Solar Energy Research and Data

Renewables: how much time to connect to the grid?

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 However, 13,000 of these applications were withdrawn, 8,000 are still active/pending and 3,500 are classed as operational. 2,500 of the projects have complete data on (a) when they applied for permission to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.

Solar surface: silver thrifting?

Ramping new energies is creating bottlenecks in materials. But how much can material use be thrifted away? This 13-page note is a case study of silver intensity in the solar industry, which halved in the past decade, and could halve again. Conclusions matter for solar companies, silver markets, other bottlenecks.

Model of losses in a solar cell: surface, emitter and shading?

This data-file calculates the losses in a solar cell from first principles. Losses on the surface of the cell are typically c4%, due to contact resistance, emitter resistance and shading. Sensitivity analysis suggests there may be future potential to halve silver content in a solar cell from 20g/kW to 10g/kW without materially increasing the losses beyond 4%.

There are three types of surface losses in a solar cell (chart below). Usually, the losses might run to around c4-5%. This is due to resistance in the contact fingers (0.5-1%), resistance in the emitter (1.5-2.5%) and resistance due to shading of the silicon by fingers and busbars (2-3%).

This data-file allows stress-testing of different impacts on solar cell efficiency. You can vary the busbar number (#), busbar spacing (mm), finger length (mm), finger spacing (mm), finger height (μm), finger width (μm), finger resistivity (Ωm), emitter resistivity (Ω/square), open circuit voltage (Voc), current density (mA/mm2). The maths are quite satisfying.

There is no real challenge thrifting silver use in a solar panel by reducing the volume of the contacts. Improved printing techniques may even allow for lower shading losses. More efficient cell designs (e.g., TOPCons) may also allow for slightly higher surface losses.

Lowering silver content from 20 g/kW to 10 g/kW over the next 5-10 years is possible, especially as more efficient new printing technologies are developed. This matters as silver could become a bottleneck in the ascent of solar and face its own challenges in ramping silver mining.

Sensitivity analysis is shown for optimizing finger spacing, narrower fingers, increasing the number of busbars and printing taller-thinner fingers, and how this will all impact losses in a solar cell.

Solar capacity: growth through 2030 and 2050?

Forecasts for future solar growth have an unsatisfyingly uncertain range, varying by 3x. Hence this 15-page note discusses the future of solar. Solar capacity additions likely accelerate 3.5x by 2030 and 5x by 2040. But this creates bottlenecks, including for seven materials; and requires >$1trn pa of additional power grid capex plus $1trn pa of power electronics capex.

Ethylene vinyl acetate: production costs?

Ethylene vinyl acetate is produced by reacting ethylene with vinyl acetate monomer. This data-file estimates ethylene vinyl acetate production costs, with a marginal cost between $1,500-2,000/ton, and a total embedded CO2 intensity of 3.0 tons/ton. EVA comprises around 5% of the mass of a solar panel and could be an important solar bottleneck.

Ethylene vinyl acetate production occurs via the relatively complex pathway shown below. Natural gas liquids are fractionated into ethane. Ethane is cracked into ethylene. Natural gas is also converted into methanol. Then carbonylated with CO to form acetic acid. Acetic acid and ethylene react into vinyl acetate monomer. Ethylene and VAM are then co-polymerized into EVA. Yikes.

The key input that drives production costs of ethylene vinyl acetate is the price of ethylene. Ethylene is used both to make vinyl acetate monomer (reacting with acetic acid, in turn derived from the carbonylation of methanol) and as co-polymer in EVA itself.

As a rule of thumb, a $0.1/gal increase in the price of ethane (from our base case of $0.3/gallon) results in a $100/ton increase in the marginal cost of ethylene, which in turn results in a $100/ton increase in the cost of EVA. Hence there could be a “push” on prices from under-investment in conventional energy or in US shale.

The larger “pull” on prices is likely to come from the rapid scale-up of solar. The EVA market is relatively small, around 4.5MTpa in 2021. Whereas we think the ultimate solar demand for EVA will ramp up to 1.5-2.5MTpa (please see our solar bill of materials).

If EVA becomes a bottleneck, then solar manufacturers will have to outbid other customers for product. Another complexity is that not all EVA is inter-changeable. The solar industry uses a specialized product.

Leading EVA producers include Hanwha-TOTAL, ExxonMobil, Wacker and China’s Lianhong Xinke. Other names are noted in the data-file.

This data-file contains background notes, approximated estimates for ethylene vinyl acetate production costs, and approximated cost estimates for the underlying production of vinyl acetate monomer. We have tabulated capex costs from recent projects to inform our estimates.

Solar: energy payback and embedded energy?

What is the energy payback and embedded energy of solar? We have aggregated the consumption of 10 different materials (in kg/kW) and around 10 other line-items across manufacturing and transportation (in kWh/kW). Our base case estimate is 2.5 MWH/kWe of solar. The average energy payback of solar is 1.5-years. Numbers and sensitivities can be stress-tested in the data-file.

Our base case estimate covers a standard 560W solar panel, as is being manufactured in 2022-23, weighing 30kg, and having an efficiency of 22%.

By mass, this solar panel is about 65% glass, 15% aluminium, c10% polymers (mainly EVA encapsulants and PVF back-sheet), c3% copper. Photovoltaic silicon is only 5% of the panel by mass, but about 40% by embedded energy.

Another 10kg of material is contained in the balance of project, across inverters, wiring, structural supports, other electronics. Thus the energy embedded in manufacturing the panel is likely only around 60% of the total energy embedded in a finished solar project.

Our base case is that it will take around 2.5 MWH of up-front energy and release almost 3 tons of CO2 per kW of installed solar capacity. In turn, this suggests an energy payback of around 1.5-years and a CO2 payback of around 1.8-years.

The complexity of the solar value chain is enormous. Often it is also opaque. Thus the numbers can vary widely. We think there will be solar projects installed with an energy payback around 1-year at best and around 4-years at worst.

Our numbers do not include energy costs of power grid infrastructure or battery back-ups. This is simply a build-up for a vanilla project, trying to be as granular and objective as possible.

Inputs for the embedded energy and CO2 of different materials are drawn from our other CO2 screening work and economic models.

The other great benefit of constructing a detailed bill of materials for a solar installation is that we can use it to inform our solar cost estimates. Our best guess is that materials will comprise around half of the total installed cost of a solar installation in 2021-22 (chart below). There is going to be a truly remarkable pull on some of these materials from scaling up solar capacity additions.

We absolutely want to scale solar in the energy transition. This will be easiest from a position of energy surplus.

Please download the data-file to stress test our numbers around the embedded energy needed to construct a solar project, and the energy payback of solar.

Solar volatility: tell me lies, tell me sweet little lies?

This 20-page note quantifies the statistical distribution of short-term volatility at solar power plants, using second-by-second data, for an entire year. Solar output typically flickers downwards by over 10%, around 100 times per day. We absolutely want to ramp solar in the energy transition. But how can industrial processes truly be ‘powered by solar’? Buffering the volatility creates opportunities for gas and nuclear back-ups, inter-connectors, supercapacitors, smart energy and power electronics?

Solar volatility: second by second output data?

We have aggregated the power output and power drops across an entire year of second-by-second solar data, using a publicly available data-set from the NREL, measured across c15 weather stations, in a 500m x 1,500m array in Hawaii. We want to use this data-site to understand the typical second-by-second volatility of solar output from a solar plant.

Hence, we restricted our analysis to the time between 10am and 4pm each day, when the sun is clearly ‘up’, and to ensure we are not simply measuring the power drops due to the sun setting. Then we aggregated all of the second-by-second volatility and power drops.

The typical second-by-second volatility of solar power is surprisingly high, with around 100 noticeable fluctuations per day on average, ranging from 1 – 10,000 seconds, and varying by 10-90% peak to trough. The main cause is clouds. For example, a typical day of second-by-second solar output is plotted below.

Distribution of solar volatility. Around 50% of the second by second power drops exceed more than 30% of the array’s prior output, and around 10% exceed 70% of the array’s prior output. Around 50% of the power drops exceed 10-seconds, 4% exceed five-minutes, and 0.1% exceed 1-hour.

Distribution across different days. The average day will see 100 power drops, although this varies by day. The best days are totally clear, and see a completely smooth output profile. Top quartile days see <45 power drops. Bottom quartile days see over 135 power drops. And the worst day, frankly, are hellish — they would struggle to power most modern industrial processes, even with the help of batteries.

Definitions and methodology. Specifically, we have defined a “power drop” as a >10% reduction from the trailing 10-minute average solar energy hitting the solar array. The power drop’s duration is defined as the number of seconds until solar output returns to 90% of that previous 10-minute average power output. The average power drop is the average reduction in power output during the duration of the power drop, compared to the prior trailing ten minute average. And the peak drop is the maximum reduction in power output during the power drop, compared to the prior trailing ten minute average.

This data-file contains a read-out of all 35,000 power drops that took place across the NREL measurement site over the course of the year: their average magnitude, duration and when they occurred. It also contains some charts, showing the statistical distribution of the solar volatility. And it contains an overview of the methodology (and VBA scripts) we used to parse 365 x 8MB input data-files from the NREL.

Data source: Sengupta, M.; Andreas, A. (2010). Oahu Solar Measurement Grid (1-Year Archive): 1-Second Solar Irradiance; Oahu, Hawaii (Data); NREL Report No. DA-5500-56506. http://dx.doi.org/10.5439/1052451

Solar volatility: interconnectors versus batteries?

Interconnectors cure renewables volatility

The solar energy reaching a given point on Earth’s surface varies by +/- 6% each year. These annual fluctuations are 96% correlated over tens of miles. And no battery can economically smooth them. Solar heavy grids may thus become prone to unbearable volatility. Our 17-page note outlines this important challenge, and finds that the best solutions are to construct high-voltage interconnectors and keep power grids diversified.

Solar variability: how much does solar energy vary by year?

How much does solar energy vary by year in typical locations? To answer this question, this data-file aggregates the average annual volatility of solar (and wind) resources across ten locations, mainly cities, in the United States.

Specifically, we find that the annual volatility of incoming solar radiation reaching ground level tends to vary by +/- 6% per year in a typical city, is 96% correlated across different locations within that city, and 50-70% correlated with other cities in the same region.

Workings are given in the data-file, while underlying data are from the excellent NREL NSRDB resource.

In order to smooth out annual solar volatility, we think the best options are non-correlated (i.e., diversified) energy sources, such as wind (also modelled), and other energy inputs (nuclear, hydro, gas, etc). For example, see our notes here and here.

Another excellent option is long-distance inter-connect power lines, as there is almost no correlation between the different annual insolation reaching, say, San Francisco and Houston, or New York and Seattle. For example, see our notes here and here.

Data in this file are useful for illustrating these arguments, and answering the question of ‘how much does solar energy vary by year?’. We can also run bespoke modelling for TSE clients using the NSRDB data, in which case, please contact us.

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