Wind and solar: curtailments over time?

Wind and solar curtailments average 5% across different grids that we have evaluated in this data-file, and have generally been rising over time, especially in the last half-decade. The key reason is grid bottlenecks. Grid expansions are crucial for wind and solar to continue expanding.

Curtailments occur when wind and solar are capable of generating electricity, but operators cannot dispatch that electricity into the grid.

This data-file tabulates curtailment rates in California, Australia, the UK, Germany, Spain, Chile and Ireland, averaging c5% in 2022.

The main reason for curtailment is bottlenecks in the grid — i.e., moving renewables from points of generation, to points of unmet demand — rather than renewables having saturated total grid demand.

In a recent research report into the ultimate share of renewables in power grids, we calculated that based on their statistical distributions, solar would only start meeting 100% of a grid’s total demand around 1% of the time when solar was providing c30% of the total grid, while wind would only start meeting 100% of a grid’s total demand around 1% of the time when wind was providing 40% of the total grid (see below). We are not at these levels yet, in the countries in this sample.

The bottleneck is power grids. You might have a 100MW grid, composed of 10 x 10MW inter-connected nodes, and the issue causing curtailment is trying to flow 20MW through a 10MW node.

This confirms that meeting the theoretical potential of renewables (per the note above) requires vast grid expansion, and indeed, countries that have seen YoY reductions in curtailment rates have often achieved this by building new interconnectors.

Second, it gives a new lens on energy storage. Battery deployments can absorb low-cost renewables and prevent curtailment, by circumventing grid bottlenecks, especially for renewables developers who fear bottlenecks in the grid will be persistent.

Power grids: when will wind and solar peak?

Wind and solar peak at 50-55% of power grids, without demand-shifting or storage, before their economics become overwhelmed by curtailment rates and backup costs. More in wind-heavy grids. Less in solar heavy grids. This 12-page note draws conclusions from the statistical distribution of renewables’ generation across 100,000 x 5-minute grid intervals.

California’s grid: wind and solar statistical distributions?

Wind and solar statistical distributions

This data-file aggregates wind and solar statistical distributions, plotting solar generation and wind generation, every 5-minutes, across California, for the entirety of 2022, in order to understand their volatility and curtailment rates. The data suggest that wind and solar will most likely peak at 50-55% of renewables-heavy grids.

How will ramping up wind and solar increase volatility of other generation sources, lower the utilization rates of the overall grid, and where will wind and solar naturally peak?

We can answer these questions by evaluating 100,000 x 5-minute intervals of power generation, from California’s power grid in 2022, then calculating the statistical distributions of wind, solar and total demand.

Our key findings are in our recent research note, assessing where will wind and solar peak? (see below).

As always, we publish the data behind our analysis, to give transparency on the methodology, and assumptions can be stress-tested.

Tabs ending _Averages plot the average load across different five-minute intervals, across the year, showing wind and solar statistical distributions, and how they vary throughout the day, in different months.

Tabs ending _DitsributionFlex plot the distribution of each time series, by percentile, across the entire year.

The analysis then asks: if the future distribution matches today’s distribution, how much of a hypothetical ‘constant’ grid load could wind, solar and wind+solar supply, at different curtailment thresholds.

If we assume that supply will stop growing when marginal capacity additions are likely to incur 30-60% curtailment rates, this suggests that solar would most likely peak out at 35-40% of a grid, wind would most likely peak out at 60-70% of a grid, while a mixture of wind and solar would most likely peak out at 50-55% of a grid.

Please see our broader research into power grids, into California’s grid and for similar statistical analysis of wind generation elsewhere, we have run similar analysis for the UK power grid.

Peak commodities: everything, everywhere, all at once?

Commodities needed for energy transition

This 15-page note evaluates 10 commodity disruptions since the Stone Age. Peak demand for commodities is just possible, in total tonnage terms, as part of the energy transition. But it is historically unprecedented. And our plateau in tonnage terms is a doubling in value terms, a kingmaker for gas and materials. 30 major commodities are reviewed.

Thermal energy storage: heat of the moment?

Thermal energy storage will outcompete other batteries and hydrogen for avoiding renewable curtailments and integrating more solar? Overlooked advantages are discussed in this 21-page report, plus a fast-evolving company landscape. What implications for solar, gas and industrial incumbents?

California power generation over time?

California power generation

California’s power grid ranges from 15-61GW of demand. Utility scale solar has almost quadrupled in the past decade, rising from 5% to almost 20% of the grid. Yet it has not displaced thermal generation, which rose from 28% to 36% of the grid. We even wonder whether wind and solar are entrenching natural gas generators that can backstop their daily, weekly and even seasonal volatility.

This data-file aggregates descriptive statistics into California’s power grid, plotting California power generation over time, looking across 150MB of CAISO data into solar, wind, nuclear, hydro, imports and thermal generation, every five minutes, for almost a decade.

Over the past decade, California’s power grid has averaged 26GW of load, but the demand is highly variable, troughing at 15GW in April-2022 and peaking at over 61GW in July-2021. Summer demand is almost 50% higher than winter demand due to air conditioning.

California power generation
California Power Grid Descriptive Statistics Average Load, Minimum, Maximum and Quartiles

Utility-scale solar is the biggest change in the generation mix, rising from 5% of the grid to almost 20% of the grid over the past decade. Solar generation is volatile. The average load from solar was 4GW in the trailing twelve months, but c40% of the time, solar generation is zero, while 25% of the time it is above 10GW and 5% of the time it is above 13GW.

California power generation
California Utility-Scale Solar Generation Over Time in GW

Seasonality also matters for solar. Solar generates around 2x higher output during the summer months than the winter months. Similar charts for wind are in the data-file.

Thermal generation has not necessarily been displaced by the rise of solar and wind in California’s grid, but arguably, entrenched, as gas-fired power plants can rapidly ramp up production, after the sun sets, or in non-sunny months.

The increasing volatility of California’s power prices rewards natural gas generators, but is not high enough to bring longer-duration battery storage into the money.

Total thermal generation has actually risen from 28% of the grid in 2017 to 36% of the grid in 2023, albeit the picture is somewhat distorted by annual fluctuations in hydro output and the change in imports, tabulated in the data-file.

California power generation
California Thermal Generation Over Time in GW

Is California’s thermal generation base backstopping its renewables? There is also evidence of thermal generation plants being run more flexibly, to ‘backstop’ solar and wind in California’s grid, with rising interquartile ranges, absolute ranges and deviations between upper-decile and lower-decile generation.

California power generation
Thermal Generation Monthly Ranges

Full descriptive statistics are in the data-file, covering all of California’s main generation sources — solar, wind, nuclear, hydro, imports and thermal — and the proportionate share of each one. For each month, we plot each generation source’s minimum output, 10th percentile, lower quartile, median, mean, upper quartile, 90th percentile and maximum output.

Renewable-heavy grids: dividing the pie?

Renewable-heavy grids

The levelized cost of partial electricity (LCOPE) is very different from the levelized cost of total electricity (LCOTE). This 21-page note explores the distinction. It suggests renewables will peak at 30-60% of power grids? And gas is well-placed as a back-up, set to surprise, by entrenching at 30-50% of renewables-heavy grids?

Thermal energy storage: cost model?

This data-file captures the costs of thermal energy storage, buying renewable electricity, heating up a storage media, then releasing the heat for industrial, commercial or residential use. Our base case requires 13.5 c/kWh-th for a 10% IRR, however 5-10 c/kWh-th heat could be achieved with lower capex costs.

Thermal energy storage solutions aim to help integrate solar and wind into power grids, by absorbing excess generation that would otherwise be curtailed, and then re-releasing the heat later when renewables are not generating.

Different storage media are compared in one of the back-up tabs of the model. However, one-third of the companies in our thermal energy storage company screen are pursuing molten salt systems, hence our thermal energy storage model focuses on this option.

In our base case, the cost of thermal energy storage requires a storage spread of 13.5 c/kWh for a 10MW-scale molten salt system to achieve a 10% IRR, off of $350/kWh of capex costs. Costs are sensitive to capex, utilization rates, opex, electricity prices and round trip losses. The sensitivities can be stress tested in the data-file.

Capex costs of thermal energy storage may be reduced below our base case estimate, which has been built-up using the same input assumptions as our broader battery cost models. Larger systems require proportionately more storage material, larger tanks, and more insulation. But other lines in the capex build up do not change, and hence these costs deflate in MWH-terms.

The round-trip efficiency of thermal energy systems can also be higher than we might have naively expected, possibly in the range of 85-95%. The physics is modeled from first principles in other back-up tabs of the data-file. As a generalization, a large and well-insulated thermal energy storage system loses 1-2% of its stored heat over the course of 24-hours.

The full data-file contains the workings behind our recent deep-dive into thermal energy storage. We have also included similar estimates for residential-scale storage, adding an electrically heated hot water tank to absorb excess renewables, which looks simple and can be highly economical. Please download the data-file to stress test all of our numbers.

Renewable grids: solar, wind and grid-scale battery sizing?

Grid-scale battery sizing

How much wind, solar and/or batteries are required to supply a stable power output, 24-hours per day, 7-days per week, or at even longer durations? This data-file stress-tests grid-scale battery sizing, with each 1MW of average load requiring at least 3.5MW of solar and 3.5MW of lithium ion batteries, for a total system cost of at least 18c/kWh.

Start by modelling a power demand curve. Then model how much wind or solar would need to be installed to provide this electricity demand across a comparable timeframe. Then model how big a battery is required to move the renewables to align with the timing of the power demand curve. This data-file works through the maths, for different batteries, including their round trip efficiencies, and their costs.

The minimum possible requirement for a fully solar-powered electricity grid is that each 1MW of load requires 3.5MW of solar modules and 3.5MW of lithium ion batteries with daily charging-discharging, in a location where every day is perfectly sunny, with no clouds, and no seasonality, for a total levelized cost (LCOTE) of 18c/kWh.

Introduce volatility into the weather pattern, and the requirement for a fully solar-powered grid is that each 1MW of average load requires 5MW of solar modules and 9MW of lithium ion batteries with full charging-discharging every 1.5 days on average, and a total levelized cost (LCOTE) of 35 c/kWh. For more detail, please see our data-file into the volatility of solar generation.

Grid-scale battery sizing
Sizing of a solar array and battery module to supply a 100MW load for 30-days including cloudy days

Introduce seasonality in the weather pattern, with 50% lower solar output in winter versus the summer, and the requirement for a fully solar-powered grid is that each 1MW of average load requires 6MW of solar modules and a somewhat insane 235MW of lithium ion batteries with full charging-discharging every 70-days on average, for a total levelized cost (LCOTE) of 800c/kWh. Which is also somewhat insane.

Grid-scale battery sizing
Sizing of a solar array and battery module to supply a 100MW load for 365-days including seasonality

Wind numbers are more demanding than solar numbers, all else equal, because the sun rises and sets daily (helping the utilization rate of the batteries), while wind can incur 2-3 windy days followed by 2-3 non-windy days (hurting the utilization rate of batteries). For more detail, please see our data into the volatility profile of wind generation.

Grid-scale battery sizing
Sizing of a wind farm and battery module to supply a 100MW load for 2-3 weeks including still days

Redox flow batteries are particularly helpful for integrating larger shares of renewables, and are modelled to result in total system costs that are c50% lower than using lithium ion batteries at grid scale. Please see our deep-dive research note into redox flow batteries.

This data-file provides underlying workings into renewable asset sizing, grid-scale battery sizing and total system costs for our recent research into renewables’ true levelized cost of electricity (LCOTE).

Top Public Companies In Energy Transition

Top public companies in energy transition

Some of the top public companies in energy transition are aggregated in this data-file, looking across over 1,000 items of research into the energy transition published to date by Thunder Said Energy.

The data file should be useful for subscription clients of Thunder Said Energy, if you are looking for a helpful summary of all of our research to-date, how it reflects upon public companies, and links to explore those companies in more detail, across our other research.

Specifically, the file allows you to filter different companies according to (a) listing country (b) size — i,e., small-cap, mid-cap, large-cap, mega-cap (c) Sector — e.g., energy, materials, capital goods, OEMs (d) TSE research — and whether the work we had done made us incrementally more optimistic, or cautious, on this company’s role generating economic returns while advancing the energy transition.

A back-up tab then reviews all of our research to date, going back to 2019, and how we think that specific research conclusion might impact upon specific companies. This exercise is not entirely perfect, due to the large number of themes, criss-crossing a large number of companies, at a large number of different points in time. Hence the observations in this data-file should not be interpreted as investment recommendations.

The screen is updated monthly. At the latest update, in January-2023, it contains 324 differentiated views on 162 top public companies in energy transition.

Copyright: Thunder Said Energy, 2019-2023.