Our research into the wind industry captures opportunities for wind power in the energy transition. Wind power should have levelized cost between 4-12 c/kWh, embedded CO2 intensity of 0.02 kg/kWh, and fewer bottlenecks than other new energies we have modeled.
Capacity at least quintuples in our roadmap to net zero and can provide over 20% of all global electricity, c10% of all global energy in the 2040s.
Materials matter for scaling up wind, hence we have modeled value chains in glass fiber, carbon fibers, epoxies, permanent magnets, Rare Earths, and novel ideas such as reducing steel intensity in lieu of lower-carbon construction materials.
Power electronics are another facet of research into the wind industry. This helps to improve the power quality of wind, so it can substitute better for conventional energy. We think the industry is evolving to require 0.5 MVARs of reactive power compensation per MW of wind power.
The volatility of wind generation is illustrated in this data-file, by aggregating the data for a large wind project in Australia, every five minutes, across an entire calendar year. Intra-day and inter-day volatility is 30-60% higher than for solar. 2-6 day feasts and famines are hard to backstop with batteries. Solar also cannibalizes wind?
Stockyard Hill is a 149 turbine, 511MW wind farm in New South Wales, Australia, located approximately 150km west of Melbourne and 35km west of Ballarat, Victoria. As a case study for large-scale wind generation, we have evaluated its output, every 5-minutes, over the course of 2023 (105,000 data-points), using data from AEMO.
The project’s average utilization factor across the year was 37%, varying from 0% on the worst day to 98% in the best day, higher than in our usual onshore wind models. Seasonally, the utilization factor was lowest in January, at 27% and highest in June at 56%, although this may simply be due to random weather fluctuations.
Solar versus wind? Over the entire year, wind output at Stockyard Hill was 20% lower than average between 10am and 2pm, i.e., the hours of peak solar generation (chart below). In January/February, wind output was 50% lower during this time-window. This strongly suggests that wind is being curtailed when the grid is already full of solar.
Short-term volatility also appears to be higher for wind than for solar. The 5-minute-by-5-minute volatility of a typical large solar installation is +/- 5%, while for this particular wind installation it is +/- 8%, and as high as +/- 10% in September/October. The volatility across three typical (median) days is shown below.
Daily volatility also appears to be higher for wind than for solar. The daily standard error for a typical large solar installation is +/-50%, while for this particular wind installation, it is +/- 65%. One reason for the higher volatility is that on the best days, wind runs flat out, while on the worst days, wind does not run at all (chart below).
The biggest challenge for integrating wind into power grids, we think, are the long gaps (aka dunkelflaute), seen in this data-file. Generation was nil for five consecutive days in May-2023, 4-days in August-2023 (chart below), 4-days in October, 3-days in March, 3-days in April, 3-days in November, 2-days in July, 2-days in September. 2-6 day feasts and famines are hard to backstop with batteries, which is why we think gas-fired backups entrench. All of the data are in the data-file.
This 14-page report re-visits our wind industry outlook. Our long-term forecasts are reluctantly being revised downwards by 25%, especially for offshore wind, where levelized costs have reinflated by 30% to 13c/kWh. Material costs are widely blamed. But rising rates are the greater evil. Upscaling is also stalling. What options to right this ship?
Global offshore wind capacity stood at 60GW at the end of 2022, rising at 8GW pa in the past half decade, comprising 7% of all global wind capacity, and led by China, the UK and Germany. Our forecasts see 220GW of global offshore wind capacity by 2030 and 850GW by 2050, which in turn requires a 15x expansion of this market.
Installed global offshore wind capacity stood at 60GW at the end of 2022, representing 7% of all global wind capacity installed to-date, based on helpful data from GWEC.
The largest offshore wind capacity bases among different countries are in China (31GW, meaning that 9% of China’s total wind capacity was offshore), the UK (14GW, 50% of total capacity is offshore) and Germany (8GW, 12% of total capacity is offshore).
Global offshore wind capacity forecasts by 2030 that have crossed our desk range from 200-400GW, and our own estimate is towards the lower end, at 220GW, which would be 13% of all global wind capacity.
Global offshore wind capacity forecasts by 2050 that have crossed our desk range from 600-2,000GW, and our own estimate is also towards the lower end, at 850GW, which would be 20% of all global wind capacity.
Please download the data-file to explore offshore wind capacity by country, and how the market might evolve. Our numbers are top-down estimates, approximations that seem reasonable to us, based on the size of the grid in each region, the share that can realistically come from wind, and the percent of wind that can realistically come from offshore, linking between various TSE energy market models.
Share of Total Installed Wind Capacity that is Offshore by Country by Year with Forecasts to 2050
Mathematically, if total installed wind capacity rises by 5x by 2050, and offshore’s share of that capacity rises 3x (from 7% to 20%) then offshore capacity is rising by about 15x, which is one of the larger ramp-ups required in our roadmap to net zero.
Offshore wind installations averaged 8GW per year in 2018-22, and must rise by 5x to 40GW per year in the 2040s (chart below).
Offshore Wind Installations by Country by Year in GW from 2010 to 2050
Offshore wind capex requirements? At an average cost of $4,000-5,000/kW, this would require about $160-200 bn per year of capex, for comparison with other market sizes. For further details on our capex build-up, please see our offshore wind cost model.
Offshore wind generation in TWH? At an average capacity factor of 40% on installed capacity, the numbers in this data-file would translate into about 3,000 TWH of useful offshore wind energy, or 3% of the global total useful energy in our global energy demand models.
This model estimates the levelized cost of offshore wind at 13c/kWh, to generate a 7% IRR off of capex costs of $4,000/kW and a utilization factor of 40-45%. Each $400/kW on capex adds 1c/kWh and each 1% on WACC adds 1.3 c/kWh to offshore wind levelized costs.
The levelized costs of offshore wind are built up in this economic model, and a flat price of 13c/kWh is needed, over a 25-30 year project life, in order to generate a 7% IRR.
Offshore wind capex will tend to average $4,000 per kW according to our build-up
Operating costs of wind turbines are aggregated in our data-file here. Decline curves of wind turbines are aggregated in our data-file here. Our estimate of grid connection charges is based on tracking past projects and the need for synthetic inertia and reactive power compensation. Decision-makers are welcome to reflect or ignore these costs, but we recommend understanding them and reflecting them.
Levelized costs of offshore wind are most dependent upon WACCs, as effectively all of the costs are capex costs, incurred up front, unlike other power generation sources. A rule of thumb is that each 1% variation in WACC impacts levelized cost by 1.3 c/kWh and each $400/kW on the capex impacts levelized cost by 1c/kWh. Although for more detailed numbers, these two variables are interdependent (sensitivity chart below).
Sensitivity of offshore wind costs to capex costs and WACCs
What is the levelized cost of offshore wind? Our 13c/kWh estimate is a flat estimate over 25-30 years. I.e., it means that the weighted average sales price of electricity must run at 13c/kWh in order to generate a 7% IRR. We would note that some other commentators have published levelized costs using quite different financial assumptions…
Some other commentators have WACCs in the range of 3-5% (the Fed Funds Rate is above 5% in 2023). Others publish numbers on a ‘real’ basis, which means that the price in Year 1 escalates over time, e.g., with inflation. So for example, what is quoted as an 8c/kWh ‘real levelized cost’ really means 10c/kWh over the life of the project and 14c/kWh by the end of the project (chart below). To be clear, our numbers are nominal numbers, and do not juice the IRR by assuming an annual price escalation.
Levelized costs can be defined in nominal terms or in real terms
A detailed case study is also presented in the data-file, covering the 816MW Empire Wind project, off New York, constructing c80 x c10 MW wind turbines, each as tall as the Chrysler building. Base case IRRs would be in single digits. The model explores how hypothetical initiatives might uplift the returns, including power marketing, continued cost-deflation, leverage, carbon prices and feed-in tariffs (chart below).
Empire Wind Uplifts 5 percent IRR to 10 percent IRR via incentives leverage and tariffs
Please download the data-file to stress test the economics of offshore wind projects, and see how levelized costs vary with capex, opex, utilization rates, taxes and other costs. For comparison, our economic model for onshore wind projects is linked here.
Wind turbine generators can use doubly fed induction generators (DFIGs) or permanent magnet synchronous generators (PMSGs) based around Rare Earth metals. This data-file captures the trends in DFIGs vs PMSGs over time by tabulating 40 examples, as turbines have grown larger, and different wind turbine manufacturers have adopted different strategies.
How do wind turbines generate electricity? Both doubly fed induction generators (DFIGs) and permanent magnet synchronous generators (PMSGs) retain a balanced market share in modern wind turbines, converting rotational energy into electrical energy.
This data-file tabulates data into forty recent wind turbine designs, sampling across all of the leading wind turbine manufacturers. We have tabulated the model, manufacturer, country, capacity (MW), year introduced (YYYY), rpm, gear system, typical voltages (kV, where available), generator system and other power electronic details.
Capacity trends. The maximum power capacity for DFIG turbines has been hovering around the 6-8MW mark for the past decade, while the trend towards larger 10-15MW turbines, especially offshore, is almost always associated with PMSG turbines.
Why do wind turbines use Rare Earth magnets? The short answer is that Rare Earth magnets have greater magnetic field strengths (flux densities), which opens up direct drive generator configurations with much lower gearing, and without requiring their own input power supply like the electromagnets in DFIGs do.
Advantages of PMSGs are cited by different operators, including higher >96% efficiency, fault ride-through, lower maintenance, more compact nacelles, easier installation.
Elimination of gears is an advantage for PMSGs as it can help to avoid maintenance issues, which typically costs $40/kW of capacity and 1-2c/kWh on levelized cost. For geared turbines, the gearbox is by far the largest source of maintenance issues (data here).
About two-thirds of the PMSGs in our data-file use direct drive to impart rotation into their power-dense magnets (no gears). Almost all of the IGBT turbines use gears, to step up the rotational speed by around 100:1, on average. Some PMSGs retain gears in order to lower the amount of Rare Earth materials/magnets required by up 90% (e.g. Vestas).
More compact turbines are associated with PMSGs, and this is an advantage, as it lowers the costs associated with wind turbine installation and offers faster commissioning.
Fault ride-through is one of the most commonly cited issues for using Rare Earth PMSGs rather than DFIG turbines. A DFIG requires a power connection to magnetize the electromagnets in its rotor. If the power input drops, then the power output also drops.
Chinese manufacturers have been gravitating towards PMSGs especially since 2016. For example when Goldwind switched from DFIGs to PMSGs, the company noted it was able to eliminate 13 gears and “hundreds of parts”.
Vestas and GE have been relatively vocal about benefits of permanent magnets. GE back to 1998 (!). Vestas is now using Rare Earth elements in all new grid turbine models (per the Vestas website here).
Conversely, two other European manufacturers stand out as they have been more reluctant to use permanent magnets, and more focused on DFIG designs, especially in smaller turbines and onshore turbines.
Our forecasts for Rare Earth magnet use in future wind turbine’s bill of materials, both in mass terms (kg/kW) and in cost terms ($/kW) is modelled here.
Underlying data behind these observations is set out in the data-file of wind turbine generators. If you want to understand how magnets in wind turbines work, we recommend our overview of magnets. All of our broader wind research is here.
This database tabulates the typical fuel consumption of offshore vessels, in bpd and MWH/day. We think a typical offshore construction vessel will consume 300bpd, a typical rig consumes 200bpd, supply vessels consume 150bpd, cable-lay vessels consume 150bpd, dredging vessels consume 100bpd and medium-sized support vessels consume 50bpd. Examples are given in each category, with typical variations in the range of +/- 50%.
This data-file tabulates the typical fuel consumption for different types of offshore vesesel, across all of our research into the offshore and shipping industries.
Offshore construction vessels are especially used in the offshore wind industry, where installation costs for a large-scale wind project will average aroud $1,000/kW spread across 60-100 vessels during peak activity. The largest are offshore construction vessels which will tend to consume around 300bpd of fuel. This is also factored in our EROEI calculations for a wind turbine.
Cable lay vessels are also used in offshore wind and more broadly amidst the expansion of power grids and HVDC interconnectors. We think a typical cable lay vesel will consume 150bpd of fuel.
Offshore rigs also see a continued role in our energy balances, in order to provide 85Mbpd of long-term oil demand and 800 bcfd of long-term gas demand in our roadmap to net zero. A typical offshore oil rig consumes 200bpd of fuel. The numbers are lower for jack-ups and ultra-efficient drillships, but can be higher for larger and older semi-subs.
Elsewhere in our shipping research, we see the typical fuel consumption of a large container ship at 1400bpd, a bulk tanker at 420bpd and a LNG carrier at 270bpd.
The fuel consumption of dredging vessels and the fuel consumption of platform supply vessels (PSVs) are also covered in the data-file of offshore vessels’ fuel consumption.
Please download the data-file for additional datapoints into the fuel consumption of different ships, and individual data-points that led us to these numbers.
Wind power energy paybacks? This data-file estimates 3MWH of energy is consumed in manufacturing and installing 1kW of offshore wind turbines, the energy payback time is usually around 1-year, and total energy return on energy invested (EROEI) will be above 20x. These estimates are based on bottom-up modelling and top-down technical papers.
The average wind energy project has an energy intensity of 3MWH/kW, which is repaid after c1-year, for a total energy return on energy investment above 20x, over a 20-25 year operating life.
One observation from reviewing technical papers is that many have rough methodologies. Some are still basing numbers upon small, <1MW turbines, which are no longer representative. Conversely, others are incomplete, and have not fully captured materials costs.
Hence we have built up our own bottom-up estimates for the energy intensity of wind power, and the EROEI of wind turbines.
The largest individual contributors to the up-front energy costs of wind turbines are transporting materials to the site (0.75MWH/kW), steel (0.6MWH/kW), other materials (0.3MWH/kW), large offshore vessels that install foundations and turbines (0.3 MWH/kW) and the tail of 20-40 smaller vessels that support offshore operations (data here).
The average CO2 intensity of wind turbines is suggested at 10-20g/kWh (0.01-0.02kg/kWh). This coheres with the technical papers that we reviewed, and our own bottom-up estimates.
Wind power energy paybacks will vary with individual project parameters, and we think that a realistic range for offshore wind projects is 15-30x EROEI.
The most important parameteris the location of the project, which will determine energy generated per year, but also transportation distances and steel requirements.
Is the power grid becoming a bottleneck for the continued acceleration of renewables? The median approval time to connect to the grid for a new US power project has climbed by 30-days/year since 2001; and has doubled since 2015, to over 1,000 days (almost 3-years) in 2021. Wind and solar projects are now taking longest to inter-connect, due to their prevalence, lower power quality and remoteness. This data-file evaluates the data, looks for de-bottlenecking opportunities, and wonders about changing terms of trade in power markets.
Accelerating wind and solar are a crucial part of our roadmap to net zero. But we have also been worrying about bottlenecks, especially in power grids. Project developers are increasingly required to fund new power transmission infrastructure, before they are allowed to interconnect, usually costing $100-300/kW, but sometimes costing as much as the renewables projects themselves (data here). If there is one research note that spells out the upside we see in power grids and electrification, then it is this one. We also see upside in long-distance transmission, HVDCs, STATCOMs, transformers, various batteries.
Other technical papers have also raised the issue of rising interconnection times and power grid bottlenecks for wind and solar. And the US’s Lawrence Berkeley National Laboratory has also started tracking the ‘queue’ of power projects waiting to inter-connect. We have downloaded their database, spent about a day cleaning the data (especially the dates), and aimed to derive some conclusions below.
Methodological notes. The raw LBL database contains a read-out of over 24,000 US power projects that sought to inter-connect to a regional power grid, going back to 1995. However, 13,000 of these applications were withdrawn, 8,000 are still active/pending and 3,500 are classed as operational. 2,500 of the projects have complete data on (a) when they applied for permission to inter-connect to the grid and (b) when they were ultimately granted that permission, allowing us to calculate (c) the approval time (by subtracting (b) from (a)). But be warned, this is not a fully complete data-set. And some States, which have clearly constructed large numbers of utility-scale power projects, seemed not to report any data at all. Nevertheless, we think there are some interesting conclusions.
The median time to receive approval to inter-connect a new US power project to the grid has risen at an average rate of 30-days per year over the past two decades and took over 1,000 days in 2021, which is 2.8 years. This has doubled from a recent trough level of 500 days in 2015 (chart below) and a relatively flat level of 400-days in the mid-2000s.
Renewables projects now take longer to receive approval to connect to the power grid. Wind projects have always taken longer to receive approvals. And recent wind projects continued taking 30% longer than the total sample of approved projects in 2019-21. More interestingly, however, solar projects have gone from taking 50% less time to receive grid connection approvals in the mid-2000s to taking 10% longer than average, especially in 2020 and 2021. Why might this be? We consider five factors…
#1. Project quantity is probably the largest bottleneck. The numbers of different projects receiving permission to connect to the grid are tabulated below. A surge in wind projects in 2005-2012 correlates with the first peak in inter-connection approval times on the chart above. And a more recent peak in utility-scale solar, battery and wind projects correlates with the recent peak of approval times in 2020-21. This suggests a key reason it is taking more time to approve new inter-connections is that grid operators are backlogged. It would be helpful to resolve the backlog. And we wonder if the result might be a change in the terms of trade: favoring grid operators more, favoring capital goods companies more, and requiring project developers to be more accommodating?
#2.Project sizing does not directly explain inter-connection approval times. The average utility-scale solar project has become larger over time (now surpassing 150MW). But wind projects have always been larger than the average power project seeking approval to connect to the grid. And there are many small gas, coal and nuclear projects that take longer to receive connection approval than large ones. So we do not think there is a direct link between power project size and the time needed to approve an inter-connection. However, there may be an indirect link. It is clearly going to take longer to study the impacts of connecting 10 x 100 MW solar projects (in 10x separate locations), than 1 x 1,000 MW nuclear plant, even though both have the same nameplate capacity.
#3. Connection voltage does not explain inter-connection approval times. The median project in the database is connecting into the grid at 130kV. The median wind project is at 145kV. The median gas project is at 135kV. The median solar project is at 110kV. The median battery project is connecting at 140kV. Although we do think that moving power over longer distances is increasingly going to favor higher voltage transmission and also pull on the transformer market.
#4. Power quality seems to explain relative approval times and increasingly so. Another interesting trend is the difference in interconnection approval times between different types of power projects. Wind and solar projects now take 30% and 10% longer than average to receive approvals. Whereas gas, batteries and hydro now take 15%, 50% and 90% less time than average to receive approvals. We think this is linked to power quality. On a standalone basis, wind and solar may tend to reduce the inertia, frequency regulation, reactive power compensation and balancing of power grids. Whereas gas power plants, batteries and hydro typically help with these metrics (each in their own way). We think this adds evidence in support of our power grids thesis.
#5. Remote projects take longer to approve, as they will likely require more incremental transmission lines. The shortest interconnection times across all power projects were in Texas, which already has a very large power grid, arguably the best energy endowment and infrastructure in the world. But other more densely populated states (Michigan, Illinois) tended to have 50% lower times to approve inter-connections than some of the least densely populated states (the Dakotas, Iowa, Montana), where we think new power generation likely needs to be moved further to reach demand centers. Location matters for levelized cost of electricity. Again, we think this evidence also supports our power transmission thesis.
Overall the data suggest that there are growing bottlenecks to inter-connect renewables to power grids; especially in areas with a surge of activity, where power quality is increasingly important, and in more remote areas that require new transmission infrastructure. We think this trend will continue. It would be helpful to debottleneck the bottlenecks, to sustain the upwards trajectory of wind and solar. But we do think the terms of trade are shifting in favor of grid operators, power electronics, transmission infrastructure, developers that can use their own power and consumers that can demand shift.
An offshore wind project is likely to cost $2,500/kW, of which c$1,500/kW is turbines and $1,000/kW is offshore wind installation costs. This data-file aims to estimate the breakdown by vessel type, day-rates and costs per turbine.
Estimates range from 25 to 100 vessels being deployed in the installation of a typical offshore wind project, across different studies and technical papers.
Our base case estimate is that each turbine will require 10 days to install, consuming 100 “vessel days”, of which c10% are highly specialized vessels (data here).
The highest day-rates will be commanded by the most specialized vessels, and we think this includes wind turbine installation vessels, cable lay vessels (both intra-wind farm and for the export cable to shore), and foundation vessels that drive monopiles or jackets/piles into the seabed.
Offshore installation costs are sensitive and can realistically range from $800-2,500/kW, depending on project parameters.
These installation costs come on top of the underlying turbine costs, which we think are usually going to be around $1,500/kW per our cost breakdown of a wind turbine.
Economies of scale are achieved by using larger turbines in larger projects. Conversely, costs re-inflate when moving further from shore and into deeper water.
Goldwind is one of the largest wind turbine manufacturers in the world, having delivered over 50,000 turbines by early-2023. The company was founded in 1998, headquartered in Beijing, it has 11,000 employees, and shares are publicly listed.
The wind industry is increasingly aiming to mimic the inertia and frequency response of synchronous power generators. Goldwind has published some interesting case studies, boosting power by 6-10%, within 1-2 seconds, for 5-6 seconds. Hence this data-file reviews Goldwind frequency response patents to look for an edge?
Synchronous power generators have ‘inertia’. If the grid suddenly becomes short of power, due to a large new load switching on, or a large power source disconnecting, then all of these synchronized machines will slow down very slightly, and in unison. Harvesting their rotational energy provides more power to the grid. But it also lowers the ‘frequency’ of the grid. At least for a few seconds, until firing rates can be ramped up. Generally, the larger the synchronous power generators, the higher their ‘inertia’, the more power that can be drawn out as they slow down, and the less grid frequency will drop per unit of incremental power. This is all crucial to the functioning of a modern power grid.
Wind turbines do not inherently provide any inertia or frequency response. Interestingly, they have about the same amount of angular momentum as conventional power generators, as they spin. But it is not synchronized with the grid. A wind turbine spins at 15-20 revolutions per minute, whereas a typical grid at 50 Hz (or 60 Hz in the US) is completing 50 full AC cycles per second.
An increasing goal for the wind industry is to ‘mimic’ some of the inertia and frequency responses of synchronous generators. Recently, Goldwind has published interesting case studies at wind farms, e.g., in Australia, boosting power by 6-10%, within 1-2 seconds, for 5-6 seconds (chart below).
The goal of this patent screen is to evaluate whether Goldwind’s frequency response algorithms give it an edge over other wind turbine manufacturers. We were unable to de-risk this idea from reviewing the patents, for the reasons explained in the data-file.
There are three key challenges for implementing frequency responses, as a form of ‘synthetic inertia’ and wind power plants, which are also borne out by the patent analysis. It takes time to implement the response (certainly longer than a battery, capacitor bank or conventional generator). Second, it is challenging to coordinate the responses of dozens of turbines, each with a different operating state. Third, there is always a danger that the wind drops at the precise moment you want to implement your frequency response, due to natural wind volatility.
Goldwind frequency response? Goldwind discusses possible solutions to each of these challenges in its patents. The bright spot is that we think many of the solutions are software-side, which will lower their implementation cost, and not pull too hard on already-bottlenecked power electronics. However, as usual, we find it harder to de-risk algorithm-heavy patents.
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