap indeed). Part 2 looked at the declining cost and rising reliability of wind power. Now let’s talk about storage.

How Cheap Can Energy Storage Get?

Bill Gates recently told The Atlantic that “we need an energy miracle”. The same article quotes him as saying that storage costs roughly an order of magnitude too much. How quickly will the cost of storage drop? I attempt to answer that question here.

tl;dr: Predictions of the future are fraught with peril. That said, if the current trajectory of energy storage prices holds, within a decade or two mass energy storage of a significant fraction of civilization’s needs will be economically viable.

Disclosure: I’m an investor in two companies mentioned in this post: LightSail Energy and Energy Storage Systems.

Background: The Storage Virtuous Cycle

Before going further, you may want to read my primer on energy storage technology and economics: Why Energy Storage is About to Get Big – and Cheap.

In short, there are profitable markets for energy storage at today’s prices. And additional scale drives down the price further, opening up new markets. This is the Energy Storage Virtuous Cycle.

Energy Storage Virtuous Cycle

(Almost) Everything Gets Cheaper With Scale

As I mentioned in the post on how cheap solar can get, almost every industrial activity shows signs of a ‘learning curve’. That is to say, in industry after industry, as volume scales, prices drop. This is not simply the economies of scale. Rather, the learning curve is about both scale and about the integration of lessons and innovations that build up over time.

Evidence of the learning curve goes back to the Ford Model T.

Model T Price Learnin Curve

And the learning curve is clearly on display in exponentially declining solar prices and likely continues to play a role in declining wind power prices.

It shouldn’t be any surprise, then, to find that energy storage has a learning curve too.

The Lithium-Ion Learning Curve

How fast does energy storage get cheaper? Let’s start with lithium-ion batteries. Lithium-ion is the battery chemistry used in laptops, phones, and tablets. It’s used in electric vehicles. And it’s starting to be used at grid scale.

The price of small lithium-ion batteries dropped by roughly a factor of 10 between 1991 and 2005.

Lithium-ion battery price 1991-2005

Large battery formats, such as those used in electric vehicles and for grid storage, are more expensive than the smaller batteries used in mobile devices. But large batteries are also getting cheaper

Different analysts looking at the data draw similar but slightly different conclusions about the learning rate of large lithium-ion batteries. Let’s review those estimates now.

The Electric Power Research Institute (EPRI) reviewed a variety of data to find that lithium-ion batteries drop in price by 15% per doubling of volume. (What most would call a 15% learning rate, but which they instead call an 85% learning rate.)

EPRI Future Battery and Energy Storage Cost Curve - 95 and 90 Percent - by packs per year

Winfriend Hoffman, the former CTO of Applied Materials, and one of the first to apply the learning curve concept to solar, similarly finds a 15% learning rate in large format lithium-ion batteries

Battery Learning Curve hoffmann-grafik-1-01.

Bloomberg New Energy Finance (BNEF), meanwhile, uses more recent data, and finds a 21.6% learning rate in electric vehicle batteries. In fact, the learning rate they find is strikingly similar to the learning rate for solar panels.

BNEF Battery Energy Storage Learning Curve is the Same as PV Learning Curve

So the range of estimates of from 15% to 21%. How cheap does that suggest lithium-ion battery storage will get?

How Cheap Can Lithium-Ion Batteries Get - Energy Storage

All of today’s large-format lithium-ion batteries, combined, can store less than 1 minute of world’s electricity demand. As scale increases, that number will rise, and, if current trends hold, the price of new batteries will drop.

On that trend, starting with the assumption that batteries today cost somewhere around 25 cents per kwh sent through them, by the time the planet has sufficient lithium-ion battery storage to hold just 13 minutes of today’s electricity demand, lithium-ion prices will have dropped by a factor of 2 to 2.5, down to a range of 10-13 cents per kwh stored.

By the time the world has enough lithium-ion battery storage for roughly an hour of electricity demand, prices will be in the range of 6-9 cents.

And by the time the world can store a full day of electricity demand, prices (if current trends hold) would be down to 2-4 cents per kwh.

How Cheap is Cheap Enough?

If you’re informed on wholesale electricity prices, the prices above may sound ridiculously high. Wholesale natural gas electricity from a new plant is roughly 7 cents per kwh (though that doesn’t include the cost of carbon emitted). How could batteries priced at 25 cents per kwh, or even 10 cents a kwh, compete? Particularly when you also have to pay for electricity to go into those batteries?

The answer is that batteries don’t compete with baseload power generation alone. Batteries deployed by utilities allow them to reduce the use of (or entirely remove) expensive peaker plants that only run for a few hours a month. They allow utilities to reduce spending on new transmission and distribution lines that are (up until now) built out for peak load and which sit idle at many other hours. In a world with batteries distributed close to the edge, utilities can keep their transmission lines full even during low-demand hours, using them to charge batteries close to their customers, and thus cutting the need for transmission and distribution during peak demand. And batteries reduce outages.

To roughly estimate the value that batteries provide, look at the gap between the peak retail prices customers pay at the most expensive hours of the day versus the cheapest retail power available throughout the day. In a state like California, that’s a difference of almost 20 cents per kwh, from peak-of-day prices of more 34 cents to night time power that’s less than 14 cents. That difference is an opportunity for storage.

CA Time of Use Pricing Model

Another opportunity is the difference between the cheapest wholesale power price – wind at 2 cents per kwh – and peak of day wholesale prices from natural gas peaker plants, which can be over 20 cents per kwh. Again, the gap is close to 20 cents per kwh.

That said, batteries at 20 cents per kwh are only economical for a fraction of the day’s power needs. The cheaper batteries are, the greater the fraction of hours, days, weeks, and months that they’re economical for. And if we want carbon-free energy to be cheaper than coal or natural gas on a 24/7 basis, we need batteries that are extremely cheap – down to a few cents per kwh. Lithium-ion is on track for that, eventually. But, in my view, other technologies will get there first.

What’s Cheaper Than Lithium-Ion?

The cost of energy storage is, roughly, the up-front capital cost of the storage device, divided by the number of cycles it can be used for. If a battery costs $100 per kwh and can be used 1,000 times before it has degraded unacceptably, then the cost is one tenth of a dollar (10 cents) per cycle. [In reality, the cost is somewhat higher than this – there are efficiency losses and cycles in the far future are potentially worth less than cycles now due to the discount rate.]

Lithium-ion batteries suffer from fairly rapid degradation. Getting 1,000 cycles out of a li-ion battery with full depth of discharge (draining it completely) is ambitious. Tesla’s PowerWall battery is warrantied for 10 years, or 3,650 cycles, which appears to be possible only because the battery is never fully drained. What Tesla sells as a 7kwh battery is actually a 10kwh battery that never allows the final 3kwh to be drained.

Other energy storage technologies, however, are far more resilient than lithium-ion.

  • Flow batteries can potentially be used for 5,000 – 10,000 cycles, with complete discharge every time, before needing refurbishing.
  • Adiabatic compressed air energy storage (CAES) uses tanks and compressors that are certified for 30 years or more of continuous use, meaning more than 10,000 cycles, again at complete discharge rather than the 70% discharge possible in lithium-ion.(In addition, CAES can be used to store energy for weeks, months, or years, something that batteries can’t do due to leakage.)

As an added bonus, CAES systems and some flow battery systems can be made with abundant elements that are cheaper and available in higher volumes than lithium. For instance:

  • LightSail Energy‘s compressed air tanks are made of carbon fiber, the primary ingredient of which (carbon) is the 4th most abundant element in the universe, and roughly 1,000x more abundant in the earth’s crust than lithium.
  • ESS’s flow batteries are comprised almost entirely of iron, which is at least several hundred times more abundant in the earth’s crust than lithium.

[To be clear, lithium is available in quantities sufficient to make at least hundreds of millions of Tesla-class electric vehicles. There is no near-term lithium crunch. But there may be a long-term one.]

How big is the price advantage of more and deeper discharges? It’s difficult to compare apples-to-apples, because neither compressed air nor any flow battery chemistry have reached anywhere near the scale of lithium-ion. They haven’t gone nearly as far down the learning curve. At the same time, the cost of materials for a flow battery, for instance, should be comparable to or lower than for a lithium-ion battery.That’s approximately true for compressed air as well (though some more interesting differences apply, which I may return to in a future post.)

If we assume then that flow and compressed air have similar up-front costs to lithium-ion, and a similar learning curve, we can project what a unit of electricity stored and retrieved in them will cost. We’ll do so by giving them a (conservative) 50% cost advantage to account for their many times longer lifetime. In reality, their cost advantage in the long term may be larger than this.

Even at 50%, however, we find that flow batteries and compressed air are much cheaper than lithium-ion, and reach the price points of a few cents per kwh much sooner. In the graph below, we see that, assuming a similar learning rate, flow batteries and compressed air reach around 4 cents per kwh round-tripped at around 1 million MWh of storage versus 10 million MWh for lithium-ion. They reach a price of 2 cents per kwh round-tripped (a true fossil-fuel killer of a price) at around 10 million MWh stored, versus 80 million MWh for lithium-ion.

How Cheap Can Energy Storage Get

Obviously, the above is just a projection. And for flow batteries and CAES, we have far less of a track record than for lithium-ion. Some preliminary data does support the notion that they’ll be cheap, however.

  • Redflow, a maker of zinc-bromide flow batteries, sells batteries with a cost of storage around 20 cents per kwh. And zinc-bromide is well off the left side of the graph above, many many steps in its learning function away from the beginning of the chart.
  • ESS is a graduate of the ARPA-E GRIDS program, which set a goal of $100 per kwh capital costs of batteries, for batteries that can run for many thousands of cycles. The math there points to batteries that eventually cost a few cents per kwh.

We cannot be certain that any technology will follow a trajectory on a graph. Fundamentally, though, the presence of the learning curve in nearly all industrial activities, combined with the longer lifetimes of flow and CAES systems, suggests that their prices will drop well below those of lithium-ion.

The disadvantage of both flow batteries and CAES is that their energy density is low. To hold they same amount of energy, both flow and CAES are larger and heavier than lithium-ion. As a result, I expect to see a divergence over time:

  • Lithium-ion and its successor technologies (perhaps metal air) will be used for electric vehicles and mobile devices.
  • Bulkier, heavier, but longer-lasting and deeper-draining storage technologies like flow batteries and CAES will be used for stationary power for the electrical grid.

Cheap, Zero-Carbon Power, 24/7

Solar power and wind power are each headed towards un-subsidized prices of 2-3 cents per kwh in their best areas, and perhaps 4 cents in more typical areas.

Future Solar Cost Projections - PPA LCOEFuture Wind Price Projections - Naam - 14 Percent Learning Curve

New natural gas costs around 7 cents per kwh. As solar and wind steal hours from natural gas plants (because they’re cheaper when the sun is shining and the wind is blowing), natural gas plants will sit idle longer. As a result, the price of natural gas electricity will rise to perhaps 10 cents per kwh, as the up-front capital cost of natural gas plants is spread over fewer kwhs out.

To compete with that on a 24/7 basis, we need storage that costs no more than 5 or 6 cents per kwh, and ideally less.

In other words, we need to cut the price of energy storage by a factor of 5 or 6 from today’s prices.

We’ve already cut energy storage prices by a factor of 10 since the 1990s. And if current trends hold, the world is very much on path to achieving cheap enough storage to allow 24/7 clean energy, and doing so in the next 15-20 years.

How Cheap Can Energy Storage Get


There’s more about the exponential pace of innovation in both storage and renewables in my book on innovating in energy, climate, food, water, and more:The Infinite Resource: The Power of Ideas on a Finite Planet

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Hunger is at an all-time low. We can drive it even lower.

A few observations on hunger, extracted from the latest FAO report on The State of Food Insecurity, 2015

1. The percent of humanity that’s hungry is at an all-time low.

According to FAO, 11.3% of the world is undernourished. Most of that hunger is concentrated in the developing world. There, an estimated 12.9% of people are undernourished. In absolute terms, this is a staggering 780 million people. Yet as a fraction of humanity, it’s just over half of the fraction in 1990.

Hunger Trends in the Developing World  - FAO Status of Food Insecurity 2015


Going back further, FAO estimates that in 1969, 33% of the developing world (or around 875 million people) lived in hunger. Even as population has roughly doubled since 1969, the percent of the world living in hunger has dropped by almost a factor of three.

Hunger Trends Developing World 1969 - 2010 FAO


2. Countries Once Synonymous with Hunger Have Made Huge Progress

Ethiopia, as one example, has cut its hunger rate in half. At more than 30%, it’s still tremendously too high. But the trendline is extremely encouraging. Other examples, both good and bad, abound in report.

Hunger Trend in Ethiopia  - FAO Status of Food Insecurity 2015


3. Every Large Region of the World Has Seen its Percent Hungry Drop

Latin America has cut its hunger rate in a third. Asia’s has dropped by half. Even Africa – the large region with the slowest progress , has seen the proportion of its people living in hunger drop by a quarter, from 27% to 20%.

That said, Africa’s reduction in the percent of people living in hunger has been slower than its population growth. So the absolute number living in hunger has climbed there by 50 million people.

Hunger Trends by Region  - FAO Status of Food Insecurity 2015


4. Higher Economic Growth Correlates with Lower Hunger

Not surprisingly, the countries that have higher per-capita growth rates see lower rates of hunger. Growth matters.

Hunger vs Economic Growth - FAO Status of Food Insecurity 2015


5. More Industrialized Agriculture Means Less Hunger

Also not at all surprisingly, countries where agriculture is more industrialized have dramatically lower rates of hunger. The graph below shows a measure of agricultural worker productivity. Towards the left are countries where agriculture is extremely labor intensive. Towards the right are countries where a small fraction of the population grow the food, using more modern means.

The further right on the scale one goes, the lower hunger drops.

Hunger vs Labor Productivity - FAO Status of Food Insecurity 2015


6. Instability, Civil War, and Crisis are the Biggest Drivers of Hunger

Where are people most likely to be hungry? In countries that lack stability, are going through internal armed conflict, or otherwise exist in a state of protracted crisis.

Hunger and Protracted Crisis  - FAO Status of Food Insecurity 2015


Reasons to Be Optimistic

Despite the problems the sections above close on, we’ve cut the percent of people who live in hunger nearly in half since 1990. And the trend line is consistently down. While much work remains to be done, and great hurdles still exist, the likelihood is that hunger will be even more scarce a decade or two from now.

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How Steady Can Wind Power Blow?

This is part 2 of a series looking at the economic trends of new energy technologies. Part 1 looked at how cheap solar can get (very cheap indeed). Part 3 looks at how cheap energy storage can get (pretty darn cheap).

NREL recently released data showing that next-generation wind turbines could reach an incredible capacity factor of 60% over 2 million square kilometers of the US, or enough to provide roughly 10x as much electricity as the US uses. If true, this would be a game-changer in wind power, as I explain below.

Wind power is the cheapest new electricity available in the United States. But, until recently, it has neither been reliable nor available close to the areas it’s needed. Both of those factors may be changing.

Below I’ll go over:

Wind is the Cheapest Electricity in the US, and Getting Cheaper

In 2014, the average cost of Power Purchase Agreements for new wind power in the US was around 2.35 cents per kwh, the lowest it has ever been. In the windiest parts of the great plains, prices are as low as 2 cents per kwh.

The above graph from the NREL 2014 Wind Technologies Market Report uses subsidized numbers. Even after removing the effects of the major federal subsidy, the Wind Production Tax Credit, new wind power in the US costs an average of 4 cents per kwh or less.

That 4 cents per kwh is substantially below the 7 cents per kwh or more that new natural gas electricity costs.

Wind power is likely to continue getting cheaper, though that is not as certain as it is for solar. Most of the increase in 2009 and 2010 of wind power was due to the rising prices of steel, copper, and other materials that go into wind turbines. But over a long timeframe, the price of new wind power has declined (according to IEA analysis) at an average rate of somewhere between 9 and 19% per doubling of scale of the wind industry. (pdf link)

Bloomberg New Energy Finance finds a learning rate of roughly 14% over a 28 year span, right in the middle of IEA’s estimates. (While BNEF’s data in this graph ends in 2012, costs have dropped more rapidly than usual since then.)

If we extend this trend of a 14% cost reduction per doubling forward, we get the following price projections for the US.

All the usual caveats apply: This is a forward projection based on a historical rate of progress. Price trends can and do end. And wind will face significant obstacles as penetration rises above ~30% or so.

That said, the trendlines suggest that, by the time wind supplies 20% of US electricity, the unsubsidized cost of wind power at the best sites could be around 3 cents per kwh, and at more typical sites they could be around 4.5 cents per kwh.

Wind is cheap, and will (probably) keep getting cheaper.

So why isn’t there even more of it deployed today?

Getting Wind Power Where It’s Needed

There are two substantial barriers to wind power penetration in the US. The first is transmission.

Most wind projects that have been built to date are located near the area where the energy will be used, not more than few hundred miles away. Over the last decade, new regional transmission lines have been built in Texas, California, and the Midwest to transport wind energy over those short ranges.

However, the windiest parts of the United States (and of other nations) tend to be far away from population centers where electricity is needed. For instance, compare the two maps below. The first shows wind speeds in the US. The second shows the US from space, illustrating (roughly) where electricity is being used.

Wind speeds (NREL):

US cities (NASA Earth Observatory):

The fastest on-land wind speeds (and thus the cheapest and most reliable wind power) run largely in a north-south corridor through the Great Plains and western Texas. Electricity consumption, though, clusters in a broad swath of the eastern third of the United States and a narrow strip along the US west coast.

The highest density of grid transmission lines is similarly in the eastern third of the US and the US west coast and southwest, as pictured below in this map of the grid from 2009. (You can see more at NPR’s excellent, if slightly outdated, interactive power grid map.)

Thus, one key step to unlocking wind as a low-cost resource is continuing to build new transmission, particularly from the less-populated but high-wind interior to population centers east and west.

Transmission costs money, but less than many believe. The cost of high voltage DC (HVDC) transmission lines is roughly 1 cents per kwh for 500 miles, or 1.5 cents per kwh for 1,000 miles transmitted. Over 1,000 miles, an HVDC line may lose 5% or so of the electricity it transmits.

The continental US is roughly 2,600 miles from east to west. Almost every population center is within 1,000 miles (or far less) of an area with top-notch wind resources. And most are within a few hundred miles of an area with good, if not best-in-class, winds.

HVDC lines are not common in the US, however. Compare the map of HVDC lines in China to that of HVDC lines in the US.

HVDC appears to be going through a resurgence in the US. Transmission-line builder Clean Line, for instance, has plans for ~20GW of long distance HVDC transmission lines to bring great plains wind power to areas where it’s needed. The first of these is targeted for completion in 2020. (More at UBS.)

Realistically, the US may need 10x this much in new long-range transmission to unlock the highest value winds in the country. The challenge is not so much cost (which, we can see above, still places wind prices lower than fossil fuel electricity prices), but rather regulatory approval, right-of-way, and overcoming NIMBY.

Short story: If we want the cheapest possible wind power, we need to continue to build out grid transmission.

Making Wind Reliable

The second limitation on wind power has been when the wind blows, and when it doesn’t.

Wind power is intermittent. The wind doesn’t always blow fast enough for wind turbines to produce power. In the US, the capacity factor of the current fleet of wind turbines is around 33%. That is to say, on average across the year, a wind turbine that is capable of generating a MW of power will actually produce an average of around 0.33 MW. At some hours it will operate at peak output. At other hours it will operate at a fraction of its maximum output. And at yet other hours, it won’t be generating any electricity at all.

That intermittence creates additional cost for utilities, who have to find some way to back up wind power for those times when the wind isn’t blowing.

What’s just as important is the times and months that wind provides energy.

Today, peak electricity demand in the US happens during the afternoons and early evenings. Electricity demand in the US is higher in summer months than in winter months. (Note that this pattern differs in Europe, where energy-intensive air-conditioning is less common than the US.)

Wind power patterns are nearly the opposite of US electricity demand patterns. Winds tend to max out overnight and in winter months.

The combination of low capacity factor and winds that blow primarily during lower-demand hours means that wind is often slightly less valuable than its price would indicate.

What’s more, wind’s 33% capacity factor in the US may place limits on what fraction of US electricity could come from wind. A (very rough) rule of thumb is that, without storage or integration over a large area, the maximum percentage of electricity that could come from a variable source like wind power is equal to its capacity factor. Indeed, as a resource like wind starts to provide an amount of electricity even close to its capacity factor, it tends to flip the supply/demand of the market, increasing supply, and thus lowering the prices the market is willing to pay for new electricity. It eats its own lunch, as described in a well-worth-reading piece on the limitations of renewables, by Jesse Jenkins and Alex Trembath.

There are a variety of caveats to this rule of thumb, in both encouraging and discouraging directions, which I’ll return to in a later post. And there are a number of reasons to believe the limits are substantially beyond what Jesse and Alex describe in their piece. I’ll look specifically at energy storage in my next post, and come back to the larger issue of how far renewables can penetrate sometime after that.

For now: if wind capacity factors were closer to 100%, the problems above would largely disappear, and wind with its current prices would be nearly unbeatable. We’ll likely never have wind at 100% or even 90%, but the closer wind can get to 100% capacity factor, up from its current level of 33% in the US, the more powerful it becomes.

That’s what makes this NREL report so encouraging. The average new wind turbine in the US is 80 meters tall at its hub. NREL looks at what capacity factors could be reached with 110 meter tall and 140 meter tall wind turbines.

The chart shows cumulative area of the contiguous US (along the Y axis) that could reach ever-higher capacity factors (X axis). For context, the contiguous US has a land area of roughly 7.6 million square kilometers.

The different colored lines are different technologies. The black line is 80-meter tall wind turbines that are common today. The red line is 110-meter tall wind turbines that are commercially available today, and which are slightly below the average height of new turbines in Europe, but which are not yet common in the US. And the blue line is 140-meter tall wind turbines. Turbines of that size are being installed in Europe, but not yet in the US.

With 2008 technology (the black line), the line hits zero right around 50% capacity factor. Virtually no part of the US can provide wind power with 50% capacity factor with 80-meter tall turbines.

With newer 110-meter tall turbines, however, nearly 2 million square kilometers of land, or or 26% of the contiguous US, can support wind turbines with capacity factors of 50% or higher.

With 140-meter tall turbines (similar to those already in use in Europe) NREL projects that perhaps 1.8 million square kilometers could host 60% capacity factor wind. That is a near doubling of capacity factor of wind turbines from today. Put another way, it’s wind power that is roughly twice as reliable as the average wind power in the US today.

That, in turn, would lower the cost of backups to the wind. It would raise the physical limits of how much wind power could be integrated into the grid, even without storage. It would spread out and dilute the ‘eat its own lunch’ phenomena whereby renewable resources lower the market price at the hours that they generate. Wind at 60% capacity factor would spread out its delivered electricity over roughly twice as many hours 33% capacity factor, thus reducing the rate at which the prices it could fetch declined. And, most likely, more of the electricity delivered by wind would come at times of high electricity demand, raising the prices wind could fetch via another mechanism.

In short, wind at 60% capacity factor, even at the same price per kwh of today, would be tremendously more valuable than it is now, with fewer limits to how much of it we could use.

How much wind-generated electricity could be provided on 1.8 million square kilometers? In the US, an average wind farm, as of 2009, produced around 3 W per square meter of all directly and indirectly affected area. (See Land Use Requirements of Modern Wind Power in the US (pdf)). So 1.8 million kilometers (even ignoring the potentially higher energy output of turbines higher in the air) would roughly 5.4 trillion watts, or 5.4 TW.

By contrast, the average US electricity consumption in 2014 was around 0.5 TW.

So, if NREL is correct, sufficient land area exists in the US to provide 60% capacity factor wind power to meet US electricity needs 10x over.

To be clear, for a variety of reasons, wind, like solar, will never be 100% of US electricity production. But the headroom appears to be there.

Would larger wind turbines be more expensive? Per wind turbine, they certainly would be. But each will also produce more electricity, more reliably. 140-meter wind turbines in use in Europe generate 5 to 7 MW of power, vs the 2 MW common for 80-meter wind turbines.

As a general rule of thumb, wind turbines produce electricity equal to the area their blades sweep through. And area is equal to the square of blade length. That means that doubling the tower height and blade length quadruples the area the blades spin through, and generates 4x as much energy. Partially as a result, wind prices per kwh have dropped, even as (or in part because) wind turbines have grown taller.

We should expect that taller wind turbines will continue this trend. More electricity per dollar, along with higher capacity factors.

Building 140-Meter Tall Wind Turbines

NREL’s projection of the capacity factors of future wind turbines is, of course, just a projection. NREL has an excellent track record, yet we won’t truly know the achievable capacity factors for 140 meter wind turbines until we have a number built in the US.

Actually building them is quite a challenge, however. Wind turbine components are built in factories and then transported to the site. But as wind turbines have grown larger, transportation has hit the limits of what can be moved by road.

Consider the following images from DOE’s Wind Visions report, showing the challenges of moving a segment of a wind turbine tower (first image) and of moving a single blade of a wind turbine (second image).

These images depict the challenges of transporting current wind turbine components. To move pieces of 140-meter turbines (more than 500 feet tall), new steps are needed.

The new frontier is to assemble more of the wind turbine at the site, using parts that fit in ordinary semi-trailer or flat-bed truck cargos. That’s the approach used by GE’s Space Frame wind towers, which use a scaffolding-like approach to wind turbine construction. And it’s also the approach used by a number of companies working on wind turbine blades that can be shipped in pieces and assembled into full-length blades on site.

None of this is impossible. Germany’s wind industry already averages 120 meters for new wind turbines, with some as tall as 140 meters. But deploying these in the US will require innovation.

Whole articles can and have been written on these frontiers in wind turbine assembly. For an excellent overview, read John Timmer’s piece on the future of wind power at Ars Technica. Or, for a more technical view, read DOE’s Wind Visions Report.

In Summary

Technical challenges remain. But if they can be surmounted, wind power appears to be headed for a new frontier in reliability. Wind is already the cheapest source of electricity in the United States, and could, with these advances, provide half or more of the US’s electricity consumption.


If you enjoyed this post, you might enjoy my book on innovating in energy, food, water, climate, and more: The Infinite Resource: The Power of Ideas on a Finite Planet

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How Cheap Can Solar Get? Very Cheap Indeed

This is part 1 of a series looking at the economic trends of new energy technologies. Part 2 looks at the dropping price and increasing reliability of wind power. Part 3 looks at how cheap energy storage can get (pretty darn cheap). But let’s start with solar:

What’s the future price of solar?

I’ll attempt to make some projections (tentatively) here.

tl;dr: If current rates of improvement hold, solar will be incredibly cheap by the time it’s a substantial fraction of the world’s electricity supply.

Background: The Exponential Decline in Solar Module Costs

It’s now fairly common knowledge that the cost of solar modules is dropping exponentially. I helped publicize that fact in a 2011 Scientific American blog post asking “Does Moore’s Law Apply to Solar Cells?” The answer is that something like Moore’s law, an exponential learning curve (albeit slower than in computing) applies. (For those that think Moore’s Law is a terrible analogy, here’s my post on why Moore’s Law is an excellent analogy for solar.)

Solar Electricity Cost, not Solar Module Cost, is Key

But module prices now make up less than half of the price of complete solar deployments at the utility scale. The bulk of the price of solar is so-called “soft costs” – the DC->AC inverter, the labor to install the panels, the glass and aluminum used to cover and prop them up, the interconnection to the grid, etc..  Solar module costs are now just one component in a more important question: What’s the trend in cost reduction of solar electricity? And what does that predict for the future?

Let’s look at some data.  Here are cost of solar Power Purchase Agreements (PPAs) signed in the US over the last several years. PPAs are contracts to sell electricity, in this case from solar photovoltaic plants, at a pre-determined price. Most utility-scale solar installations happen with a PPA.

In the US, the price embedded in solar PPAs has dropped over the last 7-8 years from around $200 / MWh (or 20 cents / kwh) to a low of around $40 / MWh (or 4 cents per kwh).

The chart and data are from an excellent Lawrence Berkeley National Labs study, Is $50/MWh Solar for Real? Falling Project Prices and Rising Capacity Factors Drive Utility-Scale PV Toward Economic Competitiveness

This chart depicts a trend in time. The other way to look at this is by looking at the price of solar electricity vs how much has been installed. That’s a “learning rate” view, which draws on the observation that in industry after industry, each doubling of cumulative capacity tends to reduce prices by a predictable rate. In solar PV modules, the learning rate appears to be about 20%. In solar electricity generated from whole systems, we get the below:

This is a ~16% learning rate, meaning that every doubling of utility-scale solar capacity in the US leads to a roughly 16% reduction in the cost of electricity from new solar installations. If anything, the rate in recent years appears to be faster than 16%, but we’ll use 16% as an estimate of the long term rate.

Every Industrial Product & Activity Gets Cheap

This phenomenon of lower prices as an industry scales is hardly unique to solar. For instance, here’s a view of the price of the Ford Model T as production scaled.

Like solar electricity (and a host of other products and activities), the Model T shows a steady decline in price (on a log scale) as manufacturing increased (also on a log scale).

The Future of Solar Prices – If Trends Hold

The most important, question, for solar, is what will future prices be? Any projection here has to be seen as just that – a projection. Not reality. History is filled with trends that reached their natural limits and stalled. Learning rates are a crude way to model the complexities involved in lowering costs. Things could deviate substantially from this trendline.

That said, if the trend in solar pricing holds, here’s what it shows for future solar prices, without subsidies, as a function of scale.

Again, these are unsubsidized prices, ranging from solar in extremely sunny areas (the gold line) to solar in more typical locations in the US, China, India, and Southern Europe (the green line).

What this graph shows is that, if solar electricity continues its current learning rate, by the time solar capacity triples to 600GW (by 2020 or 2021, as a rough estimate), we should see unsubsidized solar prices of roughly 4.5 c / kwh for very sunny places (the US southwest, the Middle East, Australia, parts of India, parts of Latin America), ranging up to 6.5 c / kwh for more moderately sunny areas (almost all of India, large swaths of the US and China, southern and central Europe, almost all of Latin America).

And beyond that, by the time solar scale has doubled 4 more times, to the equivalent of 16% of today’s electricity demand (and somewhat less of future demand), we should see solar at 3 cents per kwh in the sunniest areas, and 4.5 cents per kwh in moderately sunny areas.

If this holds, solar will cost less than half what new coal or natural gas electricity cost, even without factoring in the cost of air pollution and carbon pollution emitted by fossil fuel power plants.

As crazy as this projection sounds, it’s not unique. IEA, in one of its scenarios, projects 4 cent per kwh solar by mid century.

Fraunhofer ISE goes farther, predicting solar as cheap as 2 euro cents per kwh in the sunniest parts of Europe by 2050.

Obviously, quite a bit can happen between now and then. But the meta-observation is this: Electricity cost is now coupled to the ever-decreasing price of technology. That is profoundly deflationary. It’s profoundly disruptive to other electricity-generating technologies and businesses. And it’s good news for both people and the planet.

Is it good enough news? In next few weeks I’ll look at the future prospects of wind, of energy storage, and, finally, at what parts of the decarbonization puzzle are missing.


If you enjoyed this post, you might enjoy my book on innovating in energy, food, water, climate, and more: The Infinite Resource: The Power of Ideas on a Finite Planet

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New Solar Capacity Factor in the US is Now ~30%

The capacity factor of new utility scale solar deployed in the US in 2010 was 24%. By 2012 it had risen to roughly 30%.

The rising capacity factor of new solar projects is part of why the cost of electricity from new solar is dropping faster than the installed cost per watt. Installing solar at a 30% capacity factor produces a quarter more electricity than the same number of watts of solar deployed at 24%.  The rise in capacity factor effectively reduces the price of electricity by 20%.

The chart and data are from an excellent Lawrence Berkeley National Labs study, Is $50/MWh Solar for Real? Falling Project Prices and Rising Capacity Factors Drive Utility-Scale PV Toward Economic Competitiveness

The EIA shows similar numbers, showing that the capacity factor of the entire solar PV fleet in the US in 2014 (including projects deployed before 2012) was 27.8%.

As newer projects come online, they’ll likely move the average capacity factor of the total fleet upwards.

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Citizens Led on Gay Marriage and Pot. We Can on Climate Change Too.

A decade ago, it was nearly inconceivable that in 2015, gay marriage would be legal across the US and marijuana fully legal in four states plus the District of Columbia.

Yet it happened. It happened because citizens who wanted change led, from the bottom up, often through citizens initiatives.

America can change it’s mind quite quickly, as this piece from Bloomberg documents.

Whatever you may think of legalized marijuana and same sex marriage, their trajectory shows how quickly change can happen, particularly when led by the people.

That’s part of why I’m excited to support CarbonWA’s proposed initiative for a revenue-neutral carbon tax in WA.

What’s a revenue-neutral carbon tax? It’s a move that keeps total taxes the same, but shifts taxes onto pollution, instead of (in this case) sales tax, or the tax of low-income people. This particular proposal reduces total taxes on the working poor, helping address Washington’s fairly regressive state tax policy.

And, while not changing the state’s total tax bill whatsoever, it would be effective in reducing carbon emissions, and accelerating the switch to renewables. It would augment other policies, including the EPA’s Clean Power Plan, and Governor Inslee’s climate plan. In fact, in WA, it would do far more than the EPA’s plan does. The proposed $25 / ton of emissions is far larger in impact than the equivalent $3 / ton that the EPA Clean Power Plan adds to carbon emissions costs in WA.

And nationwide, a carbon price of $25 / ton, as in the WA initiative, is probably roughly as effective as the EPA Clean Power Plan. That’s the conclusion of the Niskan Center. (Here’s more detail on how effective a carbon tax would be in WA.)

That is to say, a national revenue-neutral carbon tax of $25 / ton would roughly double the speed of reducing carbon emissions over the EPA Clean Power Plan alone. While the EPA Clean Power Plan places pressure on coal, a carbon tax would broaden that, forcing natural gas plants to internalize some of the cost of the carbon they’re emitting, putting them on a fairer footing in competing with wind and solar. And it would do this while lowering other taxes on Americans – the total tax bill would stay the same.

And, as I’ve written before, a carbon tax would accelerate innovation in clean energy:

Think globally, act locally. Getting this measure on the WA ballot in 2016 would start a ball rolling. WA helped lead the nation, passing referendums on same sex marriage and medical marijuana in 2012. Those helped pave the way for other states. When one state leads, others will follow.

A carbon tax isn’t enough on its own to solve climate change. Other policies are needed. But this is an excellent start.

CarbonWA needs to accumulate roughly 250,000 verified signatures by December to get this measure on the ballot for 2016. In a state of 7 million people, that’s a large number of signatures. That takes money, volunteers, and publicity.

I’ll be donating to CarbonWA, and you’ll see me write about the importance of this again.

In the meantime, if you’re interested, you can:

And most importantly, spread the word.

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Solar Cost Less than Half of What EIA Projected

Skeptics of renewables sometimes cite data from EIA (The US Department of Energy’s Energy Information Administration) or from the IEA (the OECD’s International Energy Agency). The IEA has a long history of underestimating solar and wind that I think is starting to be understood.

The US EIA has gotten more of a pass. The analysts at EIA are, I’m certain, doing the best job they can to make reasonable projections about the future. But, time and again, they’re wrong. Solar prices have dropped far faster than they projected. And solar has been deployed far faster than they’ve projected.

Exhibit A. In an update on June 2015, the EIA projected that the cheapest solar deployed in 2020 would cost $89 / mwh, after subsidies. That’s 8.9 cents / kwh to most of us. (This assumes that the solar Investment Tax Credit is not extended.)

Here’s the EIA’s table of new electricity generation costs. I’ve moved renewables up to the top for clarity. Click to see a larger version.

How has that forecast worked out? Well, in Austin, Greentech Media reports that there are 1.2GW of bids for solar plants at less than $40/mwh, or 4c/kwh. And there are bids on the table for buildouts after the ITC goes away at similar prices.

That’s substantially below the price of ~$70/mwh for new natural gas power plants, or $87/mwh for new coal plants.

And the prices continue to drop.

The reality is that solar prices in the market are less than half of what the EIA projected three weeks ago.

When you hear numbers quoted from EIA or IEA, take this into account. As well-meaning as they may be, their track record in predicting renewables is poor, and it always errs on the side of underestimating the rate of renewable progress.

There’s more about the exponential pace of innovation in solar, storage, and other technologies in my book on innovating to beat climate change and continue economic growth:The Infinite Resource: The Power of Ideas on a Finite Planet

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Solar: The First 1% Was the Hardest

Solar power now provides roughly 1% of the world’s electricity. It took 40 years to reach that milestone. But, as they say in tech, the first 1% is the hardest. You can see why in this chart below.

As solar prices drop, installation rate rises. As the installation rate rises, the price continues to drop due to the learning curve.

How fast is the acceleration?

Looking at the projections from GTM, it will take 3 more years to get the second 1%.

Then less than 2 years to get the third 1%.

And by 2020, solar will be providing almost 4% of global electricity.

GTM expects that by 2020, the world will be installing 135 GW of solar every year, and will have reached a cumulative total of nearly 700 GW of solar, roughly four times the 185 GW installed today.

For context, at the end of 2013, after almost 40 years of effort, the world had a total of 138 GW of solar deployed. We’ll deploy almost that much in a single year in 2020. And the numbers will keep on rising.

The growth of the total amount of solar deployed around the world continues to look exponential, with a growth rate over the last 23 years of 38% per year. Over the last three years it’s slowed to a mere 22% per year. All exponentials become S-curves in the long run. But for now, growth remains rapid, and may indeed accelerate once more as solar prices drop below those of fossil fuel generation and as energy storage plunges in price.

The first 1% was the hardest.


There’s more about the exponential pace of innovation in solar, storage, and other technologies in my book on innovating to beat climate change and continue economic growth:The Infinite Resource: The Power of Ideas on a Finite Planet

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What’s the EROI of Solar?

There’s a graph making rounds lately showing the comparative EROIs of different electricity production methods. (EROI is Energy Return On Investment – how much energy we get back if we spend 1 unit of energy. For solar this means – how much more energy does a solar panel generate in its lifetime than is used to create it?)

This EROI graph that is making the rounds is being used to claim that solar and wind can’t support an industrialized society like ours.

But its numbers are wildly different from the estimates produced by other peer-reviewed literature, and suffers from some rather extreme assumptions, as I’ll show.

Here’s the graph.

This graph is taken from Weißbach et al, Energy intensities, EROIs, and energy payback times of electricity generating power plants (pdf link). That paper finds an EROI of 4 for solar and 16 for wind, without storage, or 1.6 and 3.9, respectively, with storage. That is to say, it finds that for every unit of energy used to build solar panels, society ultimately gets back 4 units of energy. Solar panels, according to Weißbach, generate four times as much energy over their lifetimes as it takes to manufacture them.

Unfortunately, Weißbach also claims that an EROI of 7 is required to support a society like Europe. I find a number that high implausible for a number of reasons, but won’t address it here.

I’ll let others comment on the wind numbers. For solar, which I know better, this paper is an outlier. Looking at the bulk of the research, it’s more likely that solar panels, over their lifetime, generate 10-15 times as much energy as it takes to produce them and their associated hardware. That number may be as high as 25. And it’s rising over time.

The most comprehensive review of solar EROI to date is Bhandari et al Energy payback time (EPBT) and energy return on energy invested (EROI) of solar photovoltaic systems: A systematic review and meta-analysis

Bhandari looked at 232 papers on solar EROI from 2000-2013. They found that for poly-silicon (the predominant solar technology today, found in the second column below), the mean estimate of EROI was 11.6. That EROI includes the Balance of System components (the inverter, the framing, etc..) For thin film solar systems (the right two columns), they found an EROI that was much higher, but we’ll ignore that for now.

Note that for the second column, poly-Si, the EROI estimates range from around 6 to 16. This is, in part, because the EROI of solar has been rising, as the amount of energy required to create solar panels has dropped. Thus, the lower estimates of EROI come predominantly from older studies. The higher estimates come predominantly from more up-to-date studies.

We can see this in estimates of the “energy payback time” of solar (again, including Balance of System components). The energy payback time is the amount of time the system must generate electricity in order to ‘pay back’ the energy used to create it. Estimates of the energy payback time of poly-si solar panels (the right half of the graph below) generally shrink with later studies, as more efficient solar panels manufactured with less energy come into play.

The mean energy payback time found is 3.1 years (last column, above). But if we look at just the studies from after 2010, we’d find a mean of around 2 years, or 1.5x better EROI than the overall data set. And the latest study, from 2013, finds an energy payback time of just 1.2 years.

That is to say, the EROI of solar panels being made in 2013 is quite a bit higher than of solar panels made in 2000. That should be obvious – increasing efficiency and lower energy costs per watt make it so. If we used only the estimates from 2010 on, we’d find an EROI for poly-Si solar of around 15. If we used only the 2013 estimate, we’d find an EROI of around 25.

So how does Weißbach et al find a number that is so radically different? There are three things that I see immediately:

1. Weißbach assumes that half of all solar power is thrown away. The article uses an ‘overproduction’ factor of 2x, which seems fairly arbitrary and doesn’t at all reflect current practice or current deployment. There may be a day in the future when we overbuild solar and throw away some of the energy, but if so, it will come after solar panels are more efficient and less energy intensive to make.

2. Weißbach uses an outdated estimate of silicon use and energy cost. Weißbach’s citation on the silicon input to solar panels (which dominates) is from 2005, a decade ago. Grams of silicon per watt of solar have dropped since then, as has the energy intensity of creating silicon wafers.

3. Weißbach assumes Germany, while Bhandari assumes a sunny place. The Weißbach paper assumes an amount of sunlight that is typical for Germany. That makes some sense. Germany has, until now, been the solar capital of the world. But that is no longer the case. Solar installation is now happening first and foremost in China, then the US. In the longterm, we need it to happen in India. The average sunlight in those areas is much closer to the assumptions in Bhandari (1700 kwh / m^2 per year) than the very low-sunlight model used in Weißbach. (Remember: Germany is roughly as sunny as Canada, as you can see in the map below. Almost the entire world gets more sun than Germany, thus making costs lower worldwide and EROI higher.)

4. (Bonus) Weißbach assumes 10 days of storage. The Weißbach paper and graph also gives a second, “buffered”, number for EROI. This is the number assuming storage. Here, Weißbach uses an estimate that solar PV needs to store energy for 10 days. This is also fairly implausible. It maps to a world where renewables are 100% of energy sources. Yet that world (which we’ll never see) would be one where solar’s EROI had already plunged substantially due to lower energy costs and rising efficiency. More plausibly, in the next decade or two, most stored energy produced by PV will be consumed within a matter of hours, shifting solar’s availability from middle-of the day to the early evening to meet the post-sunset portion of the peak.

In summary: The Weißbach paper is, with respect to solar, an outlier. A more realistic estimate of poly-Si solar EROI, today, is somewhere above 10, and probably above 15. And it’s rising. Solar panels generate many times more energy over their lifetimes than is used to construct them and their associated hardware.

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