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.

Power transmission: inter-connectors smooth solar volatility?

Can large-scale power transmission smooth renewables’ volatility? To answer this question, this horrible 18MB data-file aggregates 20-years of hour-by-hour solar insolation arriving at four cities in the US (Los Angeles, San Francisco, Phoenix and Houston). This is our starting point to assess volatility and intermittency.

For example, San Francisco receives an average annual insolation of 2,100 kWh/m2/year, however the hour-by-hour standard error is 141% of the hourly average, day-by-day standard error is 50% of the daily average, month-by-month volatility is 30% of the monthly average and year-by-year volatility is 9% of the annual average. Solar insolation is volatile.

It would be helpful for a stable grid to smooth each of these different time-periods of volatility. Hence the data-file models the impact of constructing large inter-connector transmission lines. The model uses a very simple rule: minimize the difference in solar output in the two inter-connected regions. For example if Region A has 10GW of output and region B has 5 GW (e.g., because it is cloudy that day), you could export 2.5 GW from Region A to Region B, and both would now have 7.5 GW.

Can power transmission smooth renewables volatility? Inter-connectors can have a phenomenal impact. Returning to our example of San Francisco, we find that a 2.5 GW inter-connector between 10 GW solar hubs in both San Francisco and Phoenix would smooth San Francisco’s volatility considerably: With the inter-connector, the hour-by-hour standard error is 124% of the hourly average level (down from 141%), day-by-day standard error is 36% of the daily average (down from 50%), month-by-month volatility is 24% of the monthly average (down from 30%) and year-by-year volatility is 4.4% of the annual average (down from 9%).

This is interesting and helpful because we think batteries may find it harder to smooth year-by-year volatility (it requires an enormous battery that only gets to discharge once every 2+ years). These arguments are laid out in our 17-page research report here. Workings are in the different tabs of the data-model linked below, including scripts we have used to manage the gargantuan data-sets, and apologies in advance for the large file size.

Power grids: global investment?

This simple model integrates estimates the global investment in power grids that will be needed in the energy transition, as a function of simple input variables that can be stress-tested: such as total global electricity growth, the acceleration of renewables, and the associated build-out of batteries, EV charging, long-distance inter-connectors and grid-connected capital equipment for synthetic inertia and reactive power compensation.

Global investment into power networks averaged $280bn per annum in 2015-20, of which two-thirds was for distribution and one-third was for transmission. This is a good baseline.

Our base case outloook in the energy transition would see total global investment in power grids stepping up to $400bn in 2025, $600bn in 2030, $750bn in 2035 and $1trn pa in the 2040s.

Our scenario is also not particularly aggressive around renewables, which are seen accelerating by 10x to provide around 20-25% of all global energy in 2050. You can realistically reach $2trn pa of global power network investment in a scenario that relies more heavily upon renewables and batteries.

Amazingly, these numbers can actually become larger than the total spending on producing all global primary energy. Whereas in the past, transmission and distribution were a kind of side-show, equivalent to c30% of total primary energy investment, the energy transition could see them become comparable, at 50-100%.

Definitions. By ‘power networks’ we are referring to the grid, which moves electrical energy from producers to consumers. Please note that our classification of power grids excludes (a) investments in primary energy production, such as renewables, nuclear, and hydro (b) investments in large conventional power-generating plants (c) downstream investments made by customers, such as in switchgear, power electronics and amperage upgrades.

The model can be downloaded to stress-test simple numbers, inputs and outputs. Please contact us know if the work provokes any questions, or further numbers that we can heplfully pull together for TSE clients.

Nostromo: thermal energy storage breakthrough?

Nostromo technology review

Nostromo technology review. Nostromo is a public company, founded in 2016, with c40 employees in Israel and California. Website here. It is commercializing a thermal energy storage system, which integrates with AC, to store coolness (e.g., during peak wind/solar generation), then re-release the coolness at ‘peakload’, (e.g., in mid-late afternoon, or after sunset).

The flagship product is called ‘IceBrick’, a modular, water-based energy storage cell, which can be retro-fitted onto most commercial buildings in about 4-6 months. It claims 86-92% round-trip energy efficiency, 94% depth of discharge over 4-hours and <1% degradation over 20-years.

We have reviewed the company’s patents on our usual patent framework. Nostromo’s patent library is concentrated, but it scores highly on our framework, as it lays out specific challenges that have hampered other designs, very specific details on how Nostromo’s system improves efficiency and consistency, and the patent library is also easy-to-understand, focused and considers deployments.

The technology is an exciting alternative and complement to lithium ion batteries for energy storage, or more specifically demand shifting. It may be particularly well-suited to commercial buildings in hot climates, where AC can comprise 50% of peakload power generation, per our note here. The main challenge is system costs, explored in the data-file and compared with lithium cells and lithium battery storage. Finally, we think there may be other applications of phase change materials that do not simply accomplish energy storage, but also reductions in total energy consumption (note here).

Full details of our Nostromo technology review can be downloaded in our data-file below.

Further conclusions are linked in the recent article sent out to our distribution list, here.

Electrical conductivity: energy transition materials?

Electrical conductivity energy transition materials

The electrical conductivity of energy transition materials is tabulated in this data-file, intended as a useful reference.

Electricity conductivity is simply the inverse of electrical resistivity, measured in Ohm-meters, and varying from 10^-30 in super-conducting materials through to 10^20 at the most highly insulating plastics as might feature in HVDC power cables.

Most of ‘the action’ in energy transition will take place in the range of 10^-8 to 10^-3 Ohm-meters.

Silver is the most conductive metal used in the energy transition, which combined with its high stability, to make it the most commonly used front contact material in solar cells, which in turn consume around 10% of global silver production today.

Copper is used in machinery and appliances. As a rule of thumb, wind, solar and EVs are around 4x more copper intensive than the conventional generators and ICE vehicles they replace. Hence we see copper demand trebling in the energy transition.

Aluminium is c50% more resistive than copper, but it is also 70% lighter and stronger, which explains why it is used in overhead transmission lines or in rigid conductors behind the back contact of solar panels.

Battery metals are 5-10x more resistive, and graphite is 200x more resistive, than the excellent conductors discussed above, because they are primarily selected for their ability to intercalate lithium ions and promote battery energy density.

Electrical grade steel is another 3x more resistive again versus battery metals. Electrical grade steel is used in electric motors, transformers and generators, in order to create electro-magnetic fields.

Finally, PV silicon is a semi-conductor, around 10,000 more resistive than conductive metals, because in order to conduct electricity, it requires electrons to be promoted from their valence bands to their conductance bands. Conductivity depends on doping levels (silicon metal hardly conducts at all) and it is higher for N-type silicon the P-type silicon.

We will continue building out this data-file, into electrical conductivity of energy transition materials, so please email if there are any materials that we can helpfully add, or model more fully.

To read more about The electrical conductivity of energy transition materials, please see our article here.

FACTS of life: upside for STATCOMs & SVCs?

Upside for STATCOMs

Wind and solar have so far leaned upon conventional power grids. But larger deployments will increasingly need to produce their own reactive power; controllably, dynamically. Demand for STATCOMs & SVCs may thus rise 30x, to over $25-50bn pa. This 20-page note outlines the opportunity and who benefits?

FACTS: costs of STATCOMs and SVCs?

Costs of Statcoms and SVCs

This data-file aggregates the costs of STATCOMs and SVCs. At the risk of drowning you in acronyms, these are all FACTS, Flexible Alternating Current Transmission System components. STATCOMs are Static Synchronous Compensators. SVCs are Static VAR Compensators.

These components are used to stabilize power grids, by rapidly controlling reactive power flows. Although their properties differ, including their response latency and their capacity of active power filtering. Case studies are reviewed in the data-file.

Based on aggregating cost data from 25 prior projects, we think typical costs for SVCs are around $100/kVAR, while STATCOMs are above $150/kVAR. This makes them more expensive than capacitor banks but more expensive than synchronous condensers.

FACTS sizing. We have also compiled data into the typical size of FACTS systems that are directly associated with large wind power projects: each MW of real power capacity is likely to be backstopped with 0.5-1.0 MVAR of reactive power capacity via STATCOMs, SVCs and associated filters.

Case studies are also covered in the data-file, based on company reports and technical papers. For example, the 1.2 GW Hornsea ONE wind project in the UK North Sea, completed in 2020, contains 3 x offshore shunt reactors sized at 85-135MVAR, 3 x onshore variable shunt reactors sized at 120-300MVAR, 3 x C-type harmonic filters each rated to 100MVAR to dampen low-frequency resonance, 3 x 200MVAR STATCOMs and two high-pass C-type harmonic filters rated at 75MVAR and connected to the 400kV busbars onshore to dampen high frequency harmonics. All of this illustrates that modern wind projects are using larger and increasingly sophisticated power electronics, where smaller and earlier projects might have simply leaned on the existing grid.

Recent commentaryplease see our article here.

Capacitor banks: raising power factors?

Wind and solar power factor corrections

Power factor corrections could save 0.5% of global electricity, with $20/ton CO2 abatement costs at typical facilities in normal times, and 30% pure IRRs during energy shortages. They will also be needed to integrate more new energies into power grids. This 17-page note outlines the opportunity in capacitor banks, their economics and leading companies.

Capacitor banks: the economics?

This model captures the economics of power factor correction via installing capacitor banks upstream of inductive loads.

Specifically, these capacitors prevent power drops and unnecessary I2R losses by keeping voltage in phase with current, even when power is supplied to components such as motors, electric arc furnaces, LED lights, computing infrastructure.

In our base case scenario, a 10% IRR is derived from a capacitor bank costing $30/kVAR, reducing real power losses by 0.5%, and thus earning its keep through a combination of 8c/kWh electricity prices (75% of savings), $3.5/kW demand charges (15%) and a $20/ton CO2 price (10%).

Therefore rising power prices, demand charges and CO2 prices would all support greater deployment of capacitor banks.

Please download the data-file to stress test the economics….

Recent commentary: please see our report into capacitor banks.

Copyright: Thunder Said Energy, 2022.