Download Category: Demand

Our global energy demand research aims to break down the world’s 70,000 TWH of useful energy demand, by category, by process, by fuel.

Useful energy demand has risen at a 2.5% CAGR over the past decade and will likely surpass 100,000 TWH by 2050. Electricity comprises 40% of the total, and this share has changed little in the past decade. Wind and solar now comprise 10% of all electricity, c4% of all global energy. But around 85% of total useful energy still comes from three fossil sources: coal, gas and oil.

Our recommendation for understanding energy flows is to use the kWh family as a common currency. 1kWh is equivalent to running a home dishwasher cycle. Making different materials usually requires 0.3 – 30 MWH per ton. Entire industrial facilities tend to consume energy at the GWH-scale. The largest power plant in the world, China’s Three Gorges Dam, produces 110 TWH per year. While entire countries tend to consume thousands of TWH per year.

Our global energy demand research breaks down the world’s energy use across different categories: such as air-conditioners, aviation, crushing-grinding operations in the mining, through to efficiency factors for power generators and smelting metals. In many of these sub-cases, we have been able to tabulate demand data facility-by-facility.

Global energy: supply-demand model?

global energy supply-demand

This global energy supply-demand model combines our supply outlooks for coal, oil, gas, LNG, wind and solar, nuclear and hydro, into a build-up of useful global energy balances in 2023-30. We fear chronic under-supply if the world decarbonizes, rising to 5% shortages in 2030. Another scenario is that emerging world countries bridge the gap by ramping coal. Numbers can be stress-tested in the model.

Useful global energy demand grew at a CAGR of +2.5% per year since 1990, and +3.0% per year since 2000. Demand would ‘want’ to grow by +2% per year through 2030, due to rising populations and rising living standards (model here). We have pencilled in +1.75% pa growth to this model to be conservative.

Combustion energy is seen flat-lining in our net zero scenario. This includes global coal use peaking at 8.4GTpa in 2024 then gently easing to 2010 levels by 2030 (model here). It includes oil demand, rising to 102Mbpd in 2024 (data here), then plateauing (model here) as OPEC and US shale (model here) offset the decline rate impacts of conventional under-investment. It includes risked LNG supplies rising +70% from 400MTpa in 2022 to almost 700MTpa by 2030 (model here). While our roadmap to net zero would need to see global gas growing at +2.5% per year through 2050 (model here), this data-file has pencilled in flat production in 2022->30, as we think that latter scenario currently looks more likely to transpire.

Renewable energy is exploding. Our model of wind and solar capacity additions is linked here and discussed here. In our roadmap to net zero, solar more than doubles from c220GW of new adds in 2022 to 500GW by 2030, while wind rises from c100GW of new adds in 2022 to 150GW by 2030.

Other variables in the model include rising energy efficiency (note here), the need for a nuclear renaissance (note here) and other variables that can be flexed.

What is wrong with this balance is that it does not balance. The assumptions pencilled into the model see an under-supply of global energy of about 3% in 2025, rising to 5% in 2030. I.e., by 2030, the world will be “half a Europe” short of energy. The first law of thermodynamics dictates that energy demand cannot exceed supplies. So what would it take to restore the balance? Well, pick your poison…

(1) Slower demand growth could re-balance the model. Very high energy prices might mute demand growth to only +1.25% per year, although this would be the slowest pace of demand growth since the Great Depression, lower even than during the oil shocks (useful data here). Unfortunately, our view is that pricing people out of the global energy system in this way is in itself an ESG catastrophe.

(2) Ramping renewables faster could re-balance the model, although it would require an average of 1 TW pa of wind and solar capacity additions each year in 2024-30, and over 2 TW pa of wind and solar additions by 2030 itself, which is 3x higher than in our roadmap to net zero (discussion here). For perspective, this +2TWpa solution requires primary energy investment to quadruple from $1trn pa to at least $4trn pa, which all needs to be financed in a world of rising rates. It means that global wind and solar projects will consume over 200MTpa of steel, which is 2x total US steel production, and yet steel would not even qualify as a ‘top ten’ bottleneck, in our wind bill of materials or solar bill of materials. This scenario also requires a 3x faster expansion of power grids and power electronics than our base case estimates (see the links). Is any of this remotely possible?

(3) Continue ramping coal? The main source of global energy demand growth is the emerging world. The emerging world is more likely to favor cheap, dirty coal. Or worse, deforestation for firewood. Thus another way to ameliorate under-supply in our global energy supply-demand balance is if global coal continues growing, reaching a new peak of 9GTpa in 2030. Unfortunately, this scenario also sees global CO2 hitting a new peak of 54GTpa in 2030.

(4) Pragmatic gas? Another means of re-balancing the global energy system is if global gas production rises at 2.5% per year, which is the number required, and that is possible, on the TSE roadmap to net zero (model here). This scenario does see global CO2 falling by 2030. The main problem here is that pragmatic natural gas investment has become stranded in no man’s land, within a Manichean duality of fantasies and crises.

(5) Some combination? The world is complex. It is unlikely that a single lever will be pulled to resolve under-supply in our global energy supply-demand balance. In 2023, we think economic weakness will mask energy under-supply, mute energy prices, and lure many decision makers into looking at spot pricing and thinking “everything is fine”. Please download the model to stress-test the numbers, and different re-balancing solutions…

Decarbonize shipping: alternative fuel costs?

This data-file screens the costs of alternative shipping fuels, such as LNG, blue methanol, blue ammonia, renewable diesel, green methanol, green ammonia, hydrogen and e-fuels versus marine diesel. Shipping costs rise between 10% to 3x, inflating the ultimate costs of products by 0.1-30%, for CO2 abatement costs of $130-1,000/ton. We still prefer CO2 removals.

Shipping consumes 5Mbpd of global oil demand, emits 1.5% of the world’s CO2, and adds c1% to the final cost of a typical shipped product, using $1.8/gallon marine diesel at 9.0 kg/gal CO2 intensity.

This data-file appraises the costs of alternative shipping fuels, drawing on models from our prior work into methanol, lower-carbon ammonia, renewable-diesel, green hydrogen, and electrofuels.

In each case, we have estimated the inceased fuel costs of alternative shipping fuels versus marine diesel; plus the increased capex costs of ships that can handle these different fuels, increased maintenance costs and other increased operational costs. This draws on our models of container ships, bulk shipping, LNG tankers and other vessel types.

The title chart above shows a base case where shipping with marine diesel fuel adds 1% to the final price of a product that is transported between continents, and emits about 100kg of CO2per ton of product that is shipped. Alternative shipping fuels add 0.1 – 3.3% to this baseline cost.

LNG is most competitive, adding just c10% to total shipping costs in LNG-fueled ships – possibly much less, or even deflating costs, when oil prices are higher, or LNG prices are lower. But LNG only lowers CO2 emissions by c10%. And even this is debatable, if a gas-fired marine engine suffers from methane slip.

Blue methanol, blue ammonia and renewable diesel are next most economical, but add 0.4 – 0.7% to the final costs of shipped products, while achieving 60-70% reductions in the CO2 intensity of shipping. This equates to a decarbonization cost of $135-260/ton.

Most costly are green methanol, green ammonia, green hydrogen and e-fuels, which add 2.3-3.3% to the final costs of shipped products, while achieving 80-90% reductions in the CO2 intensity of shipping. Thus the decarbonization costs are an eye-watering $700-1,000/ton.

The numbers do vary markedly, however, based on the products being shipped, especially their mass, their costs and the shipping distance, which can all be stress-tested in the data-file.

For bulk products such as sugar, iron ore or grains, shipping using marine diesel can comprise as much as 5-10% of product prices, hence switching to the green fuels above can inflate end product costs by 20-30%.

Conversely, for light but high-value products, such as iPhones, shipping costs are basically irrelevant. You can use any fuel you like, and it will not even sway final product prices by 0.00%. Most other products are in between. Numbers can be stress-tested in the model.

The most economic options to decarbonize shipping are through larger and more efficient ships, using high-quality hydrocarbon fuels and coupling these ships with nature-based CO2 removals. Decarbonization must increasingly prove it can be competitive. We have also looked at carbon capture on ships.

Global energy demand: false ceiling?

Can GDP decouple from energy demand? Wealthier countries’ energy use has historically plateaued after reaching $40k of GDP per capita. Hence could future global energy demand disappoint? This 15-page report argues it is unlikely. Adjust for the energy intensity of manufacturing and imports, and energy use continues rising with incomes.

Japan oil demand: breakdown over time?

Japan's oil demand from 1990 to 2023. Japan's oil demand peaked in 1996 at 5.8Mbpd and has since declined to 3.4Mbpd by 2023.

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 zero sees 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.

Further data is available on the TSE site into Japan’s gas and power demand, energy security, population and GDP, and other commodities supply-demand.

Energy security: right to self-determine?

The average major economy produces 70% of its own energy and imports the other 30%. This 12-page note explores energy self-sufficiency by country. We draw three key conclusions: into US isolationism; Europe’s survival; and the pace of EV adoption, both in China and in LNG-importing nations.

Mainspring Energy: linear generator breakthrough?

Linear generator technology can convert any gaseous fuel into electricity, with c45% electrical efficiency, and >80% efficiency in CHP mode. This data-file reviews Mainspring Energy’s patents. We conclude that the company has locked up the IP for piston-seal assemblies in a linear generator with air bearings, but longevity/maintenance could be a key challenge to explore.

EtaGen was founded in 2010 by three Stanford engineers, and rebranded as Mainspring Energy in 2020. Its headquarters are in Menlo Park, California; and the company has c400 employees, having closed a $290M Series E financing in 2022.

Mainspring is commercializing a linear generator, which is low-cost, reliable, flexible and can use any clean fuel (e.g., natural gas, biogas, hydrogen, ammonia), in sizes from 230kW to multiple-MW, >45% electrical efficiency and >80% total thermal efficiency in CHP mode.

In a linear generator, the compression of fuel and air causes a uniform and flameless combustion reaction to occur, releasing the energy from the fuel, but creating no NOx emissions. The energy from combustion pushes a piston through a cylinder (or in Mainspring’s case, two pistons, through two cylinders). Stator magnets in each piston move past coils in each cylinder, inducing a current. An air spring on the other side of the cylinder is thereby compressed, and re-expands to drive the piston back to its starting point.

Illustration of the working principles of a linear generator.

The main advantages are the simplicity, which could in principle translate into lower capex, compared to the blades and precision-engineered compression and turbine stages within a gas turbine.

Higher efficiency can also be unlocked by harnessing the expansion of combustion gases directly, rather than having to convert it into rotary motion, per the loss attributions for conventional thermal generation. On the other hand, maximum efficiency will always be lower for low-temperature combustion, due to the laws of thermodynamics.

From reviewing Mainspring’s patents, we think there are three main challenges for commercializing linear generators. The main challenge is linked to longevity and maintenance.

Mainspring’s patents focus upon piston-seal assemblies, and seem to have locked up the IP for its linear generator designs. This may also be relevant to other companies aiming to commercialize linear generators, such as Hyliion in the vehicle sector.

Energy intensity of AI: chomping at the bit?

Rising energy demands of AI are now the biggest uncertainty in all of global energy. To understand why, this 17-page note is an overview of AI computing from first principles, across transistors, DRAM, GPUs and deep learning. GPU efficiency will inevitably increase, but compute increases faster. AI most likely uses 300-2,500 TWH in 2030, with a base case of 1,000 TWH.

Industrial cooling: chillers and evaporators?

This data-file captures the costs of industrial cooling, especially liquid cooling using commercial HVAC equipment, across heat-exchangers, cooling tower evaporators and chillers. Our base case is that removing 100MW-th of heat has capex costs of $1,000/ton, equivalent to c$300/kW-th, expending 0.12 kWh-e of electricity per kWh-th, with a total cost of 7 c/ton-hour.

Across the US cooling market, the most common metric for measuring cooling capacity is in ‘tons’. This is shorthand for the coolness provided by 1 US ton of ice melting over the course of a day, equating to 3.52kW-th of heat removal. Providing 3.52 kW-th of cooling for one hour can thus also be called 1 ton-hour.

Cooling can be delivered via three mechanisms: simple heat exchange with ambient air or water (depends on ambient temperatures), evaporating some of the water in an evaporating tower (depends on water availability) and chilling a working fluid using a refrigeration cycle. In practice, all three may be used in combination (as exemplified in the chart below).

Electricity use of a cooling system. About 50% is from pumping, 20% from the fans in cooling towers, and 30% from the chiller system.

Capex costs of industrial cooling depend on the precise combination of equipment that is used, but a good ballpark is $1,000/ton, equivalent to $300/kW-th, based on our models of compressors, heat-exchangers, pumps, fans and blowers, storage tanks, piping, VFDs, switchgear, grid connections, engineering and construction.

Installed cooling cost for data-centers. Installation and EPC make up ~60% of costs and the rest is equipment: pumps, cooling towers, chiller, piping, VFDs, etc.

Removing each kWh-th of heat requires 0.12 kWh-e of electricity, in our base case, but the numbers vary as a function of water evaporation rates and ambient temperatures, running anywhere from 0.03 to 0.5 kWh-e per kWh-th. Cooling in water-scarce and hot climates is c60% more costly than in water-abundant and cool climates.

Base case numbers in our commercial cooling model are primarily geared to data-centers, where 10-20% of total installed costs will be on cooling, in order to keep chips below a thermal limit of 27ºC or cooler. Water intensity of AI computing can thus be estimated in the range of 1,000-3,000 liters per MWH, meaning that each ChatGPT query consumes as much as 10-30ml of water. Or alternatively, PUEs can be increased by c5-10% to avoid any water use in evaporators. Hence the data-file also screens 20 companies, with 65% of the market in data-center cooling.

Market shares of companies providing equipment for data-center cooling versus the percentage of their business dedicated to it.

All of our numbers into the costs of industrial cooling can be stress-tested in the data-file. Backup tabs of the model contain details of companies and our notes from technical papers.

Energy and AI: the power and the glory?  

The power demands of AI will contribute to the largest growth of new generation capacity in history. This 18-page note evaluates the power implications of AI data-centers. Reliability is crucial. Gas demand grows. Annual sales of CCGTs and back-up gensets in the US both rise by 2.5x?

Data-centers: the economics?

Financial model over 25 years for a model data-center.

The capex costs of data-centers are typically $10M/MW, with opex costs dominated by maintenance (c40%), electricity (c15-25%), labor, water, G&A and other. A 30MW data-center must generate $100M of revenues for a 10% IRR, while an AI data-center in 2024 may need to charge $3M/EFLOP of compute.

Data-centers underpin the rise of the internet and the rise of AI, hence this model captures the costs of data-centers, from first principles, across capex, opex, land use and other input variables (see below).

In 2023, the global data-center industry is $250bn, across 500 large facilities, 20,000 total facilities, and around 40 GW of capacity, which likely rises by 2-5x by 2030.

A 30MW mid-scale data-center, costing $10M/MW of capex, must generate $100M pa of revenues, in order to earn a 10% IRR, after deducting electricity costs and maintenance.

The capex breakdown for a typical non-AI data center is built up in the data-file, drawing on cost data for various IT components, cooling, chillers, transformers, switchgear, battery UPS, backup generators, plus broader infrastructure such as generation, transmission and fiber links.

Capex buildup for a data-center. The cost is $10,000/kW, with the largest parts being the servers, cabling and installation. Another roughly $2,000/kW comes from associated infrastructure.

If the data-center is computation heavy, e.g., for AI applications, this might equate to a cost of around $3/EFLOP of compute in 2023. This fits with disclosures from OpenAI, stating that training GPT 4 had a total compute of 60M EFLOPs and a training cost of around $160M.

However, new generations of chips from NVIDIA will increase the proportionate hardware costs and may lower the proportionate energy costs (see ComputePerformance tab). An AI-enabled data-center can easily cost 2x more than a conventional one, surpassing $20M/MW.

Reliability is also crucial to the economics of data-centers: uptime and utilization have a 5x higher impact on overall economics than electricity prices. This makes it less likely that AI data-centers will be demand-flexed to power them using the raw output from renewable electricity sources, such as wind and solar?

The cost of training AI depending on the price of electricity and data-center utilization. The price is more sensitive to utilization than to power prices.

Economic considerations may tip the market towards sourcing the most reliable power possible, especially amidst grid bottlenecks, and it also explains the routine use of backup power generation.

Another major theme is the growing power density per rack, rising from 4-10kW to >100kW, and requiring closed-loop liquid cooling.

Please download the data-file to stress-test the costs of a data-center, performance of an AI data-center, and we will also continue adding to this model over time. Notes from recent technical papers are in the final tab.

Copyright: Thunder Said Energy, 2019-2024.