Maxwell’s demon: computation is energy?

Computation, the internet and AI are inextricably linked to energy. Information processing literally is an energy flow. Computation is energy. This note explains the physics, from Maxwell’s demon, to the entropy of information, to the efficiency of computers.

Maxwell’s demon: information and energy?

James Clark Maxwell is one of the founding fathers of modern physics, famous for unifying the equations of electromagnetism. In 1867, Maxwell envisaged a thought experiment that could seemingly violate the laws of thermodynamics.

As a starting point, recall that a gas at, say 300ºK does not contain an even mixture of particles at the exact same velocities, but a distribution of particle speeds, as given by the Maxwell-Boltzmann equations below.


Now imagine a closed compartment of gas molecules, partitioned into two halves, separated by a trap door. Above the trap door, sits a tiny demon, who can perceive the motion of the gas molecules.

Whenever a fast-moving molecule approaches the trap door from the left, he opens it. Whenever a slow-moving molecules approaches the trap door from the right, he opens it. At all other times, the trap door is closed.

The result is that over time, the demon sort the molecules. The left-hand side contains only slow-moving molecules (cold gas). The right-hand side contains only fast-moving molecules (hot gas).

This seems to violate the first law of thermodynamics, which says that energy cannot be created or destroyed. Useful energy could be extracted by moving heat from the right-hand side to the left-hand side. Thus in a loose sense the demon has ‘created energy’.

It also definitely violates the second law of thermodynamics, which says that entropy always increases in a closed system. The compartment is a closed system. But there is categorically less entropy in the well-sorted system with hot gas on the right and cold gas on the left.

The laws of thermodynamics are inviolable. So clearly there must be some work done on the system, with a corresponding decrease in entropy, by the information processing that Maxwell’s demon has performed.

This suggests that information processing is linked to energy. This point is also front-and-center in 2024, due to the energy demands of AI.

Landauer’s principle: forgetting 1 bit requires >0.018 eV

The mathematical definition of entropy is S = kb ln X, where kb is Boltzmann’s constant (1.381 x 10^-23 J/K) and X is the number of possible microstates of a system.

Hence if you think about the smallest possible transistor in the memory of a computer, which is capable of encoding a zero or a one, then you could say that it has two possible micro-states, and entropy of kb ln (2).

But as soon as our transistor encodes a value (e.g., 1), then it only has 1 possible microstate. ln(1) = 0. Therefore its entropy has fallen by kb ln (2). When entropy decreases in thermodynamics, heat is usually transferred.

Conversely, when our transistor irreversibly ‘forgets’ the value it has encoded, its entropy jumps from zero back to kb ln (2). When entropy increases in thermodynamics, then heat usually needs to be transferred.

You see this in the charts below, which plots the PV-TS plot for a Brayton cycle heat engine that harnesses net work via moving heat from a hot source to a cold sink. Although really an information processor functions more like a heat pump, i.e., a heat engine in reverse. It absorbs net work as it moves heat from an ambient source to a hot sink.

In conclusion, you can think about the encoding and forgetting a bit of information as a kind of thermodynamic cycle, as energy is transferred to perform computation.

The absolute minimum amount of energy that is dissipated is kb T ln (2). At room temperature (i.e., 300ºK), we can plug in Boltzmann’s constant, and derive a minimum computational energy of 2.9 x 10^-21 J per bit of information processing, or in other words 0.018 eV.

This is Landauer’s limit. It might all sound theoretical, but it has actually been demonstrated repeatedly in lab-scale studies: when 1 bit of information is erased, a small amount of heat is released.

How efficient are today’s best supercomputers?

The best super-computers today are reaching computational efficiencies of 50GFLOPS per Watt (chart below). If we assume 32 bit precision per float, then this equates to an energy consumption of 6 x 10^-13 Joules per bit.

In other words, a modern computer is using 200M times more energy than the thermodynamic minimum. Maybe a standard computer uses 1bn times more energy than the thermodynamic minimum.

One reason, of course, is that modern computers flow electricity through semiconductors, which are highly resistive. Indeed, undoped silicon is 100bn times more resistive than copper. For redundancy’s sake, there is also a much larger amount of charge flowing per bit per transistor than just a single electron.

But we can conclude that information processing is energy transfer. Computation is energy flow.

As a final thought, the entirety of the universe is a progression from a singularity of infinite energy density and low entropy (at the Big Bang) to zero energy density and maximum entropy in around 10^23 years from now. The end of the universe is literally the point of maximum entropy. Which means that no information can remain encoded.

There is something poetic, at least to an energy analyst, in the idea that “the universe isn’t over until all information and memories have been forgotten”.

Electric adventures: conclusions from an EV road trip?

It is a rite of passage for every energy analyst to rent an electric vehicle for an EV road trip, then document their observations and experiences. Our conclusions are that range anxiety is real, chargers benefit retailers, economics are debatable, power grids will be the biggest bottleneck and our EV growth forecasts are not overly optimistic.

(1) Range anxiety is real. Last weekend, we traveled from Brussels to Kortrijk, to Ypres, to the site of Operation Dynamo in Dunkirk, to the Western front of the Somme, as part of a self-educational history trip.

The total journey was 600km (map below). Undertaken in a vehicle with 300km of range. By a driver somewhat anxious about running out of electricity, and themselves needing to be rescued from Dunkirk.

For contrast, the range of an equivalent ICE car is around 800km. Although we did enjoy charging our vehicle in France’s famously low-carbon grid (65% nuclear). Combined with the prevalence of onshore wind in Northern Europe, you can easily convince yourself that you are charging using very low-carbon electricity.

(2). Chargers benefit retailers. We did spend over 2-hours charging at a Level 2 charger, near an out-of-town supermarket in Dunkirk. We passed the time by shopping in the supermarket. Ultimately, my wife and I were unable to resist buying a large bag of madeleine cakes, which would sustain us for the next 2-days. This is the biggest reason we ultimately expect EV chargers to get over-built. They will pay for themselves in footfall.

(3) Economics are debatable. Many commentators argue that electric vehicle charging should be ‘cheaper’ than ICE vehicles, but this was not entirely borne out by our own adventures.

For perspective, €1.8/liter gasoline in Europe is equivalent to $8/gallon, of which c50-65% is tax. Combusted at 15-20% efficiency, this is equivalent to buying useful transportation energy at $1.1/kWh.

Our receipt is below for Friday’s night’s EV charge in Dunkirk, equating to around $0.6/kWh of useful energy. This is about 2-4x higher than the various scenarios in our EV charging model (below). It is comparable to the untaxed cost of gasoline. And 50% below the taxed cost of gasoline.

My own perspective is that I would happily have paid more for a faster charge. As evidenced by my glee, on Sunday morning, when paying €40 for 40kWh at a fast-charger in Belgium, which took a mere 25 minutes!!

(4) Power grids will be the biggest bottleneck. What enabled us to fast charge at 100kW in the video above was a large amount of electrical infrastructure, specifically a 10kV step-down transformer and associated power electronics, to accomodate 3 x 300 kW docks, each with 2 charging points (photo below). The continued build-out of EV infrastructure therefore requires overcoming mounting power grid bottlenecks.

(5) Our EV growth forecasts are not obviously over-optimistic? Overall, our EV experience was a good one. Charging points were widely available. In big towns and small towns. Queues were minimal. Charging was easy (albeit time-consuming).

There was nothing in our experience that made me think I needed to rush home and downgrade my previously published numbers, which see global EV sales ramping up from 14M vehicles in 2023 (10M BEVs, 4M PHEVs) to 50M by 2028 (model below), including the concomitant impacts on our oil demand forecasts.

Post-script. I have listed back to this EV road trip video several time and wish to apologize for some errata. My geography is not as bad as implied by the Betherlands fiasco. At one point, I said “50 kilowatts” when I meant “50 kilowatt hours”. But our biggest mistake… well, it turns out we did have a charging cable, hidden under the front bonnet (photo below). Clearly the final barrier to EV adoption in some cases may simply be the unfamilarity of users :-/.

European gas: anatomy of an energy crisis?

European gas demand across residential heat, commercial heat, electricity and a dozen industries.

Europe suffered a full-blown energy crisis in 2022, hence what happened to gas demand, as prices rose 5x from 2019 levels? European gas demand in 2022 fell -13% overall, including -13% for heating, -6% for electricity and -17% for industry. The data suggest upside for future European gas, global LNG and gas as the leading backup to renewables. Underlying data are available for stress-testing in our gas and power model.

Energy data from Eurostat have pros and cons. The pro is 100 lines of gas market granularity across 27 EU member countries. The cons are that the full 2022 data were only posted online in March-2024, and require careful scrubbing in order to derive meaningful conclusions. We have scrubbed the data and updated our European gas and power model (below).

European gas demand (EU27 basis) fell from 414 bcm in 2021 to 363 bcm in 2022, for a decline of -52 bcm, or -13%. The first conclusion is about price inelasticity. Gas prices averaged $29/mcf in 2022, up 110% YoY, and up 5x from 2019 levels, yet gas demand only fell by 13%. Energy price inelasticity allows for energy market volatility, which we think is structurally increasing in the global energy system, benefitting energy traders, midstream companies and load-shifters (note below).

Heating comprises 40% of Europe’s gas demand, of which 24pp is residential, 11pp is commercial, 3pp is heat/steam sold from power plants to industry and 1pp is agriculture (yes, 1% of Europe’s gas is burned to keep livestock warm). Total heating demand fell -13% in 2022, in line with the total market trend, and demonstrating similar price-inelasticity.

The temperatures of processes used in different economic sectors and their contribution to total global heat demand in TWH per year.

Electricity comprises 30% of Europe’s gas demand, and our thesis has been that gas power will surprise to the upside, entrenching as the leading backup for renewables (note below). 2022 supports this thesis. Gas demand for electricity only fell by -6%, the lowest decline of any major category; and total gas demand for power, at 105bcm was exactly the same as in 2012, despite 3x higher gas prices and doubling wind and solar from 9% to 22% of the mix. These are remarkable and surprising numbers.

Industry comprises 30% of Europe’s gas demand. What is fascinating is how YoY gas demand varied by industry in 2022. Most resilient were the production and distribution of gas itself (-1% YoY), manufacturing food products (-6%) and auto production (-6%). The biggest reductions in gas demand were refineries (-41%) and wood products (-26%) because both can readily switch to other heat sources amidst gas price volatility. Other large reductions in gas demand occurred for chemicals (-26%) and construction (-24%) due to weak economic conditions.

Most strikingly, the European chemicals industry shed a full 1bcfd of gas demand YoY in 2022. This is the portion of European gas demand that seems most at risk to us in the long-term, as the US can produce the same materials, at lower feedstock costs, while possibly also decarbonizing at source, via blue hydrogen value chains (examples below).

The latest data from Eurostat and the IEA both imply that Europe’s total gas demand fell by a further -7% in 2023, due to exceptionally mild weather (heating degree days are also tabulated in our gas and power model). In other words, total European gas demand remains -8bcfd lower than in 2021, equivalent to 60MTpa of LNG, and we wonder how much of this demand can come back with LNG capacity additions, thereby muting fears of over-supplied LNG markets.

LNG ramp-rates: MTpa per month and volatility?

What are the typical ramp-rates of LNG plants, and how volatile are these ramp-ups? We have monthly data on several facilities in our LNG supply-demand model, implying that 4-5MTpa LNG trains tend to ramp at +0.7MTpa/month, with a +/- 35% monthly volatility around this trajectory. Thus do LNG ramps create upside for energy traders?

Qatar is expanding its LNG capacity from 77MTpa to 142MTpa, by adding 8 x 8.1MTpa mega-trains into the 400MTpa global LNG market.

For perspective, 65MTpa of new LNG capacity is almost 1,000 TWH pa of primary energy, whereas the total global solar industry added +400 TWH of generation in 2023 (our latest solar outlook is linked here).

Hence we wonder how fast large LNG projects ramp up? Month-by-month ramp-ups of different LNG facilities are plotted above, where we can get the data, as an excerpt from our LNG supply-demand model.

The historic precedent sees LNG facilities ramp up 4-5MTpa trains at 0.2-2MTpa/month, with an average ramp rate of 0.7MTpa/month.

The ramp-ups are also volatile, with a +/- 35% standard error around the trajectory implied by a perfectly smooth ramp-up. Volatility may benefit energy traders? Let us review some examples below.

Australia ramped up 7 mega-projects with 62MTpa of capacity from 2015 to 2019, over four-years (+1.1MTpa/month), and with surprisingly high volatility (+/- 35% standard error above/below the 1.1MTpa ramp rate). In bottom quartile months, annualized output fell by -1.2MTpa and in bottom decile months if fell by -4.3MTpa.

Sabine Pass ramped up 6 x 5MTpa trains from 2016 to 2022, which also took five years (0.4MTpa/month), and included volatility (+/- 55% standard error), a -2MTpa annualized decline in one-quarter of the months and -3MTpa decline in one-tenth. For example, the facility shut down in August 2020 due to Hurricane Laura.

Freeport LNG ramped up at 0.7MTpa/month with +/- 45% standard error, with a particularly disrupted ramp-up, due to an explosion in June-2022, which took 9 months to remedy. The incident was blamed on deficient valve-testing procedures, which allowed LNG to become isolated, heat up, expand, breach the pipeline and explode. US regulators asked for information on 64 items before permitting a restart, which speaks to the complexity of these ramp-ups (!).

Other LNG facilities have also had volatility during their ramp-ups. Elba Island LNG went offline in May-2020 after a fire. Sabine, Corpus and Freeport cut volumes by 70% peak-to-trough during the worst of the COVID crisis. The average project in our data set ramped up 4-5MTpa LNG trains at 0.7MTpa/month with +/- 35% standard error.

Hence our conclusion is that the start-up of Qatar’s first two LNG trains in 2026 will be gradual, rather than a sudden 16MTpa shock to LNG markets, while LNG traders could even benefit from the volatility? For more perspectives, please see our outlook on the LNG industry.

Email deliverability: who broke the internet?

One of our goals at Thunder Said Energy is to help make everyone smarter on the amazing world of energy, by sending out a daily email to our distribution list. But sending a daily email to 10,000 people turns out to be harder than you’d think. This video explains research email deliverability, SPF, DKIM, DMARC and lessons learned over 15-years.

We also endured an unfortunate issue in December that prevented 4,000 subscribers from receiving our research. We’re very sorry. We hope we’ve fixed it! And some comments follow below to make sure important research reaches you in the future.

This story goes back over fifteen years. In my first ever research job, we used to send research emails to a list of 2,000 investors… via Outlook. At the time, there was a limit that you could only BCC 800 people per email. And so we had to ‘blast out’ each research note in three separate batches, in a somewhat horrific process.

Hence today, large mailing lists tend to be managed by email marketing platforms. An amazing amount of computation goes on behind the scene, in an attempt to ensure emails reach you safely. This explains the common statistics that an email embeds 1Wh of electricity, emits 0.3 grams of CO2, and in aggregate the energy consumption of the internet runs to 800 TWH pa, or 2.5% of all global electricity.

An email list of 10,000 people becomes a somewhat unwieldy beast, and we worry that our research might not reach our clients, who genuinely want to receive it. We send out an email most days to our distribution list at 6:45am Eastern time. If you would like to receive this email, but are for some reason not receiving it, then please contact us, and we will help you resolve the issue.

The most common resolution for clients that are not receiving our emails is for your company’s IT administrator to whitelist our mailing list sending-domain, which is For GDPR reasons, the emails are sent from servers in Europe, which has also historically caused some of our US clients to screen out these emails. If your IT department needs any further details, then please do contact us.

We did have a major issue with our research email deliverability in December-2023. We had 3,500 users unsubscribe from our mailing list in a single day, all precisely one minute after our email was sent out. We understand that the cause was client-side mail servers checking all of the links in our outgoing emails (to make sure they are safe), including, unhelpfully, the one-click unsubscribe link that is now required by Google. Apparently we were not alone, and hundreds/thousands of other mailing lists have suffered from this issue. The issue is still under discussion in some angry Reddit threads!

Not all of our research reached all of our clients in December-2023 and early January-2024. What upset us about this, in particular, was that this timing happened to coincide with some of the most important and actionable research we have published over the past five years. In case you missed it, the three most important research notes are copied below.

Energy transition from first principles?

Our top three questions in the energy transition are depicted above. Hence we have become somewhat obsessed with analyzing the energy transition from first principles, to help our clients understand the global energy system, understand new energy technologies and understand key industries. 

Our research notes aim to make smart decision-makers even smarter, covering the key concepts and numbers, while being clear and concise, and dissecting the energy transition from first principles…

Energy theory from first principles: energy units, thermo-dynamics, electricity, electrochemistry, magnets and motors and semiconductors.

Decarbonization technologies from first principles: renewables, batteries, EVs, EV charging, lithium batteries, flow batteries, thermal batteries, SSBs, heat pumps, fusion, geothermal, CCS, DAC, blue hydrogen, green hydrogen, electrofuels, biofuels, landfill gas, biomass, biochar, nature.

Energy efficiency technologies from first principles: EROEI, electric vehiclesLED lighting, VFDs, CHPs, insulation, methane mitigation.

Energetic industries from first principles: the internetindustrial gases, hydrogen, ammonia, steel, battery recycling, trucks, transport, compressors.

The new age of electricity from first principles: transmission, transformers, transistors, harmonic filters, capacitors, reactive power.

The new age of volatility due to renewables, geopolitics, politics, policies.

Latest views on global energysolar innovationwind recalibration, nuclear, grid bottlenecks, shale and how AI is going to save the world.

We would be delighted to help you understand the energy transition from first principles. Please consider joining our distribution list or signing up to access our research.

Energy transition: three reflections on 2023?

In October-2022, we wrote that high interest rates could create an ‘unbridled disaster’ for new energies in 2023. So where could we have done better in helping our clients to navigate this challenging year? Our energy reflections on 2023 suggest some new year’s resolutions for 2024. They are clearer conclusions, predictions over moralizations, and looking through macro noise to keep long-term mega-trends in mind.

What has prompted this self-reflection is looking back on a report from October-2022, where I wrote – direct quote – that “each 1% increase in interest rates re-inflates new energies costs by 10-20%” and hence 2023 could be – again direct quote — an “unbridled disaster” for wind, solar, clean-tech (note below).

I am not bringing this up to do some kind of victory lap. Actually, the opposite, I think could have done a better job of helping my clients to navigate 2023.

The first self-reflection is about big conclusions. I did write that note above about interest rates. But then I also went on to write 37 other notes about different battery chemistries and CCS technologies. Hence a first resolution is to publish clearer summaries, which are clear, concise and regular. For those that do not have time to read all of our publications. Examples below.

The second self-reflection is a distinction, between predictions and normative aspirations. Honestly, I think one of the reasons I did not push harder on the idea that clean technologies could have a tough 2023 was to avoid ruffling feathers. I run a research firm focused on energy transition. I would like to see the world’s energy system materially improve over the course of my lifetime.

If I have a fear for, well, basically all long-term energy analysis being published today, it is that almost all energy forecasters have been brow-beaten into publishing normative aspirations about what should happen, as though they were predictions for what will happen. Really they are very different things. So for 2024, please don’t take it personally, but I am going to try to do less forecasting about what should happen, and more about what will happen.

Predictions of what will happen, not what we ‘want to’ happen?

The third self-reflection is about purpose. Research is about helping decision-makers to make good decisions and build cool stuff. Including in the face of macro turbulence, and going back to first principles (summary below).

After our energy reflections on 2023, we feel very lucky to help 260 world-class decision makers to build cool stuff. In a world that increasingly needs it. So here is wishing you a great wind-down to 2023, and I am looking forward to helping you build cool stuff in 2024.

Thermodynamics of prime movers: energy from first principles?

A highlight of 2023 has been going back to first principles, to study the underpinnings of prime movers in the global energy system. Context matters. There is no energy source to rule them all. However, if you understand the thermodynamics of prime movers, you will inevitably conclude that the world is evolving towards solar, semi-conductors, electro-magnetic motors, lithium batteries and high-grade gas turbines.

Muscle power was the original prime mover in the pre-industrial energy system (chart below). But a typical horse outputs 0.75 kW of power, converts only c10-30% of food energy to useful work (depending on how hard you work the horse), can only cover 25-40 miles in a day, must be treated humanely and annoyingly poops everywhere. So we would score horses as a 1 out of 6 on our score of prime movers. It is remarkable that despite these limitations, the total global population of horses remains flat on 1960, with around 60 million horses in the world today, showing how hard it is to disrupt established technologies.

Heat engines changed the world, starting with coal-fired steam engines, then oil-fired engines, and later gas turbines. What is remarkable and under-appreciated when homogenizing coal, oil and gas as “fossil fuels” is that they each tend to harness totally different thermodynamic cycles. We think every serious decision-maker in energy should know the laws of thermodynamics and the basics of heat engines (primer below).

Solid fuels tend to be harnessed using the Rankine Cycle, which suffers from the limitations of steam as the working fluid, and typically achieves an efficiency of 38% (model below, again from first principles). Fuel must constantly be added, ash residue, dusts, NOx and SOx must be constantly removed. A cold start takes 6-hours and the ramp-rate is only 1-5% per minute. CO2 intensity averages 0.8-1.0 kg/kWh. So overall, we score steam cycles as a 3 out of 6 on our score of prime movers.

Gas-phase fuels can be harnessed using the Brayton Cycle, which generates work by expanding super-hot and high-pressure gases across a turbine. They can run at 1,600ºC, versus steam cycles at 300-500ºC, hence achieving simple-cycle efficiencies of 45% (data here). Then for stationary plants, an entire further steam cycle can be run on the exhaust gases exiting the turbine at 600ºC, boosting total efficiency to 60%. Dust/NOx/SOx emissions are minimal. Start times are low. Ramp rates are 5-20% per minute. Combustion is continuous. There is only one dimension of rotational movement. The turbines are compact. Insanely reliable. It is for this reason that Brayton Cycle gas turbines can be strapped to the sides of airplanes, misted with jet fuel, and safely underpin 7 trn passenger-miles of air travel per year. A strongly held view based on this theory is that Brayton cycle turbines will be a major workhorse of the 21st Century energy system, help to backstop renewables, and we score them as a 4.3 our of 6 on our score of prime movers.

The engineering of Brayton cycle gas turbines comes down to high-grade materials, which can withstand insanely hot T-max temperatures, in order to maximize efficiency.

Semi-conductors are the best prime movers in the world, jumping to the top of our ranking, with a score of 5.7 out of 6. Solar semiconductors harness the photovoltaic effect absorbing diffuse sunlight and then emitting a concentrated and useful direct current, which other semiconductors can then manipulate into an alternating current, then further conductors can transmit and distribute, and even further semi-conductors can manipulate into emitting light, heat or performing computational work. We have now written primers on all of these value chains below.

But for something truly remarkable, consider that this entire chain — from solar generation to Microsoft Excel model — might have ZERO moving parts. Only moving electrons. Nothing is burning. Nothing is turning. Nothing needs maintenance. Nothing is emitted. Total system efficiency is above 90%. As long as the sun is shining. If you understand the underlying theory of semiconductors, you will find it deeply uncomfortable to bet against the long-run rise of semiconductors.

Electric motors are also highly effective prime movers, awarded a score of 5.6 out of 6 on our scorecard, converting electrical energy into rotational energy, using the principles of electromagnetism. This is covered in our overview of electro-magnetism (below). Strictly, Rare Earth magnets are best, for electric vehicles and in wind turbines. And improved semi-conductors can also optimize the performance of motors that comprise around half of all global electricity consumption today.

Where electrical value chains struggle amidst the thermodynamics of prime movers is with storing electrical excitation. For this, it is necessary to turn to electrochemistry (primer below). Pure electro-chemical cells such as lithium-ion batteries have high efficiency but low energy density, which precludes their use in heavy-duty long-distance transportation, and probably always will. Plus they suffer from battery degradation. There are moving parts in a battery, in the form of ions, shuttling across the cell, and intercalating at the electrodes, undergoing unwanted side reactions along the way. Overall, we score batteries as a 4.2 out of 6 on our score of prime movers.

Finally electro-chemical fuels can have similar energy densities to hydrocarbons and low emissions. However, their round-trip efficiency is lower than Brayton cycle turbines. The key benefit of electrical systems is that they do not need to store and manage fuel, including the inevitable needs for maintenance, valves, pipes and moving parts. Resiliency is low, degradation is high, electrochemical cells perform poorly outside carefully controlled conditions such as temperature and humidity, somewhat like the Goldilocks of prime movers. This has come up in our patent screening (examples here and here). Today’s costs are also, in our view, prohibitively high. And without a willingness to pay very high green premia, we expect it may be hard to displace hydrocarbons using electro-chemical fuels.

Grid-scale battery costs: $/kW or $/kWh?

Grid-scale battery costs can be measured in $/kW or $/kWh terms. Thinking in kW terms is more helpful for modelling grid resiliency. A good rule of thumb is that grid-scale lithium ion batteries will have 4-hours of storage duration, as this minimizes per kW costs and maximizes the revenue potential from power price arbitrage.

Quantum mechanics asks us to think of the electron as both a particle and a wave. Despite the obvious fact that a particle is not a wave, and a wave is not a particle. This is probably a reason that most people do not love quantum mechanics.

Battery models similarly ask us to think about a battery as a ‘per kW’ device and as a ‘per kWh’ device. Where 1 kWh is the supply of 1 kW for precisely 1-hour (or some similar multiplication, such as 0.5 kW for 2-hours, or 0.25 kW for 4-hours, per our overview of energy units). Clearly, kW are not kWh and kWh are not kW.

Our own grid-scale battery model is guilty of this dualistic behaviour, quantifying the costs of grid-scale batteries both in $/kW terms and in $/kWh terms. Our view is that it makes marginally more sense to think about a grid-scale battery in kW terms, when modelling the costs of integrated power systems. But there is some flexibility.

Standalone batteries in kWh terms?

Battery costs are often quoted in $/kWh on a standalone basis, tabulated here, charted below, and showing the amazing deflationary profile of moving the mass manufacturing of batteries over the past decade and leaving mostly material costs (note the units of the y-axis).

Lithium ion battery materials
Lithium ion battery costs breakdown between materials and manufacturing

Especially in the realm of electric vehicles, this is the cost at which battery packs tend to be procured, for integration into a vehicle. And $/kWh is the most relevant cost metric when thinking about the enormous impending ramp-up of EV batteries.

Grid-scale systems in kW terms?

The output from a battery module is DC electricity at a voltage level driven by electrochemistry. However, circuits in the power grids consist of AC electricity at a very specific and pre-defined voltage. Hence power electronics are required to connect a battery into the grid.

An inverter containing multiple layers of MOSFETs is used to synthesize an AC sine wave from the DC output of a battery. Inverters are sized in kW terms, and priced in kW terms.

A transformer then steps up the voltage of the AC electricity to whatever level is required by the specific grid loop downstream. Transformers are sized in kW terms, and priced in kW terms.

A physical connection is then made between the step-up transformer and the circuitry of the power grid, using cables and other power electronics. These connections are usually rated in kW terms and their costs are best quantified in $/kW terms, or even per kW-km of transmission and distribution distance.

Resolving Duality: $/kW or $/kWh?

When we add up the total installed costs of a grid-scale battery, about 40% is the core battery, best measured in $/kWh; another 30-40% is the power electronics and grid connection, best measured in $/kW; and the remainder includes costs such as engineering, permitting, land-leasing, construction, which are best measured in absolute $ terms.

It is a philosophical choice how to present battery costs. You can add all of the cost lines together (in $) and divide them by the total power rating in kW (yielding a $/kW metric). Or you can add all of the cost lines together (in $) and divide them by the total energy storage in kWh (yielding a $/kWh metric).

Our own capex numbers are tabulated below for different systems, assuming that each one stores 4kWh of electricity per kW of rated storage capacity. This is not to say that all batteries must have 4-hours of storage, but just a simplification to enable apples-to-apples cost-benchmarking.

Grid-scale battery costs
Cost of medium duration energy storage solutions from lithium batteries to thermal pumped hydro and compressed air

Energy storage and power ratings can be flexed somewhat independently. You could easily put a bigger battery into your lithium LFP system, meaning the costs per kWh would go down, while the costs per kW would go up; or you could connect your LFP battery to a bigger inverter and transformer, meaning costs per kW would go down, while costs per kWh would go up.

“Somewhat independently” and the 4-hour battery?

A limitation of lithium batteries is that the faster you charge them and discharge them, the faster they degrade. The reasoning is explained in the note below. But in short, when a battery is charged and discharged, lithium ions physically need to move through the cell, intercalating and de-intercalating from electrodes. The faster you push this process, the more side-reactions will occur.

For safe and long-lasting batteries, it is recommended not to exceed a 0.25 C-rate. This means that no more than 25% of the battery’s total electricity storage will be cycled per hour. Or in other words, the charge time of a lithium ion battery should not be less than 4-hours, and the total discharge time at full capacity should be 4-hours. Faster charging and discharging are possible, but they may invalidate the battery’s warranty.

Grid modelling: why we prefer kW and $/kW metrics?

When we start modelling the integration of renewables into power grids, we are looking at grid loads (in MW) supplied moment to moment by different power generation sources (in MW). If a cloud passes over a solar array, or if some large power plant trips out during a heatwave, then the grid is going to be short of MW. If the grid does not have capacity to rapidly add MW, then the grid is going to fall over. This is why we care predominantly about batteries producing MW.

Grid-scale batteries will tend to minimize duration?

Today, sizing batteries is mostly about ensuring resiliency of the grid. Hence companies developing wind and solar, or consumers using wind and solar, tend to focus on the MW capacity ratings of batteries. And longer duration lithium ion batteries become more expensive on a $/kW basis (as they need to contain more battery cells priced in $/kWh).

Grid-scale battery costs
Capex costs of a lithium ion battery at longer duration in $ per kW and $ per kWh

Costs per unit of energy storage do fall as battery duration increases. The reason is that you are adding more battery cells priced in flat $/kWh terms, while other $/kW cost lines are being amortized across more energy storage. But is this leaving money on the table, in a way that will tend to incentivize building out the power electronics too?

It is 7-8pm in California. Power prices are high. And you have stored 100kWh in your battery. You really want to fill the gap at 7-8pm. If you can discharge all 100kWh at 8pm, that is going to generate the best economic results. But if you have undersized the power-electronics, and can only discharge 10kWh at 8pm, then money has been left on the table.

The old adage in traded commodity markets, is that the majority of the profit potential comes in the volatility, not in the core day-to-day spread. It does not cost materially more (in $/kWh terms) to build out the power electronics and buy the capability to run batteries at a 0.25C charge/discharge rate. It also helps to ensure resiliency.

Overall we still think most grid-scale lithium batteries will aim at around 4-hours of duration. At longer duration, we prefer redox flow batteries among electrochemical solutions. Even better backups are thermal energy storage and demand shifting. While for economic reasons, we think clean natural gas turbines will ultimately end up being the most widely used backup for renewables’ volatility.

Energy transition: active duty?

After five years researching the energy transition, we believe it favors active managers. Within the energy transition, active managers can add value by ranging across this vast mega-trend, balancing risk factors in a portfolio, timing volatility, understanding complexity, unearthing specific opportunities and benchmarking ESG leaders and laggards.

We recently enjoyed exploring this topic on the Capital Cyclists podcast. The theme matters because total associated investment needs to treble to $9trn per year to achieve an energy transition, and this is not going to happen if capital is misallocated. This short note explores ten advantages for active managers in the energy transition.

(1) Energy transition is a structural mega-trend. Not an isolated niche. It is the largest construction project in the history of human civilization, impacting markets worth $25 trn per year today and doubling them to $50 trn per year by 2050; re-shaping terms of trade for industrial eco-systems across the entire global economy; re-shaping ever more sectors that at first glance have nothing to do with the energy transition (note below). Net Zero means zero net CO2 emissions for every sector in the market. Hence in our view, the impacts of energy transition need to be considered across entire portfolios.

(2) Managing risk factors. Investing involves being paid to take risk. Energy transition investing involves at least ten risks (below). Constructing a portfolio involves minimizing overall risk via diversification. And on top of this, at any given point in time, the market may be willing to over-pay you to take some risks and under-pay you for taking others. The role of an active manager is that they are constantly appraising these risks, their balance and their relative attractiveness (as explored in the link below).

(3) Times are changing. We are told that we are one of few research firms with a fully modeled roadmap to net zero, including the resultant pull on all of the different themes, sectors, materials and commodities. The average one gains in size by 3-30x in the energy transition. Vast new markets scale up in Wind, Solar, Electric Vehicles, Batteries, underlying Metals and Materials, next-gen Nuclear, Electrification, the Rise of AI, Hydrogen, CCS, Nature. And yet vast value remains in oil, gas, LNG, plastics, possibly even coal. Vast changes — and the expectation of vast changes — are particularly likely to create dislocations in the ‘correct’ market prices of securities.

(4) Timing matters. There are many themes that we want to scale up as part of the energy transition. And ultimately they will scale up. But the trajectory will not be a straight line. We have recently seen share prices crushed in offshore wind (forewarned here) or PV silicon (forewarned here). Active managers can add value by avoiding looming walls of over-supply, or conversely, by finding industries that may pivot from unloved to station central (we have some ideas on this front too).

(5) Technical expertise is rewarded. Many of the big questions in energy transition come down to technology, which frankly, can only be understood by building up technical expertise. Will the rise of the solar industry add 40% to total global silver demand, or will silver be entirely thrifted out? (note here). Will the entire lithium mining industry be rendered obsolete by direct extraction from brines? (note here). Does CCS actually work? (note here). Our perspective is that active managers can add value where they build up a technical understanding of complex market debates (video below).

(6) Second order consequences? In an efficient market, obvious opportunities are quickly seized. Thus in our experience, the best opportunities are non-obvious opportunities, which markets have not yet fully grasped. If you are constructive on electric vehicles, then clearly “most obvious” is to own Tesla (the fact that it is 2% of the S&P 500 at 73x PE perhaps indicates the “obviousness”). Next most obvious is to own bottlenecked battery materials. Less obvious is the possibility that the rise of EVs will unexpectedly double the margins of polyurethane and textile fiber producers, which can source increasingly cheap BTX feedstocks as demand for gasoline declines (note below). Across all of our work, our biggest focus is to help our clients find non-obvious market dislocations.

(7) Fast evolving. We noted above that some markets in the energy transition are changing vastly. But others are also changing rapidly. Technology is advancing faster than ever before with 7M patents now filed each year and accelerating further due to AI. One thing we have observed in our research is that new themes can emerge very quickly. For example, in the past twelve months, after passing IRA regulations, the US is seeing a boom in blue hydrogen, blue ammonia, blue steel, blue chemicals. This has come out of nowhere. We think active managers can add value by latching on to new themes quickly.

(8) Bubble risks. It is also important to latch onto the right themes. Fantasy thinking can become commonplace during times of vast changes. We have worried that some themes in the energy transition show similarities with past bubbles (note below). A strange feature of index fund is that the more a security price inflates, the more of the security the index fund will own (by definition). Our favorite antidote to getting ruined by bubbles is sound economic modeling, hence we have built up a library of 170 economic models.

(9) Benchmarking leaders and laggards. Within individual sectors, markets are likely to reward ESG leaders and punish ESG laggards. But appraising leaders and laggards is not always simple. Our favorite example is illustrated in the refinery database below, as well as c50 broader data-files appraising CO2 intensity by industry.

(10) Technology opportunities? Finally, earlier-stage companies are increasingly raising capital, even becoming listed in public equity markets, despite being quite a binary ‘bet’ on an unproven technology. Is the technology real? Will it work? Does it have a moat? Will it change the world? Or could it be another Theranos? Index funds often do not get to ask these questions. But active managers do. And so do our patent screens.

Energy transition: active managers? Our view is that energy transition favors active managers, to appraise the market dislocations and complexities discussed above. For more information on our energy transition research, how we work with our clients, or if we can help you with an introduction on this front, then please do contact us.

Copyright: Thunder Said Energy, 2019-2024.