Download Category: Economic Model

What are the costs of different technologies in the energy transition? We construct economic models in order to capture costs, compare costs, and stress-test sensitivities.

Our modelling framework always focuses on marginal cost. This is the price that would need to be charged (in $/ton, c/kWh, etc), for a technology to earn a 10% full-cycle return, on its up-front capex costs (in $/Tpa, $/kW, etc), net of operating expenses, maintenance and taxes.

In each of our models, we have aimed to aggregate capex costs and opex costs, based on company data and technical papers. This is just useful data to tabulate, in order to appraise what constitutes a “good” or a “challenging” project within individual industries.

Energy costs are also tabulated in most models, across electricity, heat and materials. This allows us to calculate the CO2 intensity of projects.

A general observation in our economic models is that they can be useful building blocks. For example, if you want to model the costs of making wind turbine blades, you need to know the cost of carbon fiber, which in turn hinges on the costs of polyacrylonitrile, which in turn depends on the price of ammonia, which in turn depends on the price of natural gas. These inter-connections are fascinating and suggest where there may be future bottlenecks and cost re-inflation in the energy transition.

Energy economics: an overview?

This data-file provides an overview of energy economics, across 175 different economic models constructed by Thunder Said Energy, in order to put numbers in context. This helps to compare marginal costs, capex costs, energy intensity, interest rate sensitivity, and other key parameters that matter in the energy transition. Our top five facts follow below.

This data-file model provides summary economic ratios from our different economic models across conventional fuels, conventional power, renewables, lower-carbon fuels, manufacturing processes, infrastructure, transportation and nature-based solutions.

For example, EBIT margins range from 3-70%, cash margins range from 4-80% and net margins range from 2-50%, hence you can use the data-file to ballpark what constitutes a “good” margin, sub-sector by sub-sector; and to screen different industries, according to the capital intensity, opex costs and resultant profitability (chart below).

Capital intensity ranges from $300-9,000kWe, $5-7,500/Tpa and $4-125M/kboed. So if you are trying to ballpark a cost estimate you can compare it with the estimated costs of other processes. The median average industry has a capex cost of $750/Tpa (chart below).

Capital intensity of different energy sources also varies by an order of magnitude (chart below). Each $1 dollar that is disinvested from new hydrocarbon capex ideally needs to be replaced by $25 invested in wind and solar, in order to add the same amount of primary energy to the global energy system (chart below, note here).

Economies of scale are visible in the data-file, across our models of Air Separation, Cables, Comminution, Compressors, Electric Motors, Electrowinning, Fans, Flotation, Gas Dehydration, Harmonic Filters, Heat Exchangers, Inverters, Motor Drivers, Pumps, Rankine Engines, Tanks and Turbines. Generally, making these units 10x larger reduces their unit costs by around 45%.

Cost reduction from scale for different energy technologies.

Interest rate sensitivity is visible in our overview of energy economics. Each 1% increase in capital costs re-inflates new energies 10-20%, infrastructure 2-20%, materials 2-6%, and conventional energy 2-5% (chart below, note here).

Marginal cost inflation per 1% WACC increase for different energy technologies, materials, and infrastructure projects.

The energy intensity of materials is visible across our models of Acetylene, Aluminium, Ammonia, Carbon Fiber, Cement, Copper, Cyanides, Desalination, Glass, H2O2, Hydrogen, Industrial Gases, Lithium Batteries, Methanol, NaOH/Cl2, Nitric Acid, Paper, Plastics, Silicon, Silver, Steel, Wood Products. As a rule of thumb, energy is 50% of the cash cost of typical materials.

Renewables stand out. Despite high capital intensity (35% of revenues, 2x the average), once constructed, they also have the highest cash margins (75%, also 2x the average). The rise of wind, solar and electrification make capex costs and capital costs increasingly important.

The full data are available in the data-file below. However, please be aware that this is simply a compilation of headline figures across our library of 175 economic models. Access to all of the underlying models is covered by a Thunder Said Energy subscription.

Phosphoric acid production costs?

Economic model for a phosphoric acid plant.

Phosphoric acid production costs are $500-900/ton, for a 10% IRR on a new facility, with $1,000-2,000/Tpa of capex. This is using the ‘wet process’, where phosphate ores are reacted with sulfuric acid. CO2 intensity is 0.6 tons/ton. However, the numbers depend on product purity. There is also a 10x higher-carbon, yet potentially lower-cost process, using coke in China. These variations are captured in our model.

Global production of phosphoric acid is around 80-90MTpa on a diluted basis, and 40-50MTpa on a pure H3PO4 basis. 80-90% is used in the fertilizer industry, often in the form of ammonium phosphate, for which 40-50% concentrations are sufficient. High-grade phosphoric acid is also used in the food and electronics industry, as a milder acid than sulfuric or hydrochloric acids. While a new use is in synthesizing the cathodes for LFP batteries. c90% of global phosphoric acid is made via the wet process.

In the wet process of phosphoric acid production, phosphate-containing rocks are crushed and reacted with sulfuric acid, then extensively filtered in hydrocyclones, and further treated to purify H3PO4. Most plants generate sulfuric acid on-site from elemental sulfur, as this allows the heat of H2SO4 formation to be used within the process, lowering energy costs. This is modeled in the Model_S tab, although there is also a variant where sulfuric acid is imported, on the Model_H2SO4 tab.

At the trailing 10-year average price of $900/ton, we can derive a 10% IRR on a facility producing high-grade H3PO4, using the wet process for phosphoric acid production, with a total CO2 intensity of 0.6 kg/kg. The largest opex lines are phosphate ores (c30%), O&M (15%), elemental sulfur (7%), labor (6%), heat (5%) et al.

The costs of phosphoric acid production depend largely on the product concentration. Greater purification requires higher capex costs (chart below).

Capex costs of a phosphoric acid plant depending on the purity of the final product.

The key limitation of the wet process for phosphoric acid production is the need for high-grade ores, often found in Morocco, Jordan and Russia, which should contain high quantities of phosphates, with limited carbonates, MgO, Fe2O3, Al2O3.

There is also a dry process, which can run lower-grade ores, by effectively smelting them in an electric arc furnace with metallurgical coal or coke. This process is said to take place in Kazakhstan and China. Together with lower ore costs and cheap coal, the pyrogenic process for phosphoric acid production can have 25-50% lower costs than the wet process, but almost 10x higher CO2 intensity at 5 tons/ton of H3PO4. This is modeled in the ‘Dry_Process’ tab.

Further notes on the supply chain are contained in the final tab of the model, including conversions from P2O5 to H3PO4 and more details on the energy usage. Please download the model to stress-test the costs.

Costs of biogas upgrading to biomethane?

Financial model for a biogas upgrading facility.

Costs of biogas upgrading into biomethane are estimated at $7/mcf off of capex cost of $400/ton, in this data-file. The largest contributor to total costs is carbon filtering, to remove siloxanes, VOCs and H2S, which we have modelled from first principles, at $2/mcfe. Underlying data into biogas compositions and impurities are also tabulated for reference.

Biogas is a mixture of 40-75% methane, 20-50% CO2, H2O, nitrogen, oxygen, H2S, ammonia, and other impurities such as siloxanes, mercaptans and other volatile organic compounds. Different assets of biogas compositions from technical papers are aggregated in the data-file, showing the variability by feedstock.

Composition of biogas different from different sources and their energy densities.

Indeed it is interesting to note that the biogas compositions are not fixed, but vary over time, as the anaerobic digestion progresses in batch reactors, or as different feedstocks are added into continuous digesters (examples charted below). This requires flexibility and advanced planning for biogas upgrading facilities.

Yields of different gases over days in a batch reactor.

It is important to remove the most harmful impurities before combusting biogas, while to make biogas fully fungible with natural gas, and blendable into pipelines, it should be upgraded into biomethane. The Impurities tab of the data-file summarizes each impurity, what it is, why it matters, and how it is removed.

Which impurities are most important to remove from biogas.

This data-file estimates the costs of upgrading biogas to biomethane. We have a dedicated model for filtering biogas using activated carbon (title charts above), which can remove siloxanes, volatile organics and some H2S.

We have also aggregated the results from stress-testing our other models, including for pressure swing adsorption, amine plants, membrane separations, cryogenic gas separations, gas dehydration, compressors and small-scale pipelines.

Total costs of upgrading biogas into biomethane (and then compressing it to pipeline grade, and building a short pipeline connection) are estimated at $7/mcf, of which $3/mcf is biogas treatment, $3/mcf is further upgrading and $1/mcf is midstream-related.

Cost buildup of upgrading biogas into biomethane.

Capex costs are estimated in the range of $200-1,000/Tpa of biogas inputs depending on the lengths of the pipeline interconnection. A breakdown is given in the data-file, including comparisons with other estimates from other industry bodies and technical papers.

These costs can be worthwhile when they will be covered by fiscal incentives, CO2-conscious buyers, or for improving energy security.

Please download the data-file for further details on our numbers, the costs of upgrading biogas into biomethane, costs of activated carbon biogas filtering or for the varying compositions of biogas. We have also modeled the costs of biogas production and tabulated global biogas production by country as part of our biofuels research.

Plastic recycling: the economics?

Plastic recycling requires a $500/ton product price, to earn a 10% IRR off of c$1,000/Tpa of up-front capex, at a mechanical recycling facility with 0.3 tons/ton of CO2 intensity (up to 80-90% below virgin plastics, more than we expected). This data-file captures the economics and the costs of plastic recycling, especially for the mechanical recycling of PET.

10% of today’s end-of-life plastic is recycled, or around 40MTpa, within the global plastics industry. Substantively all of this is mechanical recycling, particularly for PET and HDPE, but also to a lesser extent, polypropylene, PVC and to an even lesser extent, polystyrenes.

Mechanical recycling of plastics starts by aggregating plastic waste into bales, transporting to a recycling facility (possibly via truck), then shredding. Next come successive stages of sorting, washing and drying, before clean plastics of the same grade are melted and re-pelletized.

Early sorting stages are aimed at removing paper and dirt, while later stages group plastics of similar optical properties. Washing stages may use caustic soda from the chlor-alkali process. Drying may also be used to char off contaminants.

Capex costs vary, facility-by-facility, but are built up in the data-file via surveying past projects. Energy costs also vary, hence the electricity use of plastic recycling (in kWh/ton) and the heat use (also in kWh/ton) are also built up by surveying technical papers.

The largest opex cost line in the model is labor, due to the need for precise sorting of waste materials. Labor intensity of plastic recycling is based on the disclosures from past projects.

Finally, economics are sensitive to transportation distances. Our model allows for plastic waste to be moved up to 50-miles, however higher prices are needed, for transport over longer distances.

Please download this data-file to stress-test the costs of plastic recycling, by varying the capex, opex, utilization, labor rates, feedstock costs, electricity prices, heat prices, CO2 costs plus taxes and incentives.

In our broader research, we have also explored chemical recycling of plastics, via plastic pyrolysis, and other existing and next-generation recycling technologies. For more on our outlook for polymers, please see our overview of plastics in the energy transition.

Gold hydrogen: the economics?

Economic model for white hydrogen production in the best case scenario.

Gold hydrogen could be recovered from the Earth’s subsurface, with costs ranging from $0.3-10/kg, and CO2 intensities of 0.2-5.0 kg/kg. This data-file models the economic costs of gold hydrogen, and its sub-variants such as white hydrogen and orange hydrogen.

Gold hydrogen denotes a constellation of possible hydrogen resources, that could be recovered from the Earth’s subsurface, analogous to the production of natural gas, with an upfront development capex, production profile, opex and other purification costs.

This data-file models the costs of gold hydrogen, and sub-variants such as white hydrogen and orange hydrogen. We have drawn on our models of shale gas production, other gas field production, pressure swing adsorption, gas dehydration, gas sweetening, gas pipelines, shale CO2 and broader CO2 of producing natural gas.

White hydrogen denotes the recovery of hydrogen from a natural hydrogen reservoir, via drilling and completing production wells, then separating gases at the surface. The best white hydrogen resources could cost $0.4/kg with a CO2 intensity below 0.4 kg/kg. This is materially better than today’s grey hydrogen from steam methane reforming. Although the numbers inflate with higher-cost wells and lower-purity hydrogen (sensitivity analysis below).

H2 price needed for a 10% IRR for a white hydrogen project depending on H2 percentage in the reservoir gas. The different lines are for well capex costs.

Orange hydrogen denotes an engineered approach, where water is injected into fractured reservoirs of ultra-mafic peridotites, where Fe(II) oxidizes into Fe(III), and thereby reduces water into hydrogen, which in turn can be recovered back to the surface. Orange hydrogen could be recovered at a cost of $1.5/kg, in extensive and naturally-fractured greenstones. The economics depend on the ratio of hydrogen resources to total project capex, at $0.4/kg in our base case.

A shale-type approach to orange hydrogen is also modelled, where a horizontal well is drilled into ultra-mafic peridotites, then fractured, then flowed back. Costs are higher here, likely in the range of $2-7/kg. Lower costs are possible in theory, but hinge on extensive fracturing along very long laterals, which would simultaneously need to be lower-cost than equivalent horizontal wells in today’s shale industry.

H2 price needed for a 10% IRR for an orange hydrogen project depending on well lateral length. The different lines are for total capex.

The costs of gold hydrogen can be stress-tested in the model, to interrogate the relationships between input variables and hydrogen economics. For all of the tabs in the model, you can vary the capex, purity and flow-rates of hydrogen wells. For the orange hydrogen play-types, you can vary ten variables into the composition of the peridotites.

For further discussion, please see our 19-page report into gold hydrogen opportunities, costs, CO2 intensities and challenges.

Natural hydrogen: going for gold?

Gas peaker plants: the economics?

Economic returns for a gas peaker plant over 30 years.

Gas peaker plants run at low utilizations of 2-20%, during times of peak demand in power grids. A typical peaker costing $950/kW and running at 10% utilization has a levelized cost of electricity around 20c/kWh, to generate a 10% IRR with 0.5 kg/kWh of CO2 intensity. This data-file shows the economic sensitivities to volatility and utilization.

The economics of gas peaker plants are all about volatility. Hourly power prices are lognormally distributed, which means their natural logarithms are normally distributed, per other commodity prices, and upside volatility is higher than downside volatilty (chart below).

The distribution of electricity prices is lognormal. This means it has a long higher price tail that peaker plants take advantage of.

Hence a grid with 10c/kWh mean average power prices can easily host a peaker that achieves 20c/kWh average power prices 10% of the time, even assuming non-perfect alignment between generation profiles and peak pricing. This can be flexed in the model, and is informed by actual data in ERCOT, CAISO, the UK, and Australia.

Another source of income for gas peaker plants is from capacity payments, which will usually make up 0-20% of total revenues. Grid balancing authorities are required by NERC and FERC to maintain healthy reserve margins that ensure they have adequate capacity to limit major outages to just once per decade.

While we have a separate model of combined-cycle gas turbine economics, capturing plants with >50% utilization, this data-file focuses in upon the economics of gas peaker plants, by modelling out the impacts of capacity payments and upside pricing volatility.

A fascinating observation is that each 1 c/kWh increase in power grid volatility increases peaker plant cash flows by $6/kW/year. Each 1pp reduction in utilization rate lowers cash flow by $5/kW/year. Numbers can be stress-tested in the data-file.

Cash flow for a gas peaker plant depending on power price volatility and plant utilization.

Other inputs in the model are informed by our data into gas turbine parameters, gas turbine capex costs, gas prices by region, CO2 prices and tax rates. However, we think the data-file is a neat way to stress-test the levelized costs of gas peaker plants, as they are impacted primarily by utilization and electricity price volatility.

Levelized cost of electricity: stress-testing LCOE?

Levelized cost of electricity of different electricity sources, in cents per kWh and their true CO2 intensity, in kg per kWh.

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,ย offshore wind, solar, hydro, nuclear,ย gas power,ย coal power,ย biomass,ย RNG, diesel gensets, geothermal, hydrogen, fuel cells, power transmission, batteries, thermalย 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).

Levelized cost: ten things I hate about you?

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.

Levelized cost of electricity of different electricity sources, in cents per kWh-e. Coal, gas, and solar are some of the cheapest but there is a lot of variability within each category. Note that this is on a partial electricity basis, not total.

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…

Levelized cost of total electricity of different electricity sources, in cents per kWh, versus their true net CO2 intensity, in kg per kwh-e. Coal is cheap yet polluting while green hydrogen is the opposite. Different gas options tend to be the best.

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.

Levelized cost of total electricity of different electricity sources, in cents per kWh, versus their capex cost, in $ per kWe. The best are coal and gas sources.

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. ย 

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).

Levelized cost of total electricity of different electricity sources, in cents per kWh, versus their direct land intensity, in acres per MWe. This method only counts land used by the power plants themselves.

Fiber optic data transmission costs?

The costs of fiber optic data transmission run at $0.25/TB per 1,000km in order to earn a 10% IRR on constructing a link with $120 per meter capex costs. Capex is 85% of the total cost. This data fiber breaks down the costs of data transmission from first principles, across capex, utilization, electricity and maintenance.

This model captures the costs of transmitting data across fiber optic cables, with a base case of $0.25/TB to earn a 10% IRR per 1,000km of data transmission. For an overview, please see our data-file into the energy use of fiber optic cables.

Energy intensity of fiber optic cables?

The economics of fiber optic cables are sensitive to cable length, utilization, and especially capex costs, which comprise 85% of the total costs, and are estimated in the data-file by tabulating details on 25 past fiber optics projects and their fiber counts (chart below).

The fiber running to an individual household might have 1-2 fiber optic strands, while a data-center interconnection (DCI) between two hyperscale facilities can have several thousand fibers.

Generally, each 10x increase in the number of fiber optic strands per cable only doubles the cost. A key reason is that 60-80% of total costs are construction, which do not change materially for higher-capacity cables.

Other factors that impact the capex costs of fiber optic cables: underground cables cost around 2-3x aerial cables. Undergrounding costs are also higher in rockier soil types and in urban environments. There are some nice cost breakdowns in the Capex_costs tab of the model.

Capex costs per Tbps of bandwidth can also be estimated, with a formula linking capex costs ($/m) to the bit rate (Trillion bits of data transfer capacity per second, Tbps). Some intercontinental fiber links quote a specific bandwidth in Tbps, helped by amazing multiplexing. For others, we can estimate the bandwidth from the fiber count.

Utilization rate is another variable that impacts the costs of fiber data transmission, which can be stress-tested in the model. Hurdle rate also matters. The energy consumption of fiber optic cables matters less, but is also included in the model.

Power distribution: the economics?

Cash flow for our financial model of a power distribution project over 30 years.

Power distribution costs to residential, commercial and industrial consumers are estimated at 3.5 c/kWh in this model, to generate a 10% levered return, in a 5km x 10MW distribution line, at 17kV, rated up to 400A, with a $150/kW-km capex cost, a 5% line loss and 40% annualized utilization. All of these inputs can be stress-tested in the data-file.

Power grids move electricity from generation sources, through the high-voltage transmission network (120-500kV), stepping down via transformers to the medium voltage grid (35-120kV) and then finally through smaller distribution lines (4-35kV), before ultimately reaching residential, commercial and industrial customers.

This data-file aims to model the costs of power distribution, across projects that average 4-35kV voltages, 10MW (strictly MVA) of average capacities, and distances from 1-30km. Our base case estimate is for 3.5 c/kWh for distributing electricity to consumers.

Power distribution costs are highly sensitive to capex costs and utilization rates, as shown in the chart below. 40% annualized utilization is a good rule-of-thumb for a distribution line that is at full capacity at the 1-2% peak load hours throughout the year. But capex is more complex.

Cost of power distribution as a function of average utilization for different capex levels. Higher utilization guarantees lower overall distribution cost.

One challenge is that no two projects are identical, which is borne out by reviewing different power cable configurations plus hundreds of planned projects in the capital improvement programmes of regional system operators. For example, MISO makes details available for all MISO transmission projects.

What is challenging is finding meaningful data-points, which represent the cost of adding new MVA-km to the distribution network. Most planning regions do not report the smaller expenditures separately (e.g., those costing <$1M, or <250k). Many projects also replace old equipment or improve reliability (e.g., transformers, circuit breakers, switchgear) but do not add any length to the network.

Nevertheless we have aimed to gather useful data-points in the projects tab (chart below). The range is very broad, from $10 to $5,000 /kW-km. As good average rules of thumb, large-scale overhead transmission lines cost $1.5/kW-km, rising to $30/kW-km for rural overhead distribution lines, $100/kW-km for urban distribution lines and $200/kW-km for underground distribution lines.

Capex cost of grid connections versus line capacity for projects in our database.

These costs matter for connecting new loads to the grid, such as electric vehicle charging points or other electricity-consuming facilities captured in our broader economic models. Please download the data-file to stress test the costs of electricity distribution.

Power transmission: the economics?

This data-file captures the costs of AC power transmission, requiring a 1.5c/kWh spread to earn a 10% levered IRR on a new 100km and 1,000MW transmission line, with capex costs of $1.5/kW-km. These numbers are supported by backup tabs, tabulating the costs of recent projects and a granular breakdown for the capex costs across 15 lines.

The capex costs of AC power transmission lines depend on both the capacity and length of the line, hence neither metric alone is particularly stable, when we tabulate the cost of past projects (chart below). Lengths range from 50-1,000km and power ratings range from 300-3,000MW. A better metric is the cost per kW of capacity and per km of distance, which we abbreviate as $/kW-km, averaging $1.5/kW-km.

The capex costs of AC power transmission lines can also be built up from first principles, as a breakdown of this $1.5/kW-km cost, across fifteen separate categories. The largest cost lines are installation (c25%), the metal structures (c20%), their foundations (c10%), the conductors (c10%), land preparation (c10%), substations (7%) and smaller contributors. Our source for these estimates are excellent granular disclosures from PJM.

As general rules of thumb, a higher voltage line requires larger and more expensive towers (first chart below), while a higher current line requires larger and more expensive conductors (second chart below). Nevertheless, all else equal, higher voltage and higher current lines will increase the power rating of a cable, and lower its total costs.

However our base case is relatively generous and can easily come in at $2-3/kW-km. Please download the data-file for sensitivies around land acquisition, permitting, site preparation, line length, line power, circuits per line, et al. While trenched lines are more resilient, they can also be 5-20x more expensive, according to some studies.

The key challenge, however, is not cost, but timing, as the average project in our screen takes 8-years to plan/permit, then 3-years to construct. It may take a long time to resolve power grid bottlenecks.

Power grids: the biggest bottleneck in the world?

Other cost lines are taken from the disclosures of regulated utilities, conductor costs, and other data-files we have constructed into high-voltage transmission lines. We have also separately modeled the costs of HVDCs, for longer-distance transmission. For more details, please see our overview of power transmission.

Power transmission: raising electrical potential?

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