the research consultancy for energy technologies
Our renewables research and data-files capture broader opportunities for renewables in the energy transition. This might include opportunities to integrate or debottleneck renewables. Or, going beyond wind and solar, which are categorized separately, it might involve additional renewables categories, such as hydro power, next-generation geothermal, or some forms of biomass energy.
Other renewables research covers growth trajectories, capex levels, physics of how renewables work, power volatility, inter-correlations of different renewables technologies, maintenance costs and decline rates.
Looking across all of our work in the energy transition, our roadmap to net zero sees renewables providing over 25% of all global energy by 2050.
The grid-forming inverter market may soon inflect from $1bn to $15-20bn pa, to underpin most grid-scale batteries, and 20-40% of incremental solar and wind. This 11-page report finds that grid-forming inverters cost c$100/kW more than grid-following inverters, which is inflationary, but integrate more renewables, raise resiliency and efficiency?
Global electricity supply-demand is disaggregated in this data-file, by source, by use, by region, from 1990 to 2050, triangulating across all of our other models in the energy transition, and culminating in over 50 fascinating charts, which can be viewed in this data-file. Global electricity demand rises 2.5x by 2050 in our outlook.
Global electricity demand stood at 30,000 TWH pa in 2023, equivalent to 37.5% of global useful energy consumption. The breakdown is 40% industrial, 25% residential, 17.5% commercial, 6% agriculture, as disaggregated by region in the data-file.
Global electricity demand surpasses 70,000 TWH by 2050, in our outlook, which effectively means that 100% of all net growth in global useful energy consumption through 2050 is electricity demand growth.
Total electricity demand is seen growing at a 3.5% CAGR through 2050, of which the largest contributors are in new energies areas such as electric vehicles, CCS and within batteries. But the largest growth in absolute terms, at +10,000 TWH pa, is in producing metals and materials for the energy transition.
Rising living standards are the biggest driver of the growth. For example, residential electricity consumption is currently 2.4 MWH pp pa in the developed world, and just 0.7 MWH pp pa in the emerging world, which still only rises to 1.4 MWH pp pa by 2050 on our numbers, or around half the level in the developed world today.
Global electricity generation grows by 2.5x by 2050, including a 15x ramp for solar, 5x for wind, 3x for gas, 2.5x for nuclear and 1.7x for hydro, while coal-fired power falls by 60%. This outlook sees wind and solar ramping to 50% of all global electricity by 2050, which is near the economic limit. These data are all broken out by region on the Generation tab.
Electricity generation from gas does need to rise, even with this large renewables build-out, in order to displace coal. Our numbers have gas consumption for power generation rising from 150bcfd in 2023 to 325bcfd by 2050, which also boosts demand for gas turbines. Fuel use for coal, gas and oil, by region, and over time, are broken out on the ShareOfCommodities Tab.
The CO2 intensity of global electricity generation falls from 0.54 kg/kWh today to 0.15 kg/kWh by 2050 on these numbers, ranging from <0.1 kg/kWh in the developed world to 0.2 kg/kWh in the emerging world, and 0.3 kg/kWh in India and Africa. These numbers are on a gross basis. Capturing and offsetting the CO2 would be necessary to reach Net Zero. These calculations are shown by region and by contributor on the CO2 tab.
Global electricity-supply-demand is broken down by source, by use, by region and over time, across the entire data-file, which draws from all of our other energy transition research and models.
Power generation asset lives average c70-years for large hydro, 55-years for new nuclear, 45-years for coal, 33-years for gas, 20-25 years for wind/solar and 15-years for batteries. This flows through to LCOE models. However, each asset type follows a distribution of possible asset lives, as tabulated and contrasted in this data-file.
Asset lives of power generation infrastructure are tabulated in this data-file, covering both the design life and age at retirement, for coal, gas, wind, solar, batteries, nuclear and hydro.
Average lives are c70-years for large hydro, 55-years for nuclear, 45-years for coal, 33-years for gas, 20-25 years for wind/solar, 15-years for batteries. However, the numbers follow a distribution, as can be quantified based on data in the data-file.
Thus, the capex of c$1,000/kW for wind, solar and batteries is not necessarily cheaper (per year) than $1,000-1,500/kW for gas or $3,000/kW for hydro. These very long-run costs/benefits are not well captured in LCOE models.
Our personal perspective is that long-term infrastructure has huge hidden value within stable, developed world countries. Their public benefits continue long after their capex costs have been forgotten. Our favorite example is the Brooklyn Bridge, completed for $15M in 1883, yet still standing today.
Some power plants can also be replaced and re-fitted, piece by piece, like Theseus’s Ship. It might cost $650/kW to extend a nuclear plant’s life by a further 20-years (attractive for data-centers, and stoking the order books of nuclear contractors, such as Westinghouse, now owned by Cameco).
Likewise for new energies, there may be upside in the 2030s for module-makers, turbine-makers and battery materials and manufacturers, as existing assets need to replace failing components.
There has never been more controversy over the fair values of power generation assets, which hinge on their remaining life, utilization, flexibility, power prices, rising grid volatility and CO2 credentials. This 16-page guide covers the fair values of generation assets, hidden opportunities and potential pitfalls.
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.
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.
This 12-page note studies the output from 10 of the largest gas power plants in Australia, at 5-minute intervals, comparing 2024 versus 2014. Ramping renewables to c30% of Australia’s electricity mix has not only entrenched gas-fired back-up generation, but actually increased the need for peakers?
This data-file summarizes the levelized cost of electricity (LCOE), across 35 different generation sources, covering 20 different data-fields for each source. Costs of generating electricity can vary from 2-200 c/kWh. There is more variability within categories than between them. All the numbers can readily be stress-tested in the data-file.
Levelized cost of electricity (LCOE) breaks down the costs of adding new electricity generation, across capex, capital, tax, fuel, O&M, CO2 and T&D, distilled down in c/kWh terms, or $/MWH terms.
We have constructed over 200 economic models calculating the specific levelized costs of onshore wind, offshore wind, solar, hydro, nuclear, gas power, coal power, biomass, RNG, diesel gensets, geothermal, hydrogen, fuel cells, power transmission, batteries, thermal storage, redox flow, pumped hydro, compressed air, flywheels, CCS and nature-based CO2 removals.
The goal in this data-file is to allow for easy comparisons between different power generation options, across 20 different dimensions. We have written that we hate levelized cost, because it is often portrayed as though one energy source will emerge “to rule them all”, whereas there is more variability within each category than between them (see below).
The simple chart below shows how our levelized cost estimates change if we make simple changes in this comparison file: flexing risk-free rates between 1-5%, flexing fuel costs +/- 50%, flexing capex costs by +/- 50%, or changing the distances needed for AC power transmission, CCS pipelines, or other variables.
This kind of stress-testing is really the main point of the data-file, asking questions like: how do levelized costs change with WACCs? Or how do levelized costs change with higher gas prices? How do levelized costs change with capex deflation? What are the best options for lowering CO2 intensities of grids (chart below) without inflating total costs? The data-file answers these questions across several dimensions…
Capex costs are broken down for each category and are defined as the total installed capex, in $/kWe, which can then be divided by the total number of lifetime operating hours, yielding a number in c/kWh. Usually, the capex estimates in our underlying models draw from both top-down surveys of past projects and bottom-up build-ups.
Capital costs can be described as the after-tax income that needs to be earned on top of recovering the capex to derive a passable IRR (usually 7-12%), after whatever build-time is incurred prior to start-up (usually 2-6 years). We have taken this requisite after-tax income level from our individual underlying models, where it captures nuances such as time value of money, decline curves, and volatility.
Tax costs come on top of after-tax income. For simplicity, our models assume a 25% corporate tax rate across the board, but not tax breaks or changeable policies. Thus, we can think about the numbers in our data-file as being true economic costs.
Fuel costs cover the costs of buying gas for a gas plant, coal for a coal plant, hydrogen for a hydrogen plant, etc. By contrast, fuel costs are often zero for renewables. Again, these can readily be flexed in the model, which is especially important for gas value chains, amidst high dispersion in global gas prices.
O&M costs cover operations and maintenance; and are generally going to be lowest for large and simple systems.
T&D costs cover transmission and distribution, to move power to the load center. An advantage for on-site generation is that power can be used directly, whereas the average offshore wind farm in the North Sea needs to be transmitted 20km back to shore, then onwards. AC transmission costs 1.5c/kWh/100km at large scale. For CCS value chains, we also include $3/ton/100km for CO2 transport in the T&D line.
CO2 costs cover the cost for offsetting or disposing of gross CO2 emissions: either via nature-based CO2 removals, using high-quality reforestation at $50/ton; or for CO2 geological disposal in subsurface reservoirs with a base case cost of $15/ton. Otherwise, for CCS value chains, additional costs are reflected in higher up-front capex, higher fueling costs due to energy penalties, and higher maintenance costs.
Other dimensions are also compared for all of the generation sources in our database: TRLs, logistical risks, development times, efficiency factors, CO2 intensity (kg/kWh), typical load factors (%) and land intensity (acres per MW) (see below).
Momentum behind enhanced geothermal has accelerated 3x in the past half-decade, especially in energy-short Europe, and as pilot projects have de-risked novel well designs. This 18-page report re-evaluates the energy economics of geothermal from first principles. Is there a path to cost-competitive, zero-carbon baseload heat?
This 14-page note predicts a staggering increase in global energy market volatility, which doubles by 2050, while extreme events that sway energy balances by +/- 2% will become 250x more frequent. A key reason is that the annual output from wind, solar and hydro all vary by +/- 3-5% each year, while wind and solar will ramp from 5.5% to 30% of all global energy. Rising volatility can be a kingmaker for midstream companies? What other implications?
The $1bn pa harmonic filter market likely expands by 10x in the energy transition, as almost all new energies and digital technologies inject harmonic distortion to the grid. This 17-page note argues for premiumization in power electronics, including around solar, and screens for who benefits?