This data-file models the economics of electric vehicle chargers. First, we disaggregate costs of different charger types across materials, electronic components, labor, permitting, fees, opex and maintenance (below).
Next we model what fees need to be charged by the charging stations (in c/kWh) in order to earn 10% IRRs.
Economics are most favorable where they can lead to incremental retail purchases and for larger, faster chargers.
Economics are least favorable around multi-family apartments, charging at work and for slower charging speeds.
This data-file contains the output from some enormous data-pulls, evaluating UK grid power generation by source, its volatility, and the relationship to hourly traded power prices. We conclude the grid is growing more expensive and volatile.
Different tabs in the data-file cover the total monthly demand of the UK power grid since 2016, broken down by generation source, month-by-month and smoothed over trailing twelve-month timeframes; statistical analysis of hourly power prices, by day and by quarter; and an hourly cross-correlation of wind generation with power prices (chart below).
We have recently updated the data-file to capture the extreme price spikes and volatility seen in 3Q2021.
The data-file is also regularly updated and we are happy to run bespoke analysis on the underlying data-sets for TSE clients.
This data-file captures the economics of geothermal heat and power, built up as a function of drilling costs, pumping costs and power-cycle costs.
Our base case numbers are calculated both for geothermal hotspots and for the exciting, next-generation technology of deep geothermal power. You can stress test input assumptions in cells H6:H25 of each model.
Further industry data follow in the subsequent half-dozen tabs, including a breakdown of capacity by country and by supplier, patent filings, leading companies and our notes from technical papers.
Smart energy systems are capable of transmitting and receiving real-time data and instructions. They open up new ways of optimizing energy efficiency, peak demand, appliances and costs. Over 100M smart meters and thermostats had been installed in the United States (including at c90M residences) and 250M have been installed in Europe by 2020.
The purpose of this data-file is to profile c40 companies commercializing opportunities in smart energy monitoring, smart metering and smart thermostats. The majority are privately owned, at the venture or growth stage. We also tabulate their patent filings.
We find most of the offerings will lower end energy demand (by an average of 7%), assist with smoothing grid-volatility, provide appliance-by-appliance demand disaggregations and encourage consumers to upgrade inefficient or potentially even defective appliances. Numbers are tabulated in the data-file to quantify each of these effects.
Further research. Our recent commentary that summarises the key points on Smart energy systems is linked here. Our outlook on the most conductive metals used in the energy transition is linked here.
This data-file tabulates the power generation profiles of 3,000 US natural gas-fired power plants, which have reported data to the US EIA, aggregated using in-house web-scraping software.
Unlike wind and solar assets, which exhibit clear decline rates of 1.5% and 2.5% per year, natural gas assets run at c44% of their peak utilization rates on average, which does not change materially over time, flexing within an interquartile range that spans from 14% to 74%.
In other words, gas power plants provide flexibility and long-term reliability in a grid, as they are dialled up and dialled down over time to meet demand. This is also illustrated by looking at the underlying data of individual power plants in the file (chart below).
The data-file also presents a cautionary tale from California. To accomodate 40TWH of new utility-scale renewables generation, we show that 35TWH of gas generation has now been permanently shuttered and another 11TWH has been idled. These closures are equivalent to 30% of California’s baseload and 17% of its peakload power capacity, providing one explanation for the State’s recent rolling black-outs. Full details are split out in the data-file.
We have compiled a database of 25 leading companies in Redox Flow Batteries, starting by looking across 1,237 patents filed about the technology since 2017 (all patents are summarized in the second tab of the data-file).
For each company, we summarize its technology, its recent projects news, its size, its location and whether it is public/private. Covered companies range from public Asian conglomerates to public/private redox flow pure-plays.
Good progress is visible from Redox Flow batteries, rapidly progressing toward technical maturity, constructing demonstration facilities and offering ultra long-life battery storage, which could greatly surpass lithium ion economics in grid applications.
A key challenge is the round-trip efficiency of redox flow batteries. For example, in other work, we have reviewed the patents from companies such as ESS, which most likely have a full-cycle round-trip efficiency of around 70-75%, on an AC-AC basis.
Capex costs may also be higher for some redox flow batteries, compared to deflating costs of lithium ion batteries. Another of our data-files tabulates the costs and operating parameters of different battery systems. All of our workd to-date into different battery systems is covered under our battery research tag.
Our three key points on the leading companies in Redox Flow Batteries are highlighted in the article sent out to our distribution list.
Molten Carbonate Fuel Cells could be extremely promising, generating electrical power from natural gas as an input, while also capturing CO2 from industrial flue gases through an electrochemical process.
We model competitive economics can be achieved, under our base case assumptions, making it possible to retrofit units next to carbon-intensive industrial facilities, while also helping to power them.
Our full model runs off 18 input variables, which you can flex, to stress test your own assumptions.
This model shows the full-cycle cost of storing a kWh of electricity, across ten different technologies that have been proposed to backstop renewables.
The model allows you to flex input assumptions, such as the costs of each battery, its useful life and the frequency of charge/discharge cycles.
Pumped storage currently screens as most economical for backstopping renewables, by a factor of 3x, under our base case assumptions.
Backstopping solar also looks about 3x easier than backstopping wind, as smaller batteries are needed, and costs are a function of system size. But no battery can truly backstop renewables.
Covered storage technologies include pumped storage, compressed air storage, lithium ion batteries, redox flow batteries, four other battery types, flywheels and ultra-capacitors.
This data-file models the economics of constructing a new fuel-cell power plant; generating electricity from grey, blue or green hydrogen in a PEMFC, or from natural gas in an SOFC. The work is based on technical papers and past projects around the industry.
A dozen input variables can be flexed in the model, to stress test economic sensitivity to: hydrogen prices, power prices, carbon price, distribution costs, conversion efficiency, capex costs, opex costs, utilization and tax rates.
Indicative inputs, and sensible ranges, are suggested for each of these input variables in the data-file.
Economics continue to look more challenged for hydrogen power, compared with simply decarbonizing or carbon offsetting natural gas power. Economics are closest to commercialist for gas-fired SOFCs, and could be interesting with c50% deflation and greater reliability, particularly as renewables get overbuild.
This data-file tabulates the impacts of variable electricity tariffs, after switching 4.622 households over from fixed electricity tariffs, across a large-scale sample in the United States. This theme is increasingly important as intermittent renewables reach in developed world power grids (note here).
Residential electricity demand is inelastic, with a 20% price-increase yielding a mere 1% reduction in end-demand. Peakload demand fell by 4%.
However, socially “vulnerable” consumers suffered disproportionately, only achieving a 2% decrease in peakload demand. Hence, while monthly power prices rose by 18% for non-vulnerable consumers, they rose by 22% for vulnerable consumers. The results, data and study are in the data-file.
