Wind and solar peak at 50-55% of power grids, without demand-shifting or storage, before their economics become overwhelmed by curtailment rates and backup costs. More in wind-heavy grids. Less in solar heavy grids. This 12-page note draws conclusions from the statistical distribution of renewables’ generation across 100,000 x 5-minute grid intervals.
Wind and solar are growing. Solar was 5% of all global electricity in 2022, and as much as 15% in leading countries; while wind was 7% of all electricity, and as much as 35% in leading countries (page 2).
Wind and solar peak? Intermittent renewables cannot ramp forever, because their generation is correlated across large areas. Eventually, grids get saturated, and further installations get curtailed (page 3).
As the basis of this analysis, we have compiled the statistical distribution of renewables’ generation across 100,000 x 5-minute grid intervals across California in 2022. Our methodology, and the reasons we like it, are summarized on pages 4-5.
When would solar peak in a solar-heavy grid, on a standalone basis? Our best estimate is at a c35-40% share, for the reasons on pages 6-7.
When would wind peak, in a wind-heavy grid, on a standalone basis? Our best estimate is at a 50-70% share, for the reasons on page 8.
When will wind and solar peak, in a renewables-heavy grid, on a combined basis? Our best estimate is 50-55%, for the reasons on page 9.
Higher or lower? Admittedly, our methodology makes some important simplifications, and further nuances are discussed on pages 10-12.
This 15-page note evaluates 10 commodity disruptions since the Stone Age. Peak demand for commodities is just possible, in total tonnage terms, as part of the energy transition. But it is historically unprecedented. And our plateau in tonnage terms is a doubling in value terms, a kingmaker for gas, plastics and materials. 30 major commodities are reviewed.
Confessions of a technology analyst. Over the past five years, we have been guilty of blurring the normative and the predictive. What we would like to happen from a moral, ideological and environmental perspective, is not necessarily what will happen.
This 15-page report is about evaluating past evidence of commodity disruptions, and making sober predictions. Examples of peak demand in abundant global commodities are surprisingly hard to find.
The Iron Age officially ended in AD43 when the Romans brought new technology to Britannia, yet global iron/steel demand remains at an all-time high of 2GTpa and rises another 80% by 2050.
The Bronze Age ended in 1,000 BC, yet global demand for copper and tin demand are still making new highs, and likely double again by 2050.
The Stone Age ended in 3,000 BC, yet stony concrete and coal, remain the #1 and #2 materials in the world, at >30GTpa and 8GTpa.
Where commodities have peaked it is often for good reason. Smelly and barbaric whale oil was 20x more expensive than rock oil. Asbestos is carcinogenic, yet has only seen demand fall by 75% in the past 50-years.
Statistics like these, elaborated on pages 2-9 of the report, make it seem unlikely that three of the largest commodity markets in the world by tonnage – coal, oil and gas – will all fall by over 80% within just 25-years.
The stickiness of commodity demand is explained on pages 10-12, including the most striking facts on global energy inequality, rebound effects and the need for substitutes (coal only collapses if gas doubles).
Thermal energy storage will outcompete other batteries and hydrogen for avoiding renewable curtailments and integrating more solar? Overlooked advantages are discussed in this 21-page report, plus a fast-evolving company landscape. What implications for solar, gas, lithium batteries and industrial incumbents?
Solar is the new energy source that excites us most, generating electricity from semiconductors, capable of another 50-100% efficiency gains in the next decade (here and here), deflating levelized costs (LCOPE basis) from 6-8 c/kWh today to 4 c/kWh in the global average utility-scale location. But ramping solar requires energy storage. To set some baselines, costs of different backups are re-capped on pages 2-3.
Thermal energy storage has surprisingly high energy efficiency, even at longer storage durations and across different storage media (pages 4-8).
Thermal storage has high charging capacity, more than electrochemical cells, which has overlooked benefits in power grids (pages 9-10).
Re-releasing energy is where the devil is in the details. Some challenges, and a ‘merit order’ of different heat uses, are on pages 11-13.
The costs of thermal energy storage will likely be at least 35% below lithium ion batteries, but competitiveness versus natural gas depends on the context, as is built up from first principles on pages 14-17.
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).
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.
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).
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.
The levelized cost of partial electricity (LCOPE) is very different from the levelized cost of total electricity (LCOTE). This 21-page note explores the distinction. It suggests wind and solar will peak at 30-60% of renewable-heavy power grids? And gas is particularly well-placed as a back-up, set to surprise, by entrenching at 30-50% of renewables-heavy grids?
Modelling power grids is one of the most challenging analytical exercises in the energy transition. However, in five years since starting TSE, we think this report contains our most important conclusions.
The report centers on a distinction between LCOPE and LCOTE. LCOPE is the cost to produce Partial Electricity, a partial share of a grid’s electricity, valuing renewable electricity simply “whenever it happens to be generated”. LCOTE is the cost to generate Total Electricity, the total needs of a grid, for example, supporting a 100MW load, 24-hours per day, 7-days per week. LCOPEs and LCOTEs are very different (pages 2-8).
Battery backstops cannot take solar and wind beyond a 30-40% share of most power grids, without excessively high system costs, in the range of $500/ton of CO2 abatement. This is not a normative comment about the importance of decarbonizing, or a comment that is ‘anti-renewables’ (we still think solar is a world-changing technology, whose costs will deflate by another 30-50% from here). It is simply a prediction, driven by economic analysis, per pages 8-15.
The other inescapable conclusion is that natural gas is going to emerge as the most pragmatic and cost-effective backstop for the volatility in renewable-heavy grids. Hence gas is going to ‘surprise’ by entrenching at a 30-50% market share within renewable-heavy grids. Again, this is not some normative comment about what should happen for ideological reasons. It is a prediction, for which our clients can hold us accountable, driven by numbers, per pages 16-21.
We have assessed over 100 markets in energy, materials, manufacturing and decarbonization across our research. More concentrated industries achieve higher margins across the cycle. But not always. This 10-page report draws out seven rules of thumb around market concentration to help decision-makers.
The usual format of a TSE research note is to explore a particular topic that matters for the energy transition: How does it work? What does it cost? What supply-demand outlook? What are the key technology challenges? And who are the leading companies? This means we now have a library of almost 100 company screens.
What can we conclude about profitability and concentration, by reviewing these screens? We want to derive some conclusions to improve our future research, and help decision-makers appraise opportunities in the energy transition.
The concentration metrics that we find most helpful are the ‘top five market share’ and the ‘Herfindahl Hirschman Index’, which are explained on pages 2-3, including examples from our database, such as industrial gases and the recent consolidation in US E&P.
What is the typical concentration of industries across energy, metals, materials, semiconductors and capital goods sectors? The Herfindahl Hirschman Index and top five market share are on page 4.
Do more concentrated industries generate higher margins? The answer appears to be ‘yes’, across the board, with a correlation coefficient of 50%. From this, we can also predict likely operating margins from market concentration metrics, per page 5.
But not always. 75% of the variance in different industries’ margins is explained by industry conditions and across the market cycle. This underscored the opportunity for active managers in the energy transition. We look at why lithium miners will tend to over-earn and wind turbine manufacturers will tend to under-earn (pages 6-8).
There is value in niches. Smaller markets are inherently less concentrated and higher margin. Sometimes we get feedback from our readers asking why we look at so many ‘niche industries’. This is the reason. Indeed, the epitome of a potentially low-competition and high-margin niche is in breakthrough technologies (page 9).
Mean reversion? Some industries have recently generated higher or lower margins than might be expected from their concentration. Hence which industries stand out as potentially ‘reverting to the trend line’ and what opportunities for decision-makers? (page 10).
This turned out to be one of our favorite research notes of 2023, and we think it adds useful context for investors in public markets, private equity and VC; across energy, materials, capital goods and emerging clean-tech.
Redox flow batteries have 6-24 hour durations and require 15-20c/kWh storage spreads. They will increasingly compete with lithium ion batteries in grid-scale storage. Does this unlock a step-change for peak renewables penetration? Or create 3-30x upside for total global Vanadium demand? This 15-page note is our outlook for redox flow batteries.
Flow batteries are fundamentally different from lithium batteries. They store and re-release energy as two separate electrolytes oxidize and reduce, at two separate electrodes, either side of a permeable membrane.
The image above shows how a Vanadium flow battery is charged, storing up energy in the ‘higher energy’ V2+ and V5+ oxidation states. The cell simply runs in reverse to discharge.
There are many different types of flow batteries, but this note focuses on the current front-runner chemistry, using Vanadium as the key reagent, which can exist in oxidation states from 2+, 3+, 4+ and 5+. The technology is explained on pages 2-4.
The levelized costs of storage for redox flow follow, after reflecting hurdle rates, efficiency losses and other opex. Flow batteries can be competitive with lithium ion batteries in grid-scale storage, per pages 7-8.
Why do flow batteries not dominate grid storage, if our numbers above are correct? What are the main drawbacks for redox flow batteries? Our answers and key ‘debating points’ are on pages 9-10.
How will redox flow rates re-shape the peak penetration of wind and solar in increasingly renewables-heavy grids? Our answers and key debating points are on pages 11-12.
Who are the leading companies in flow batteries? We can find smaller specialists, some listed, some private, and growing activity from listed large-caps on pages 13-14.
Global Vanadium Production runs at 100kTpa according to data from the USGS. We have quantified Vanadium demand and profiled some leading Vanadium producers on pages 14-15.
110MTpa of hydrogen is produced each year, emitting 1.3GTpa of CO2. We think the market doubles to 220MTpa by 2050. This is c60% ‘below consensus’. Decarbonization also disrupts 80% of today’s asset base. Our outlook varies by region. This 17-page hydrogen outlook explores the evolving market and implications for industrial gas incumbents?
Today’s 110MTpa hydrogen market is first broken down by product type, end use, geography, supply sources and hydrogen cost ranges (pages 2-4).
Producing 110MTpa of hydrogen in 2023 emits 1.3 GTpa of CO2, which is 2.5% of total global CO2 emissions, at an average CO2 intensity of 12 tons/ton (page 5).
There is no shortage of commentary into how the world should decarbonize. Every commentator is entitled to their views. Our goal in this report is to guess what will actuallyhappen, to black, grey, blue, turquoise and green hydrogen (pages 6-8).
Five recommendations for industrial gas companies follow from this view, some for the 2020s, others for the 2030s and beyond (page 9).
Refining industry disruption creates downside to hydrogen demand in some geographies. Refineries use 25-30% of the world’s hydrogen, mainly for hydroprocessing. While oil demand is flatlining in some geographies and declining in others (pages 10-11).
Hence our hydrogen outlook varies by region. The trajectory looks very different in US hydrogen markets, Europe, the broader OECD and non-OECD (pages 12-16).
Semiconductors underpin solar panels, electric vehicles and electronics. Hence this 20-page note aims to explain semiconductor physics from first principles: their conductivity and resistance, their use in devices, plus implications for materials value chains and the energy transition itself?
Semiconductors are a true workhorse of the energy transition, especially if solar efficiency doubles again, to enable AI, and for electrification.
A salty observation might be that commentators love to make sweeping statements about all of these initiatives, without really understanding how semiconductors work or “what is a Fermi Level?”.
We have read several textbooks and technical papers into semiconductor physics. The purpose of this note is to distil the underlying theory, into 20 concise pages, which are relevant to semiconductor devices, materials value chains and energy transition itself.
The resistance of metals is explained on pages 2-3. From first principles, why does the copper used in electronic devices have a conductivity of 5.9 x 10^5 S/cm and a resistivity of 1.7 x 10^-6 Ωcm.
The resistance of semiconductors is different from the resistance of metals, because of the complexity of their charge carriers. Pages 4-7 cover the physics: the Pauli Exclusion Principle, the Fermi-Dirac distribution, the Density of States, the Boltzmann constant, bandgaps, and key formulas for semiconductor conductivity (calculations here).
Semiconductors are really not very good conductors. This is why a solar panel contains more copper than silicon and always will (page 8).
Doping can be used to achieve different conductivity and resistance profiles in semiconductors, introducing precise amounts of pentavalent impurities (N-type) or trivalent impurities (P-type). How does doping change the physics? How much dopant is introduced? Why has the solar industry shifted from P-type cells to N-type cells? (please see pages 9-10).
Semiconductor theory allows you to understand PN junctions (page 11), light emitting diodes (page 12), solar modules (and their inevitable progress towards being the world’s lowest cost energy supply source, pages 13-14), transistors (page 15), their combination into MOSFETs, inverters, and converters (page 16) and the rise of wide bandgap technologies such as silicon carbide (SiC) (page 17).
What happens to commodity prices during conflicts? This 10-page note charts how fourteen commodities were affected, across a dozen conflicts, going back to 1800. During major conflicts, 95% of commodities saw higher prices. The average commodity doubled. A strong role is implied for commodities hedging portfolios and even entire nations against conflicts.
Supply-demand models for global energy and materials have long worried us. We think the world has under-invested in energy by $1trn since 2016, portending 2-6% global energy shortages from 2024-30.
This is assuming everything goes right. What is not factored into our numbers is the possibility of a catastrophic disruption to global supply chains. News flow in 2023 seems to provide a constant ominous rumbling.
Hence the purpose of this report is to gather data into how global conflicts have impacted commodity markets in the past, using our database of very long term historical commodity prices, which covers fourteen commodities – energy, materials, manufactured goods and agricultural products – going back to 1800.
During these five major conflicts, 95% of commodities saw higher prices, and the median commodity price rose by 110% from pre-conflict annual trough to mid-conflict annual peak (page 3).
No two conflicts were the same. However, a pattern across all of the conflicts is that the commodities seeing the most supply disruption also saw the highest price eruptions. Disrupted commodity prices often rose 2-10x. Examples and implications are on pages 4-6.
Another form of disruption is that conflicts tend to suck in attention, people, energy, materials, capital and other resources. Impacts on the prices of manufactured goods are discussed on page 7.
What implications for decision-makers in the energy transition and beyond? Our own perspectives on protecting portfolios, and even entire nations, are discussed on pages 8-10.
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