Industry data for the energy transition. Many of our data-files simply aim to quantify how specific industrial processes work, how they emit CO2, and how they can be decarbonized.
The purpose is simply to help decision-makers understand opportunities and challenges in the energy transition, backed up with data, which is transparent and easily accessible.
Examples include the volatility of solar power generation, what it costs to maintain a wind turbine, what drives the degradation rates of grid-scale batteries, gas flaring rates, the conductivity of different metals, or the share of energy sources in different countries’ power mixes.
If we can construct a data-file to help you, or aggregate any particular industrial data-sets that matter for the energy transition, then please contact us, and we will be delighted if we can help you save time and get useful industry data for the energy transition.
Global electricity demand for electric vehicles will rise from 120TWH in 2024 to 500 TWH in 2030 and 3,300 TWH in 2050, ultimately adding 11% upside to today’s global electricity demand, as part of our roadmap to net zero. This data-file quantifies electricity demand for EVs by region and over time, including data into the real-world fuel economy of EVs.
The key advantage of electric vehicles is their 4x higher efficiencies than ICEs, harnessing 80-90% of input electricity, whereas an ICE vehicle surrenders 50-70% of the energy content of fuels as waste heat, which is exhausted from the tailpipe.
The real-world fuel economy of electric vehicles is around 0.2 kWh/km, which equates to 120mpge, based on studies and self-reported data tabulated in a tab of this model (chart below). Note real world energy consumption can be 20-60% higher than stated by manufacturers or on test-cycles, especially during cold weather.
Global electricity consumption for electric vehicles likely reaches 120TWH in 2024 (0.4% of global electricity), rising to 500 TWH in 2030, 1,750 TWH in 2040 and 3,300 TWH in 2050, based on our numbers, which in turn link to our EV forecast databases.
Without any expansion of the grid, our numbers entail that EVs would add 1.6% upside to today’s global electricity demand by 2030, 6% by 2040 and 11% by 2050. The upside is mostly back-end-loaded, while near-term electricity demand is driven more by data-centers, which could require an additional 1,000 TWH by 2030.
However, we also see global electricity use more than doubling to 70,000 TWH by 2050 in our roadmap to net zero. If this ramp-up happens, without falling foul of power grid bottlenecks, then EVs will comprise 1.7% of global electricity in 2030, 3% in 2040 and 5% in 2050.
Expanding the grid is necessary for meeting electricity demand for electric vehicles, for displacing 25Mbpd of oil demand by 2050 (14,000 TWH-th of primary energy). Another requirement lies in charging networks. But we see the biggest bottlenecks and opportunities in reconductoring transmission and in urban distribution.
Annual commodity prices are tabulated in this database for 70 material commodities, as a useful reference file; covering steel prices, other metal prices, chemicals prices, polymer prices, with data going back to 2012, all compared in $/ton. 2022 was a record year for commodities. We have updated the data-file for 2023 data in March-2024.
Material commodity prices flow into the costs of producing substantively everything consumed by human civilization, and increasingly consumed as part of the energy transition. Hence this database of annual commodity prices is intended as a useful reference file. Note it only covers metals, materials and chemicals. Energy commodities and agricultural commodities are covered in other TSE data-files.
Source and methodology. The underlying source for this commodity price database is the UN’s Comtrade. This useful resource covers trade between all UN member countries, across thousands of categories, in both value terms ($) and mass terms (kg). Dividing values (in $) by masses (in kg) yields an effective price (in $/kg or $/ton). We have then aggregated, cleaned and averaged the data for 70 materials commodities.
The median commodity in the data-file costs $2,500/ton on an unweighted basis. Although this ranges from $20/ton for aggregates to $75M per ton for palladium metal.
2022 was a record year for material commodity prices. The average material commodity priced 25% above its 10-year average and 40 of the 70 commodities in the database made 10-year highs.
Steel prices reached ten-year highs in 2022, averaging $2,000/ton across the different steel grades that are assessed in the data-file. This matters as 2GTpa of steel form one of the most important underpinnings in all global construction. Our steel research is aggregated here.
Steel Price by year by steel grade in $ per ton
Base metal prices averaged 40% above their ten-year averages in 2022, as internationally traded prices rose sharply for nickel, rose modestly for aluminium and zinc, and remained high for copper (chart below).
Base metal prices by year and over time for zinc, aluminium, copper, and nickel in $ per ton
Battery metals and materials prices rose most explosively in 2022, due to bottlenecks in lithium, cobalt, nickel and graphite. This is motivating a shift in battery chemistries, both for vehicles and for energy storage. It also means that the average battery material in our data-file was higher priced than the average Rare Earth metal in the data-file (which is unusual, but not the first time).
Battery material prices over time $ per ton for lithium, cobalt, manganese, nickel, LiPF6 and lithium carbonate in $ per ton
Commodity chemicals all rose in 2022 across every category tracked in our chart below. These chemicals matter as intermediates. On average, sodium hydroxide prices reached $665/ton in 2022, sulphuric acid prices reached $140/ton and nitric acid prices reached $440/ton.
Industrial Acids and Caustic Soda Prices over time. NaH, H2O2, HCl, H2SO4 Sulfuric Acid, HNO3 Nitric Acid, H3PO4 Phosphoric Acid, HCN and HF in $ per ton
500MTpa of global plastics and polymers demand is covered in our plastics demand database. Both finished polymer prices (first chart) and underlying olefins and aromatics (as produced by naphtha crackers, second chart) prices rose sharply in 2022. Our recent research has wondered whether terms of trade are likely to become particularly constructive for polyurethanes.
Polymer prices by year LDPE HDPE PET EVA Polyurethanes Paints and Adhesives in $ per ton
Olefins and Aromatics Prices over time
Silicon prices matter as they feed in to the costs of solar, and traded silicon prices also reached ten year highs in 2022, before correcting sharply in 2023. Silica prices surpassed $70/ton, silicon metal prices reached $4,000/ton and polysilicon prices surpassed $30/kg (charts below).
Silica price, silicon price and polysilicon price in $ per ton
Oscar Wilde noted that the cynic is the man who knows the price of everything, but the value of nothing. To avoid falling into this trap, we also have economic models for most of the commodities in this commodity price database.
We will continue adding to this commodity price database amidst our ongoing research. You may find our template useful for running Comtrade queries of your own. Or alternatively, if you are a TSE subscription client and we can help you to use this useful resource, then please do email us any time.
Transaction prices for power generation assets are tabulated in this data-file, capturing 65 deals for gas plants, wind, solar, hydro and nuclear, globally and over time. Median prices are c$1,000/kW, but range from <$400/kW in the lower decile to >$2,500 in the upper decile.
Transaction prices for power generation assets vary widely in different contexts. This data-file helps to understand prices paid, and how they are changing over time.
Transaction prices for gas generation assets have been lowest among the different categories, averaging $500/kW over the past decade, which is actually below the costs of constructing new CCGTs at c$950/kW.
Low prices attribute to overcapacity and higher gas prices, especially in Europe and in 2014-2015. However the value of gas plants has been increasing over time, and recent deal prices have surpassed $1,000/kW.
Transaction prices for wind assets and solar assets have been highly variable, ranging from $400/kW to $4,000/kW. It all hinges on the strike price and duration of power purchase agreements.
For example, a pair of solar assets in Japan transacted at $4,000/kW in July-2017, backstopped by 25-28c/kWh PPAs lasting for another 19-years. Conversely, renewable assets transacting at $400-600/kW tended to sell their power on a merchant basis.
Transaction prices for low-carbon baseload generation, such as hydro plants and nuclear plants were highest, averaging $1,500-2,000/kW, however fewer assets in these categories change hands.
In some cases, nuclear deal prices have been distorted to the downside by the assumption of decommissioning liabilities. And we think the value of these assets may be higher than measured in the data-file.
Transaction prices for power generation assets are tabulated in this data-file, capturing over 65 transactions, sorted by region, acquirer, seller, deal price (in $M), generation capacity (MW), transaction price ($/kW), plus notes contextualizing each transaction.
Note, this database was last updated in August-2024, and contains 10 data-points for 2024, which are not shown in the title chart above.
The volatility of power grids has trebled over the past decade from 2013-2023. This data-file tracks the percentile-by-percentile distributions of power prices, each year, in six major grid regions (Texas, California, US MidWest, Australia, the UK and Spain), as a way of tracking increases in global power price volatility.
But how much is global power price volatility actually rising? You can drown in data, trying to answer this question!
Hence the goal of this tracker file is to tabulate the percentile-by-percentile distributions of power grids, across 8,760 hourly data-points each year, across >10-years, going back to at least 2013, in different developed world regions, and based on data from ERCOT, CAISO, MISO, Elexon, AEMO and OMIE. We selected these regions, as they have steadily been increasing their share of wind and solar.
Volatility has increased, in every single market in the data-file (based on correlation coefficients on each tab), and when aggregating all of the data together, where in turn, standard deviations of hourly power prices have trebled from $20/MWH in 2013 to over $60/MWH in 2023.
If we compare the pricing distributions in some individual years, then three observations stand out from the chart below.
(i) Average power prices are higher, rising from $40/MWH in 2013 to $65/MWH in 2023
(ii) Prices in the upper 20% of all hours have risen most, doubling from $60/MWH in 2013 to $130/MWH in 2023, and explaining 60% of the total increase in annual average power prices.
Yet (iii) Prices in the lower 20% of all hours have fallen, from $25/MWH in 2013 to $20/MWH in 2023, reflecting times when grids are over-saturated with renewables.
The most striking data in the file are in ERCOT, where median settlement prices have fallen from $25/MWH in 2013 to $22/MWH in 2023, yet mean average power prices have risen from $31/MWH to $48/MWH over the same timeframe, which is entirely driven by pricing in the upper 20% of the distribution rising from $55/MWH in 2013 to $165/MWH in 2023, while the lower 20% tail has collapsed from $25/MWH in 2013 to below $20/MWH in 2023.
The full distributions of power prices, across each percentile of hours each year, across each year from 2013-2023, and across each market – Texas, California, MISO, SW Australia, UK and Spain – can be reviewed in the data-file, for decision-makers who wish to delve deeper into the charts and how global power price volatility is rising.
US gas pipeline capex ran at $12bn pa in 2023, but likely needs to treble to reach net zero by 2050, mainly to support 1GTpa of CCS. Midstream capex for natural gas, CO2 transportation and hydrogen production are forecast out to 2050 in this data-file. Numbers can be stress-tested in the model.
The US operates the most extensive gas pipeline network in the world, moving 100bcfd of natural gas through 200,000 miles of transmission lines.
To build this network, the US has spent $14bn pa over the past decade, to construct a further 10,000 miles of transmission lines, which can carry 120bcfd, at an average capex cost of $3M/mile; while another c50% of the capex is spent maintaining the existing network.
Achieving net zero by 2050 would likely require total US gas pipeline capex to treble to almost $40bn per year, mainly as CCS volumes must ramp to 1GTpa, but also as gas displaces coal in the short-medium term and US hydrogen volumes almost double from 12MTpa to 21MTpa in our hydrogen outlook.
Our forecasts for new gas pipeline capex, CCS pipeline capex and hydrogen pipeline capex are calculated, in each case, by multiplying incremental volumes x new pipeline diameter-kilometers needed per unit of volume x capex cost per diameter-kilometer.
Capex costs of US gas pipelines are informed by a comprehensive database published by the EIA, which we scrubbed and cross-plotted, providing a good estimate for capex costs in $M per meter of diameter and per km of length (chart below).
The US already contains 50 CO2 pipelines with 5,000 miles of length, implying 100-miles per line. However, the main gas transmission network consists of 30 x large lines each running 2,000 – 15,000 miles. GTpa scale CCS in the US could continue leaning on smaller regional lines, but likely also requires an interstate network, raising the mean average CCS line length to 1,250 miles by 2050. Whether the US adopts CCS simply in regional hubs, or more extensively, is thus the largest determinant of total midstream capex requirements through 2050.
Estimates for the number and diameter of pipelines needed per bcfd or per MTpa – of natural gas, CO2 and hydrogen pipelines – are derived from the engineering equations in our US gas pipeline models.
All of the numbers can be stress-tested in the model. However, a helpful broader reference file is our roadmap model for US decarbonization. Our outlook for gas pipelines in the energy transition is also informed by recent research, summarized below.
Japan’s oil demand peaked at 5.8Mbpd in 1996, and has since declined at -2.0% per year to 3.4Mbpd in 2023. To some, this trajectory may be a harbinger of events to come in broader global oil markets? While to others, Japan has unique features that will not generalize?
The 7-page report, linked via the first button below, contains our own observations into Japan’s oil demand, which does not generalize globally.
The data-file, linked via the second button below, contains all of the underlying data, to interrogate Japanese oil demand over time.
Our roadmap to net zerosees global oil demand rising to 105Mbpd in the mid-late 2020s, then declining at a rate of -1%pa to 85Mbpd by 2050. But does Japan’s decline in oil demand, set a precedent for steeper declines ahead?
This 7-page note argues that there are key features of Japan’s energy mix, which mean its history cannot be generalized more broadly: including Japan’s reliance on imports motivating efficiency gains across the board (pages 2-3), declines in manufacturing activity (pages 4-5) and the underlying structure of Japan’s oil market, which has always been weighted to easy-to-substitute categories (pages 6-7).
The underlying data-file breaks down Japan’s oil demand over time, based on data from METI, across Passenger Vehicles, Commercial Vehicles, Motorcycles, Taxis, Buses, Trucking, Rail, Aviation, Shipping, Agriculture, Mining, Construction, Steel, Chemical Feedstock, Chemicals Heat, Materials, Food, Industrial Heat, Industrial Steam, Retail, Hotels, Restaurants, Hospitals, Schools, Waste Collection, Commercial, Power Generation, Residential Heat, Refineries, Lubricants, Asphalts, Petcoke, annually, from 1990 to 2023.
The underlying data-file also breaks down Japan’s oil demand across all of these categories, for different oil products: total oil products, gasoline, distillates, jet fuel and fuel oil.
Electric vehicles’ total cost of ownership remains 40% higher than ICE vehicles’, at $7,000 per year, versus $5,000 per year, all based on the latest 2024 data, for 50 vehicles. Electric vehicle up-front prices are 55% higher, insurance costs are 30% higher, while energy costs are 60% lower. 20 different pricing metrics are compared and contrasted in this data-file.
Total costs of ownership for electric vehicles and ICEs are built up in this data-file by evaluating 50 sedans, SUVs, pick-up trucks, hybrids, plug-in hybrids and battery electric vehicles, and compiling key metrics.
All of the data are gathered apples-to-apples in the US, for comparability. For example, we have taken starting prices for vehicles (i.e., without add-ons), obtained quotes for total cover insurance for each vehicle, and are using the same mileage, gasoline prices and electricity prices across the board. These variables can be flexed in the data-file.
Total costs of ownership across all categories run to $6,000 per vehicle per year, of which 45-50% is amortization of the up-front cost of the vehicle, 35-40% is insurance and 15% is fuel/electricity.
The total cost of ownership for electric vehicles screens as being 40% higher than for an ICE, based on the examples tabulated in this data-file, at $7,000 per year and $5,000 per year, respectively.
Electric vehicles’ total cost of ownership is higher due to 55% higher up-front vehicle cost (mainly due to the batteries), 25% shorter vehicle lifetimes, 33% higher insurance costs (due to the risks of battery fires) but -60% lower annual fueling costs.
Are the data comparable? The average ICE and EV in our data-file both had the same internal passenger volume (in m3). The ICEs have the advantage of 2x longer ranges (in km or miles). The EVs have the advantage of 50% higher horsepower, and 4x higher fuel economy (in mpg or kWh/km).
We have compiled 20 key charts in the data-file (examples below), contrasting the different vehicles, comparing different cost metrics versus different performance metrics.
Electric vehicles are already cost competitive when comparing total cost of ownership per unit of horsepower. However, they are 2.5x more expensive per km of range, and 40% more expensive per m3 of internal passenger volume.
Pumped hydro facilities can provide long-duration storage, but the utilization rate is low, and thus the costs are high, according to today’s case study into the pumped hydro generation profile within the Snowy Hydro complex in Australia. Tumut-3 can store energy for weeks-months, then generate 1.8 GW for 40+ hours, but it is only charging/dischaging at 12% of its nameplate capacity.
Tumut-3 is the largest single generating facility in the Snowy hydro and pumped hydro project, in New South Wales of Australia, whose history goes back to 1949.
1,800MW of power can be generated when up to 4,300 m3/s of water descends 150m under gravity through 6 x 300MW Toshiba turbines, from the 2,000 hectare Talbingo Reservoir into the 400 hectare Jounama Pondage, then onwards into the Blowering hydro plant.
Equally, 600MW of power can be ‘stored’, when 300 m3/s of water is pumped back up from Jounama to Talbingo, by each of 3 large pumps.
Real-world data into Tumut-3 matters generally for the costs of using pumped hydro to backstop renewables, and specifically for the A$12bn and 2.2GW Snowy 2.0 project, under construction, and featuring 27km of tunnels, the longest of any pumped hydro station ever built. Snowy 2.0 is directly adjacent to Tumut-3, using Talbingo as its lower reservoir, and cycling water by 700m into the Tantangara Reservoir.
Hence we have compiled the charging and discharging data, at 5-minute intervals for Tumut-3, across all of 2021-23 (over 50MB of data), using data from AEMO.
Example: 5-minute-by-5-minute generation on the single most active and single least active day of September-2023, at the Tumut-3 pumped hydro storage facility.
Long duration storage is clearly provided by the facility, as shown in the scatter plot below. Monthly charge and discharge are 82% correlated, but not identical. Statistically, 70% of the energy stored by Tumut-3 is re-released in the same month, and the other 30% is longer-duration.
If we zoom in on months with very large quantities of discharging, but small quantities of charging, such as May-2021 or November-2022, then we can see up to 40-hours of cumulative discharging. This is about 10x longer than a lithium ion battery.
Even longer storage durations could be achieved by constructing larger pumped hydro facilities, with larger reservoirs, both upstream or downstream. This is really the only viable long-duration battery, available at large scale today, while development progress continues with redox flow batteries, compressed air or novel chemistries.
However, the key challenge is low utilization. On average from 2021-23, charging occurred at a rate of 1.6 MWH of charging per day per MW of capacity, resulting in 1.2 MWH of discharging per day per MW of capacity.
One reason for the low charging-discharing activity at Tumut-3 in 2021-23 is that heavy rainfall occured in 2022, and thus there were risks of the Blowering Reservoir and the Tumut River flooding. Some may argue that this is simply the nature of the beast of managing hydro assets. Others may wish to adjust future utilization factors upwards.
Either way, and for comparison, a lithium ion battery with daily charging and discharging achieves 4 MWH per day per MW of capacity. And the base case in our pumped hydro cost model is for 5 MWH per day per MW of capacity, which in turn requires a storage spread of 25 c/kWh. At 2 MWH per day of charging per MW of capacity, then the same model requires a storage spread of 60c/kWh.
The key challenge, borne out in this case study, is that long-duration batteries tend to achieve low utilization, which hurts their economics. Hence we think the rise of renewables will entrench natural gas.
Finally, the evidence suggests that a typical pumped hydro generation profile is less actively used for short-term grid smoothing than lithium-ion batteries. This is borne out by charts in the data-file versus charts in our grid-scale lithium ion battery case study. Data into the charging-discharging by time of day, are shown in the data-file. Underlying data behind all of our charts are also contained in the data-file.
Coal power generation is aggregated in this data-file, at the largest single-unit coal power plant in Australia, across five-minute intervals, for the whole of 2023. The Kogan Creek coal plant produces stable baseload power, with an average utilization rate of 85%. But it exhibits lower flexibility to backstop renewables than gas-fired generation.
Kogan Creek is a coal-fired power plant in Brigalow, Queensland, Australia, located 230km west of Brisbane. It has a nameplate capacity of 787MW. It is thus the largest single coal unit in Australia.
As a case study for the flexibility of large-scale coal generation, we have evaluated this coal plant’s output, every 5-minutes over 2023 (105,000 data-points!), using data from AEMO.
Average utilization for the Kogan Creek coal plant in 2023 was 85%, varying remarkably little over the year, as coal provides low-cost baseload to the grid. The plant’s high utilization was mainly hampered by shutdowns, including a two-day outage in October and a five-day outage in August, lowering monthly utilizations to 71% and 66% respectively.
Volatility for a coal plant is low by design. The average 5-minute-by-5-minute volatility of Kogan Creek is +/-1%, while a typical large solar or wind installation is +/- 5%. For wind and solar, this is true volatility. But for coal, it is mostly flexibility, i.e., intentional variation in output levels in response to grid demand and grid pricing.
Several outages occurred at Kogan Creek in 2023, ranging from a couple of hours to several days. Ramping up to full capacity from a cold start appears to take 4-8 hours (chart below). This fits our broader data into power plants’ ramp rates from a cold start.
Solar power has a clear impact on the daily profile of coal generation. Similarly to Stockyard Hill wind farm, output at Kogan Creek was 10-20% lower than average between 8am and 3pm over the whole year. Coal ramps down in the middle of the day to make room for solar but still provides 60% of all electricity produced in Queensland.
This is yet another entry in our series analyzing the generation profiles of different electricity sources using data from the Australian grid. Previously we have looked at gas, solar, wind, and battery storage. Our key conclusion remains that gas-fired generation will entrench as the leading backstop for volatile renewables.
Reserve margins across major ISOs in the US power grid average 29% in 2024, are seen declining to 21% in the next decade by NERC, but could decline further, falling below their recommended floors of at least 15%. Possible reasons include demand surprising to the upside, or controversies in the capacity contributions of renewables. This data-file tabulates reserve margin forecasts, by ISO region, and over time.
Reserve margins are calculated by dividing (a) total power generation resources (in MW) that are seen to be available during times of peak grid demand by (b) total anticipated peak grid demand (in MW). Then subtract 1 to yield a percentage figure.
NERC guidelines recommend keeping reserve margins well above 15%, in order to limit Loss of Load Expectations (LOLE) to 1 event per 10-years, as part of resilient power grids.
Aggregated across major US ISOs, reserve margins currently average 29% in 2024, are projected by NERC to decline to 21% in the next decade, but could decline further if power demand surprises to the upside, or resource additions are delayed or disappoint.
This data-file aggregates NERC’s reserve margin forecasts over time, for major ISOs in the US, such as MISO, PJM, ERCOT, CAISO, NYISO, ISO NE, SPP and SERC FLA. Underlying charts are available on a separate tab for each region. We have aggregaed all the regions together in the charts above.
In each case, we have plotted expectations for peak demand, net demand after demand responses and anticipated resources, which in turn comprise existing firm resources plus Tier 1 capacity additions.
In the past, reserve margins have defied pessimistic projections. The main reason has been downwards reivisions in demand, and upwards revisions in renewables resources. What is changing is that demand is now surprising to the upside, linked to the rise of EVs and the rise of AI.
Another controversy in measure reserve margins is how to count the capacity from renewables. 100MW of gas generation is almost always available to provide 100MW. We think the forecasts from NERC and from underlying ISOs may be ascribing 50-60MW of availability per 100MW of renewables. But due to the intercorrelation of renewables, and especially as renewables get built out, this may turn out to be too high.
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