Industry Data for the Energy Transition

Coal power generation: minute-by-minute flexibility?

Coal power generation is aggregated in this data-file, at the largest single-unit coal power plant in Australia, across five-minute intervals, for the whole of 2023. The Kogan Creek coal plant produces stable baseload power, with an average utilization rate of 85%. But it exhibits lower flexibility to backstop renewables than gas-fired generation.

Kogan Creek is a coal-fired power plant in Brigalow, Queensland, Australia, located 230km west of Brisbane. It has a nameplate capacity of 787MW. It is thus the largest single coal unit in Australia.

The Kogan Creek coal plant uses supercritical steam in its power cycle, working at pressures of 250 bar and temperatures of 560°C, and a total system efficiency of 40%.

As a case study for the flexibility of large-scale coal generation, we have evaluated this coal plant’s output, every 5-minutes over 2023 (105,000 data-points!), using data from AEMO.

Average utilization for the Kogan Creek coal plant in 2023 was 85%, varying remarkably little over the year, as coal provides low-cost baseload to the grid. The plant’s high utilization was mainly hampered by shutdowns, including a two-day outage in October and a five-day outage in August, lowering monthly utilizations to 71% and 66% respectively.

Daily average generation for Kogan Creek coal plant for each month in 2023. Also noted are the best and worst days of each month.

Volatility for a coal plant is low by design. The average 5-minute-by-5-minute volatility of Kogan Creek is +/-1%, while a typical large solar or wind installation is +/- 5%. For wind and solar, this is true volatility. But for coal, it is mostly flexibility, i.e., intentional variation in output levels in response to grid demand and grid pricing.

Several outages occurred at Kogan Creek in 2023, ranging from a couple of hours to several days. Ramping up to full capacity from a cold start appears to take 4-8 hours (chart below). This fits our broader data into power plants’ ramp rates from a cold start.

Solar power has a clear impact on the daily profile of coal generation. Similarly to Stockyard Hill wind farm, output at Kogan Creek was 10-20% lower than average between 8am and 3pm over the whole year. Coal ramps down in the middle of the day to make room for solar but still provides 60% of all electricity produced in Queensland.

This is yet another entry in our series analyzing the generation profiles of different electricity sources using data from the Australian grid. Previously we have looked at gas, solar, wind, and battery storage. Our key conclusion remains that gas-fired generation will entrench as the leading backstop for volatile renewables.

Commodity prices: metals, materials and chemicals?

Annual commodity prices are tabulated in this database for 70 material commodities, as a useful reference file; covering steel prices, other metal prices, chemicals prices, polymer prices, with data going back to 2012, all compared in $/ton. 2022 was a record year for commodities. We have updated the data-file for 2023 data in March-2024.

Material commodity prices flow into the costs of producing substantively everything consumed by human civilization, and increasingly consumed as part of the energy transition. Hence this database of annual commodity prices is intended as a useful reference file. Note it only covers metals, materials and chemicals. Energy commodities and agricultural commodities are covered in other TSE data-files.

Source and methodology. The underlying source for this commodity price database is the UN’s Comtrade. This useful resource covers trade between all UN member countries, across thousands of categories, in both value terms ($) and mass terms (kg). Dividing values (in $) by masses (in kg) yields an effective price (in $/kg or $/ton). We have then aggregated, cleaned and averaged the data for 70 materials commodities.

The median commodity in the data-file costs $2,500/ton on an unweighted basis. Although this ranges from $20/ton for aggregates to $75M per ton for palladium metal.

2022 was a record year for material commodity prices. The average material commodity priced 25% above its 10-year average and 40 of the 70 commodities in the database made 10-year highs.

Steel prices reached ten-year highs in 2022, averaging $2,000/ton across the different steel grades that are assessed in the data-file. This matters as 2GTpa of steel form one of the most important underpinnings in all global construction. Our steel research is aggregated here.

Base metal prices averaged 40% above their ten-year averages in 2022, as internationally traded prices rose sharply for nickel, rose modestly for aluminium and zinc, and remained high for copper (chart below).

Battery metals and materials prices rose most explosively in 2022, due to bottlenecks in lithium, cobalt, nickel and graphite. This is motivating a shift in battery chemistries, both for vehicles and for energy storage. It also means that the average battery material in our data-file was higher priced than the average Rare Earth metal in the data-file (which is unusual, but not the first time).

Commodity chemicals all rose in 2022 across every category tracked in our chart below. These chemicals matter as intermediates. On average, sodium hydroxide prices reached $665/ton in 2022, sulphuric acid prices reached $140/ton and nitric acid prices reached $440/ton.

500MTpa of global plastics and polymers demand is covered in our plastics demand database. Both finished polymer prices (first chart) and underlying olefins and aromatics (as produced by naphtha crackers, second chart) prices rose sharply in 2022. Our recent research has wondered whether terms of trade are likely to become particularly constructive for polyurethanes.

Silicon prices matter as they feed in to the costs of solar, and traded silicon prices also reached ten year highs in 2022, before correcting sharply in 2023. Silica prices surpassed $70/ton, silicon metal prices reached $4,000/ton and polysilicon prices surpassed $30/kg (charts below).

The full database captures 70 globally traded materials commodities and their annual prices over time in $/ton, year by year, from 2012-2022. These are: Acrylonitrile prices, Adhesives prices, Aggregates prices, Aluminium prices, Ammonia prices, Battery Graphite prices, Benzene prices, Butadiene prices, Carbon Fiber prices, Cement prices, Cobalt prices, Cobalt Oxide prices, Cold Rolled Steel prices, Concrete prices, Copper prices, Copper Wire prices, Cumene prices, Electric Motor and Generator prices, Electrical Transformer prices, Epoxide prices, Ethanol prices, Ethylene prices, Ethylene Oxide prices, EVA prices, Formaldehyde prices, Glass Fiber prices, Gold prices, Graphite Anode prices, Graphite paste prices, HCl prices, HDPE prices, HF prices, Hot Rolled Steel prices, Hydrogen Peroxide prices, Integrated Circuit prices, LDPE prices, LiPF6 prices, Lithium Carbonate prices, Lithium Metal prices, Manganese prices, Manganese Oxide prices, Methanol prices, NaCN prices, Nickel prices, Nitric Acid prices, Paint prices, Palladium prices, PET prices, Phosphoric Acid prices, Platinum prices, Polyethylene prices, Polysilicon prices, Polyurethane prices, Propylene prices, Propylene Oxide prices, PTFE prices, Rare Earth Magnet prices, Scandium & Yttrium prices, Silica prices, Silicon Metal prices, Silver prices, Sodium Hydroxide prices, Stainless Steel prices, Steel Alloy prices, Sulfuric Acid prices, Toluene prices, Tubular Steel prices, Urea prices, Vehicle prices, Xylene prices, Zinc prices.

Oscar Wilde noted that the cynic is the man who knows the price of everything, but the value of nothing. To avoid falling into this trap, we also have economic models for most of the commodities in this commodity price database.

We will continue adding to this commodity price database amidst our ongoing research. You may find our template useful for running Comtrade queries of your own. Or alternatively, if you are a TSE subscription client and we can help you to use this useful resource, then please do email us any time.

Reserve margins: by ISO and over time?

Reserve margins across major ISOs in the US power grid average 29% in 2024, are seen declining to 21% in the next decade by NERC, but could decline further, falling below their recommended floors of at least 15%. Possible reasons include demand surprising to the upside, or controversies in the capacity contributions of renewables. This data-file tabulates reserve margin forecasts, by ISO region, and over time.

Reserve margins are calculated by dividing (a) total power generation resources (in MW) that are seen to be available during times of peak grid demand by (b) total anticipated peak grid demand (in MW). Then subtract 1 to yield a percentage figure.

NERC guidelines recommend keeping reserve margins well above 15%, in order to limit Loss of Load Expectations (LOLE) to 1 event per 10-years, as part of resilient power grids.

Aggregated across major US ISOs, reserve margins currently average 29% in 2024, are projected by NERC to decline to 21% in the next decade, but could decline further if power demand surprises to the upside, or resource additions are delayed or disappoint.

This data-file aggregates NERC’s reserve margin forecasts over time, for major ISOs in the US, such as MISO, PJM, ERCOT, CAISO, NYISO, ISO NE, SPP and SERC FLA. Underlying charts are available on a separate tab for each region. We have aggregaed all the regions together in the charts above.

In each case, we have plotted expectations for peak demand, net demand after demand responses and anticipated resources, which in turn comprise existing firm resources plus Tier 1 capacity additions.

In the past, reserve margins have defied pessimistic projections. The main reason has been downwards reivisions in demand, and upwards revisions in renewables resources. What is changing is that demand is now surprising to the upside, linked to the rise of EVs and the rise of AI.

Another controversy in measure reserve margins is how to count the capacity from renewables. 100MW of gas generation is almost always available to provide 100MW. We think the forecasts from NERC and from underlying ISOs may be ascribing 50-60MW of availability per 100MW of renewables. But due to the intercorrelation of renewables, and especially as renewables get built out, this may turn out to be too high.

The underlying source of the data is from NERC’s annual long-term reliability assessments.

Grid-scale battery operation: a case study?

Grid-scale batteries are not simply operated to store up excess renewables and move them to non-windy and non-sunny moments, in order to increase renewables penetration rates. Their key practical rationale is providing short-term grid stability to increasingly volatile grids that need ‘synthetic inertia’. Their key economic rationale is arbitrage. Numbers are borne out by our case study into battery operations.

Victorian Big Battery was the largest grid-scale battery in the world, when it was installed in Victoria, Australia in 2021. It was installed by Neoen and consists of 212 x 3MWH Tesla Megapacks. Total capacity is 300MW on a power basis and 450MWH on a storage basis (as can be contrasted in our MW/MWH battery comparison).

How does a grid-scale battery operate in practice? To answer this question, we collated an entire year of load and generation data for Victorian Big Battery, using data from Aemo.

Across the entirety of 2023, Victorian Big Battery absorbed 122GWH of power and discharged 102GWH of power, for a total net efficiency of 84%. This is in line with our typical estimate for a grid-scale lithium ion battery to have c85% efficiency.

The utilization of the battery equates to 0.7 charge-discharge cycles per day, as shown in the chart below, in MWH of charge-discharge per day per MWH of storage capacity. However, the utilization rates varied throughout the year.

Rate of charging and discharging for the Victorian Big Battery throughout 2023.

Outside of summer months, utilization of Victorian Big Battery averaged 1MWH of charge-discharge per day per MWH of storage capacity. The profile for the median day is shown in the chart below.

Some commentators argue that the main role of grid-scale batteries will be to enable higher solar penetration in power grids, by ‘storing up excess sunlight in the middle of the day, and then re-releasing that energy at night’. This is not entirely borne out by Victorian Big Battery.

The primary economic rationale for Victorian Big Battery has been economic arbitrage. The battery consistently buys power during times of low prices (1am-5am, 10am-3pm, chart below) and sells power at times of high prices (6am-8am, 4pm-8pm).

Power prices in Victoria, Australia for over 2023.

The battery is reasonably good at economic arbitrage, but not perfect. When prices were negative in May-2023, Victorian Big Battery was able to charge about 80% of the time. When prices surpassed A$200/MWH, it was able to discharge about 80% of the time. The average storage spread is calculated in the data-file, but was lower than the level we think is necessary for economic returns in our grid-scale battery models.

Percentage of time the Victorian Big Battery spent charging, discharging, and idling for different power prices.

What is helping economics is that the battery is also providing crucial smoothing services in the grid. Inertia and frequency control come for free with rotating generators. But in renewable heavy grids, these are provided as an ancillary service by grid-scale batteries. This is visible in the volatility of charts in the data-file. 5-minute by 5-minute volatilty is +/- 40MW.

Moreover in summer months, Victorian Big Battery is operated almost entirely as a contingency reserve. Specifically, this means that it is not charged and discharged to its maximum extent each day, but sits with a high state of charge, and provides 250MW of system integrity protection (SIPS), including synthetic inertia for solar-heavy grids, and the capacity to kick in should some large new load start up, or some large generation source suddenly disconnect. This is part of capacity markets.

Underlying data into real-time battery charging and discharging, at 5-minute intervals, are available in the data-file, for a selection of months (Apr-23, May-23, Jan-23) and compared with wholesale grid prices. It is interesting to pour over the charts, and see how batteries behave minute-by-minute, day-by-day and in response to pricing signals.

Wind generation case study: minute by minute volatility?

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.

Variability in wind generation over a year. Differences between the best and worst days of each month are significant.

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.

Wind generation profiles of median days in December, July, and April.

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).

Wind generation profiles of the best and worst days over 2023 for Stockyard Hill.

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.

Solar generation: minute by minute volatility?

Statistical information on the generation of Darlington Point solar plant in Australia. The daily averages, and standard deviations for day-by-day changes and 5min-by-5min changes.

The volatility of solar generation is evaluated in this case study, by tracking the output from a 275MW solar project, at 5-minute intervals, throughout an entire calendar year. Output is -65% lower in winter than summer, varies +/-10% each day, and +/- 5% every 5-minutes, including steep power drops that in turn require back-ups.

Darlington Point is a 333MW-dc and 275MW-ac PV solar facility, in New South Wales, Australia, equidistant between Sydney and Adelaide, 500km inland, at -35ºS latitude. As a case study for large-scale solar generation, we have evaluated its output, every 5-minutes, over the course of 2023 (105,000 data-points!).

Darlington Point ran at a 23% average load factor in 2023, generating 545 GWH of electricity. However, the data-file illustrates four types of solar volatility.

We see the volatility of solar generation most fairly by looking at the load profile in the median day of each month across the year. In other words, output was higher than shown in the median day, across 50% of the days in the month, and lower across another 50% of the days.

Solar production of the median day for each month in 2023 for Darlington Point solar park.

Seasonal volatility is extreme. Darlington Point achieved a very high load factor of 35% during the peak of summer, from December to February, but just 12% load factor in June, which means winter output was 65% lower than summer output. Backstopping seasonal volatility is challenging for batteries.

Average daily generation each month in 2023 and the possible daily variation for Darlington Point solar park.

Daily volatility averaged +/- 10%. In other words, output on any typical day of solar generation was likely to be +/- 10% higher or lower than the previous day, due to changes in weather.

Intra-day volatility sees output ramping in the morning, plateauing in the afternoon, then declining in the evening. The intra-day pattern varies month-by-month. February was the ‘best month’ as most days were sunny. Often generation declined in the afternoon, which we think is due to convective cloud formation.

Average load profiles for Darlington Point solar over the whole year, in February, and in June 2023.

Minute-by-minute volatility averages +/- 5% every 5-minutes. However, there is a sharp skew in the data, as output is consistently zero at night, many days contained stable generation for long periods, and then have sharp power drops due to cloud cover. On some days, output varies +/- 10% every 5-minutes. This is another reason solar requires back-ups.

Data in the file are from Australia’s Energy Market Operator (AEMO). The statistical analysis and collation into Excel are our own. Flipping through the tabs of the data-file is a nice way to visualize volatility.

Conclusions are similar to other data-files we have compiled into solar volatility. We see increasing value in backstopping volatility across global energy systems.

Gas power generation across five-minute intervals?

Gas power generation data are aggregated in this data-file, covering ten of the largest CCGTs and gas peaker plants in Australia, across five-minute intervals, in May-2024 and in May-2014. This makes for a fascinating case study into how gas turbines are used to stabilize power grids, backstop renewables, and how this has changed over time.

AEMO is Australia’s Energy Market Operator. It maintains a fantastic data portal, with multiple TB of available data, including the generation of every facility in the power grid, at five-minute intervals.

The biggest challenge for understanding gas power generation is the amount of data. A single month contains 9,000 x 5-minute intervals.

Hence we have selected ten of the largest gas generation facilities, and studied their output in both May-2014 and May-2024 as case studies. And even this limited exercise ended up yielding a 24MB data-file!

In May-2024 the average gas turbine ran at 26% utilization, which is lower than the base case in our gas generation economic model.

However the average turbine also ran 90% of its nameplate capacity across 10% of the hours, and peaked at 96% of its nameplate capacity when the regional grid was running short (chart below).

Maximum and average utilization rates for different Australian gas plants in May 2024. The average utilizations are very low but each plant reached over 90% utilization at least once during the month.

The daily pattern of gas generation in the Australian power grid shows a sharp drop in the middle of the day, to accommodate solar, while these plants then ramp sharply in the evening, in extremis ramping up to 90% of their total aggregate capacity. Can this really be replicated by batteries?

Gas power generation also varies widely, from baseload plants running at 90% utilization through to peaker plants that fire for just a few hours per month and thus run at just 1% utilization.

As an example of a baseload generator, the chart below shows the output from the 440MW Tallawarra-1 CCGT in May-2014, running at 90% utilization, at >50% efficiency, with output typically dipping from 1-5am, when daily demand troughs.

As an example of a peakload generator, the chart below shows the output from the 160MW Townsville CCGT, at Yabulu, in May-2024, firing up for 5-hours most days, from 4-9pm, sometimes longer. The total utilization rate for the month is 16%, but when the plant does run, then it is running above 90% of its nameplate capacity 93% of the time.

Another key reason that gas plants help to backstop renewables volatility is their rapid responsiveness. Smaller simple-cycle turbines can ramp from a cold start within 20-30 minutes, while larger CCGTs can ramp from a cold-start within 1-3 hours (chart below). For contrast, ramping coal plants from a cold start takes 4-8 hours, based on our case study into coal generation profiles.

The data-file illustrates gas power generation, across minute-by-minute, hour-by-hour volatility intervals, for ten facilities, in May-2014 and May-2024. Grids are relying increasingly on gas backups.

We are happy to help TSE clients set themselves up to pull data from the AEMO database, in order to run their own analyses. Please contact us if we can help you on this front.

Commodity price volatility: energy, metals and ags?

Commodity prices are distributed lognormally, so the average price will tend to be higher than the median price.

Commodity price volatility tends to be lognormally distributed, based on the data from twelve commodities, over the past 50-years. Means are 20% higher than medians. Skew factors average +1.5x. Standard errors average 50%, while more volatile prices have more upside skew.

This data-file contains data plotting the statistical distributions of volatility for twelve major commodities, ranging across energy commodities such as oil, gas and coal; industrial metals such as iron ore, copper and aluminium; precious metals such as gold and silver; and agricultural commodities such as sugar, soybeans and palm oil.

Commodity price volatility tends to be lognormally distributed, based on starting with the charts shown below, then smoothing all of these statistical distributions together, for the title chart shown above. This statistical distribution is intuitive, as prices are effectively uncapped to the upside during commodity shocks, but they are effectively capped to the downside, as commodities cannot sustainedly trade below zero.

A fascinating finding is that when commodities are more volatile overall (e.g., as indexed by standard error) then this is 75% correlated with skew in the commodity, or in other words, the mean tends to run further above the median. In other words, this is another indicator that commodities illustrate more upside volatility than downside volatility.

The positive skew (mean to median ratio) and standard error of commodity prices. These measures turn out to be 75% positively correlated, so rising volatility drives the average price further from the median.

If base case forecasts are thought of as the median price levels of commodities (e.g., $65/bbl for oil over the past 50-years, $8/mcf for global gas, etc), then the data imply that mean average prices will tend to run 1.1 – 1.5x above median expectations.

The final two tabs of the data-file model the lognormal volatility of commodities, illustrating how the value of commodity marketing and trading is likely to rise during the energy transition, as volatility grows on an absolute basis, but also inter-regional volatility is growing due to the ascent of renewables such as wind, solar and hydro.

Fantastic underlying data that helped to build this data-file came from the World Bank pink sheets, which we recommend to anyone looking for free monthly or annual commodity price data.

For more recent and more detailed pricing across a wider range of commodities, please see our commodity price database, for time series that go further back in time to the 1800s, please see our database of very long-term commodity prices, while we also have analysis into the performance of commodities during conflicts and performance of commodities during recessions.

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, 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).

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.

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.

Solar insolation: by latitude, season, date, time and tilt?

Solar insolation varies from 600-2,500 kWh/m2/year at different locations on Earth, depending on their latitude, altitude, cloudiness, panel tilt and azimuth. This means the economics of solar can also vary by a factor of 4x. Seasonality is a key challenge at higher latitudes. Active strategies are emerging for orienting solar modules.

1,353 W/m2 of solar energy arrives at the top of the Earth’s atmosphere, based on the Planck Equation, equivalent to almost 12,000 kWh/m2/year. Amazingly, solar is changing the world, even though only c2-3% of this energy is ultimately getting harnessed today.

((The location of the losses in the chart above is also a reason for exploring solar in space, then beaming the power back to Earth)).

50% of all solar energy is inaccessible due to night time (chart below). Another 20-40% is inaccessible as it is absorbed by the atmosphere and clouds (depending on location). And of the insolation that does reach a solar module, only c20-25% is currently converted into useful electricity, in todays best HJT modules.

Calculating the insolation ultimately available for solar modules depends on the mass of atmosphere that is traversed by incoming sunshine, which varies hour-by-hour, with the elevation of the sun in the sky (i.e., vertical height) and its azimuth (i.e., compass point bearing).

Calculating these numbers is quite complex, because the Earth is 23º declinated on its axis. Hence the sun’s elevation and azimuth vary hour-by-hour, day-by-day and by location. Nevertheless, the charts below plot elevation and azimuth at a 45-degree latitude, based on 8,760 calculations throughout the year (24 hours per day x 365 days). The latitude can be varied in the data-file, which also contains hour-by-hour granularity.

Insolation at ground level can thus be calculated, based on the mass of air that has been traversed (chart below left). However, fixed solar modules are not always pointed directly at the sun. This can sacrifice 30-60% of the maximum available insolation, simply due to misalignment (chart below right), which is also calculated hour by hour in the data-file.

For fixed modules, losses can be minimized by matching the tilt of the panels to the latitude at which they are situated (chart below). The losses can be reduced even further with solar trackers, which rotate the panels to follow the sun, although this does also add cost.

It is usually best to orient solar modules directly South (in the North Hemisphere). But efficiency may be sacrificed for economics! West-facing panels generate one-third less energy than South-facing ones. But the generation profile is 2-4 hours later, to smooth out the duck curve.

Insolation available to solar modules can realistically vary from 700 – 2,400 kWh/m2/year, depending on latitude and cloudiness. These numbers can be stress-tested in the data-file.

Depending on latitude, generation will also be 0-80% lower in the winter versus the summer. This is visible in the charts above, as high latitudes have short days in the winter, while even when the sun is up, it is only sitting at a low angle in the sky. This seasonality is extremely challenging to back up economically using batteries.

The full data-file allows you to calculate solar insolation, and resultant solar generation, hour-by-hour and then on a fully annualized basis; by stress-testing latitude, elevation, module tile, module azimuth, cloud cover, tracking efficiency and module efficiency. This is helpful for informing our solar economic models. The numbers match our findings from assessing real-world solar volatility. A fantastic resource that helped us with the equations is pveducation.org.

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