the research consultancy for energy technologies
Our solar energy research looks for opportunities to ramp up the production of this low carbon electricity source, as part of the energy transition. We see levelized costs of solar ranging from 4-15c/kWh depending on location, and full-cycle CO2 intensity around 0.05 kg/kWh.
Debottlenecking bottlenecks is the largest opportunity to accelerate solar, including the production of PV silicon, silver front contacts, copper, aluminium, specialty glasses and plastics such as the EVA used in encapsulants. There will be lower carbon and more efficient ways to produce all of these input materials.
Power electronics are another important bottleneck featuring heavily in our work, in order to smooth out the volatility of solar generation. This includes inverters, and synthetic sources of inertia, reactive power and capacitance. Solar output typically flickers downwards by over 10%, around 100 times per day. Even more challenging, year-over-year output typically varies by 6%, between more and less cloudy years.
Improving efficiency is another important driver. We think ultimately the best silicon-based solar cells will reach 27-28% efficiency. This is remarkable, given the maximum theoretical “Shockley-Queisser” limit on mono-crystalline PV silicon is 30%. We are recently seeing strong progress by ramping up TOPCON cells in lieu of the previous industry-standard PERC cell designs.
The grid-forming inverter market may soon inflect from $1bn to $15-20bn pa, to underpin most grid-scale batteries, and 20-40% of incremental solar and wind. This 11-page report finds that grid-forming inverters cost c$100/kW more than grid-following inverters, which is inflationary, but integrate more renewables, raise resiliency and efficiency?
Solar trackers are worth $10bn pa. They typically raise solar revenues by 30%, earn 13% IRRs on their capex costs, and lower LCOEs by 0.4 c/kWh. But these numbers are all likely to double, as solar gains share, grids grow more volatile, and AI unlocks further optimizations? This 14-page report explores the theme and who benefits?
This data-file summarizes the leading companies in solar trackers, their pricing (in $/kW), operating margins (in %), company sizes, sales mixes and recent news flow. Five companies supply 70% of the market, which is worth $10bn pa, and increasingly gaining importance?
The solar tracker industry is worth c$10bn pa, as 20 companies shipped 95GW of trackers in 2023, mostly single-axis horizontal systems.
It is a rare part of the solar supply chain, that is not dominated by Chinese suppliers – unlike PV silicon, or PV modules. The leading markets for deployment are the US and Europe, and this is also where many of the leading companies in solar trackers are based.
The solar tracker industry is concentrated. Five companies have c70% market share, helped by features that facilitate installation, and software to optimize the operations of utility-scale solar.
This data-file summarizes the leading companies in solar trackers, their pricing (in $/kW), operating margins (in %), company sizes, sales mixes and recent news flow.
The economics of solar trackers are also assessed, and can be stress-tested in the data-file. 10% IRRs can be achieved on solar trackers costing $165/kW (installed basis) that uplift solar revenues by 25%, through a mixture of higher output and higher time-value.
Uplifts in performance can be determined top-down using reported performance of solar trackers, or bottom-up from first principles, based on calculating solar insolation.
IRRs reach 20-30% of the best systems, which is tantamount to lowering solar LCOEs by 2c/kWh, when flowed through our model of utility-scale solar economics.
Hence, as one of the leaders, Array Technologies, has highlighted, solar tracker demand has been growing 30% faster than the overall solar market in recent years.
This 9-page study finds unexpectedly strong support for co-deploying grid-scale batteries together with solar. The resultant output is stable, has synthetic inertia, is easier to interconnect in bottlenecked grids, and can be economically justified. What upside for grid-scale batteries?
Solar+battery co-deployments allow a large and volatile solar asset to produce a moderate-sized and non-volatile power output, during c40-50% of all the hours throughout a typical calendar year. This smooth output is easier to integrate with power grids, including with a smaller grid connection. The battery will realistically cycle 100-300 times per year, depending on its size.
The output from a standalone solar installation is notoriously volatile, varying +/- 5% every 5-minutes on average, plus sudden power spikes and drops, and achieving an annual utilization factor of just 20%.
But how does co-deploying solar+batteries lower the volatility? This data-file uses real-world data, from an Australian solar asset, measured at 5-minute intervals, and then applies simple rules about when to flow power into and out of the batteries, to maximize the delivery of 100MW, smooth, non-volatile power.
The solar+battery output also includes synthetic inertia and frequency regulation, which helps rather than hinders overall grid stability.
The title chart above shows how the output profile of our solar+battery system might behave on a summer’s day, with the net asset providing 100MW to the grid for 24-hours. Excess solar is shunted to the battery throughout the day. Then the battery is gradually discharged to zero after sunset. This model works well in the summer.
However solar generation is highly seasonal, and on a winter’s day, this exact same battery does help to keep output stable at 100MW, but it only achieves 20% of a full charge-discharge cycle, as there is simply not enough solar generation to fill the battery. The bigger the battery, the less likely it gets full in the summer, and the less utilized it is in the winter.
This can be stress-tested in the data-file. We can also calculate the number of charge-discharge cycles that different batteries achieve, if they are charged exclusively with solar generation. Some decision-makers assume daily charging-discharging when modeling the economics of batteries, but this is shown to be much too optimistic (below).
Overall, remarkably, solar+battery co-deployment model means that a 275MW solar installation + a 275MW battery can dispatch 95% of its generated output through a mere 100MW grid connection. This is why co-deploying renewables+batteries can help to surmount power grid bottlenecks. And in turn, this is why we think battery co-deployment is accelerating.
If a battery is run purely for solar smoothing, with 1MW of battery capacity per MW of solar, then the battery will tend to achieve 180 charge-discharge cycles throughout the year, and it will allow a 275MW solar asset to output precisely 100MW to the grid in c50% of the time throughout an entire year (but still producing no power about 40% of the time).
The production profiles vary month by month. The results vary with battery sizing and charging-discharging rules. These sizings and rules can be stress-tested in the data-file, to assess how different-sized batteries result in different dispatch rates and charge-discharge cycle counts.
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 -35ยบS 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.
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.
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.
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.
Solar ramps from 6% of global electricity in 2023, to 35% in 2050. But could any regions become Solar Superpowers and reach 50% solar in their grids? And which regions will deploy most solar? This 15-page note proposes ten criteria and ranks 30 countries. The biggest surprises will be due to capital costs, grid bottlenecks and pragmatic backups.
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 23ยบ 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 tilt, 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.
Electromagnetic radiation is a form of energy, exemplified by light, infrared, ultraviolet, microwaves and radio waves. What is the energy content of light? How much of it can be captured in a solar module? And what implications? We answer these questions by modelling the Planck Equation and Shockley-Queisser limit from first principles.
Electromagnetic radiation is the synchronized, energy-carrying oscillation of electric and magnetic fields, which moves through a vacuum at the speed of light, which is 300,000 km per second.
Most familiar is visible light, with wavelengths of 400 nm (violet) to 700 nm (red), equating to frequencies of 430 (red) to 750 THz (violet).
At the center of the solar system, our sun happens to emit c40% of its energy in the visible spectrum, 50% as infra-red and c10% as ultraviolet, and very little else (e.g., X-rays, gamma rays at high frequency; microwaves and radio waves at high wavelength). But this is not a coincidence…
Planck’s Law quantifies the electromagnetic energy that will be radiated from a body of heat, across different electromagnetic frequencies, according to its temperature, the speed of light, Boltzmann’s constant (in J/ยบK) and Planck’s constant (in J/Hz).
In the chart below, we have run Planck’s equation for radiating bodies at different temperatures from 3,000-8,000ยบK, including the sun, whose surface is 5,772ยบK. Then we have translated the units into kW per m2 of surface area and per nm of wavelength.
Hence by integration, the ‘area under the curve’ shows the total quantity of electromagnetic radiation per m2. If the surface of the sun were just 10% hotter, then it would emit c50% more electromagnetic radiation and 55% more visible light!
Charts like this also explain why the filament of an incandescent light bulb, super-heated to 2000-3000ยบC is only going to release 2-10% of its energy as light. Most of the electromagnetic radiation is in the infra-red range here. And this is the reason for preferring LED lighting as a more efficient alternative. LEDs can reach 60-90% efficiency.
Planck’s Law also matters for the maximum efficiency of a solar module, and can be used to derive the famous Shockley-Queisser limit from first principles, which says that a single-junction solar cell can never be more than c30-33% efficient at harnessing the energy in sunlight.
Semiconductor material has a bandgap, which is the amount of energy needed to promote a single electron from its valence band into its conduction band: a higher energy state, from which electricity can be drawn out of a solar cell. For silicon, the bandgap is 1.1 eV.
What provides the energy is photons in light. The energy per photon can be calculated according to its wavelength. This involves multiplying Planck’s constant by the Speed of Light, dividing by the wavelength, and then converting from Joules to electronVolts. For a radiating body at 5,772ยบK, the statistical distribution of photons and their energies is below.
So what bandgap semiconductor is best? If the bandgap is too high (e.g., 4eV), then most of the photons in light will not contain sufficient energy to promote valence band electrons into the conduction band, so they cannot be harnessed. Conversely, if the bandgap is too low (e.g., 0.5eV), then most of the energy in photons will be absorbed as heat not electricity (e.g., a photon with 2.0 eV would transfer 0.5eV into electron promotion, but the remaining 1.5 eV simply heats up the cell).
The mathematical answer is that a bandgap just above 1.3 eV maximizes the percent of incoming sunlight energy that can be transferred into promoting electrons within a solar cell from their valence bands to their conduction bands, at 43-44% (chart below).
If we run a sensitivity analysis on the bandgap, the next chart below shows that our 43-44% conversion limit holds for any semiconductors with a bandgap of 1.1-1.35eV, more of a plateau than a sharp peak.
The Shockley-Queisser limit is usually quoted at 30-34%, which is lower than the number above. In addition to the losses due to incomplete capture of photon energy, the maximum fill factor of a solar cell (balancing load, voltage and current) is around 77%, so only 77% x 44% = 34% of the incoming light energy could actually be harnessed as electrical energy. Moreover, in their original 1961 paper, Shockley and Queisser assumed an 87% efficiency limit for impedance matching relative to the 77%, which is why the number they originally quoted was around 30%.
Another issue is that the solar energy arriving at a given point on Earth has been depleted in certain wavelengths, as they are absorbed by the atmosphere. 1,362W/m2 of sunlight reaches the top of the Earth’s atmosphere. While on a clear day, only around 1,000W/m2 makes it to sea level at the equator. We know the atmosphere absorbs specific infrared wavelengths as heat, because this is the entire reason for worrying about the radiative forcing of CO2 or radiative forcing of methane.
Hence for an ultra-precise calculation of maximum solar efficiency, we should not take the Planck curve, but read-out the solar spectrum reaching a particular point on Earth, which will itself vary with weather!!
The biggest limitation on the efficiency of single-junction solar cells is that they only contain a single junction. This follows from the discussion above. But what if we combine two semiconductors, with two bandgaps into a ‘tandem cell’. The top layer has a bandgap of 1.9eV (e.g. perovskite) and the second has a bandgap of 1.1eV (e.g., silicon). The same analysis now shows how the maximum efficiency can reach 44%.
Cells with multiple semiconductors are already being commercialized. For example, we wrote last year about heterojunction solar (HJT) and this year about the push towards perovskite tandems in solar patents from LONGi. It feels like the ultimate goal will be multi-junction cells that capture along the entire solar spectrum (chart below). It will simply take improvements in semiconductor manufacturing.
Elsewhere in the electromagnetic spectrum, this data-file also contains workings into the energy efficiency of microwave energy, transmitting it through space and converting it back to useful electricity via rectennas.
All of the numbers and calculations go back to first principles, in case you are looking to model the Planck Equation, Shockley-Queisser limit, multi-junction solar efficiency, lighting efficiency, or other calculations of electromagnetic radiation energy.
This data-file is a screen of leading companies in vapor deposition, manufacturing the key equipment for making PV silicon, solar, AI chips and LED lighting solutions. The market for vapor deposition equipment is worth $50bn pa and growing at 8% per year. Who stands out?
Vapor deposition uses 250-1,250ยบC temperatures and vacuums as low as 1 millionth of an atmosphere, to deposit nm-ฮผm thick layers of ultra-pure materials onto semiconductor and solar substrates, to make PV silicon, solar modules, computer chips, AI chips, LEDs, plus for hardened metals, cutting tools, insulated glass and aluminized food packaging.
We figured that we needed to compile this screen after reviewing LONGi‘s patents in early-2024. The technology underpinning HJTs and TOPCON modules is very clever, but it is clear from the patents, that it all relies upon vapor deposition. Hence who are the crucial shovel-makers here?
Half of the $50bn pa market is dominated by five public companies with 25-50% exposure to vapor deposition and c30% EBIT margins, based on our screen of leading companies in vapor deposition.
In overall Semiconductor Production equipment, the world leader is Applied Materials, which is based in the US, produces vapor deposition for the solar industry plus for the ‘angstrom era’ of chips, and has $170bn of market cap, more than Schlumberger, Baker Hughes and Halliburton combined.
In chemical vapor deposition for the semiconductor industry, a large Japanese company stood out, claiming 43% market share, and also the only integrated product suite covering the four sequential processes of deposition, coating/developing, etching and cleaning.
In the $700M niche of Metal Organic CVD, as used to make 70% of LEDs globally, but also for wide-bandgap semiconductors, such as SiC and GaN, the market leader is a publicly listed German specialist, with 70% market share.
In laser annealing, which can modify chemical properties over 10-100nm within nanoseconds, for making AI chips, a US-listed specialist stood out as a leader, and it also has a well-regarded ion beam deposition line, seen as a successor to PVD as it achieves larger and uniformly deposited grains.
Our experience as energy analysts has been that companies in the semiconductor supply chain are now just as relevant to the future of global energy as those in the subsea supply chain. Hence over time we will add to this screen of leading companies in vapor deposition.