In this note we take a look at the impact the federal ALP’s target of 1 million batteries by 2025 might have – if realised. “Impact” in this case means the impact on pool prices, investment in other forms of dispatchable generation.
The key insights in this note are:
It’s a lot of fun writing about electricity and an opportunity for me to use more than 30 years of investment bank training and experience to put something back into the community.
I couldn’t even start on what I do without the support of Global Roam and NEM Review and its CEO Paul McCardle. If you want professional data, get it from the professionals. (They supply our popular NEMWatch widget).
Electricity and decarbonisation are a vitally important topic on many levels. Coal is Australia’s biggest export and yet it has to go away. Don’t start me on global oil. Ten to twelve years ago it was hard to see see viable alternatives but these days we can imagine a coal, oil and gas-free future.
Still, the transition costs are enormous. This will be one of the biggest global economic pivots ever and clearly is going to take decades notwithstanding the ever increasing hurry.
If there is one area of cross disciplinary science that needs urgent work it’s showing the economic impacts of global warming more clearly. We all know the world is warming, but let’s show clearly to people on the streets and to their elected representatives that the costs of this far exceed the costs of transitioning out of fossil fuels.
See you on the flip side of the holiday season. But first:
Our quick summary of the available household battery incentive programs today is as follows:
The system wide peak power requires an assumption about who controls and dispatches batteries. As outlined in the AEMO 2018 statement of opportunities, if households run batteries for their own use, they won’t care about the system and are unlikely to consume more than about 2kW out of the battery even during the evening peak.
On the other hand, if big brother controls the battery it will likely be run at peak power (say 5kW) during the evening peak. The battery will only last 90 minutes that way. Figure 1 assumes 45% big brother and 55% household control.
Probably a more realistic assumption is that only 10% of battery use will be system controlled without a lot of legislative push.
The consumer is unlikely to cede control willingly. Or to put it another way, the grid arbitrage (that is not paying wires and poles consumption charges) that effectively represents about 50% of the avoided cost a household battery gets won’t on average be recouped by wholesale revenues
The most comprehensive source of information on household batteries is the www.solarquotes.com.au database maintained by Finn Peacock’s team. Gold star award Finn.
The focus at SolarQuotes is on the consumer, but we use the data base to look at cruder metrics such as the usable capacity and the rated peak power. Some systems have additional installation costs, some require other boxes but averages can cover many sins.
We use 8kWh. We think right now sales are biased to higher capacity systems but there are some reasons to think size may come down, eg impact of income limits on subsidy availability. Equally if batteries get cheaper as we hope and project size may increase. The implications of our note will be stronger if average size is say 10-13 KWh
Let’s assume that the Federal ALP’s target of 1 million households is achieved. We also assume that the average battery size and power are unchanged. How will battery prices move?
We expect installation costs to fall. Australia’s behind the meter installation industry is currently the best in the world as measured by cost. Way better than the USA.
Battery prices will depend though on the global production rate. The available data suggests a learning rate of about 15%.
However, household batteries are a low priority in the global battery market. Car batteries are the big deal. One car = 5 houses. All over the world electric vehicles are quite the thing. Not only that but there are good arguments to suggest that household batteries ideally have a different chemistry to electric car batteries.
So on balance we expect that for the time being household batteries in Australia will fall in price say 7% per year.
In the usual, easy to doodle, but hard to invest on fashion we project a pathway to 1 million households. We hold the unit size and power of the battery constant. An infinite number of other assumptions are possible up to a point, but obviously it’s going to take a lot of growth to get to 1 million when the starting point is 20,000 installations per year.
In our model the total investment, paid for largely by consumers is $8 billion and 8 GWh of storage are available. Even if none of the batteries were configured for big brother use they would still represent around 2.2 GW of power, or about the same as Snowy 2.0. The 2.2 GW can be delivered for 3.5 hours on our assumptions, but Snowy 2 can run for 175 hours.
If the batteries could all be configured for big brother purposes there is 5.5 GW of power for 90 minutes.
The $8 billion compares to $6 billion Snowy scheme including required transmission. However, we think most of the transmission will be required anyway so we prefer to see Snowy as a $4 billion – $5 billion project.
They would both be delivered at the same time but have different purposes, which we will get to.
It’s important to emphasize that the battery lifetime, although unknown, will be way less than that of Snowy, but over time it’s easy to see battery life being extended by simply replacing dud cells.
One million household batteries would significantly reduce average utilization of the wires and poles network.
Under the current revenue model where networks are entitled to recover a return on assets and their actual quantity of revenue is fixed the only way this can be achieved is via an increase in either the fixed costs of being connected to the grid (very regressive pricing method), or higher usage charges – most of which would fall on those people that don’t have batteries but provide an incentive for them to get one.
However, certainly network augmentation requirements would fall still further and all this talk about voltage increases from too much rooftop solar would turn out to be just talk.
These are all important points each of which could justify a small book but they are not what I wanted to talk about in this article.
We allocated batteries based on number of households. We don’t show W.A although its allowed for. One million batteries is about a 10% penetration rate. Of course, the total number of households will likely be 10% higher in 2025 but this is ignored here and doesn’t impact the results.
Allowing for 0.5% per year growth in total demand and making ITK assumptions for the growth of behind the meter solar output to 2025 we project in front of the meter demand for three main regions (and I have for want of another pair of hands not included Tasmania) as follows:
In this chart note that QLD demand ex rooftop is flat in the middle of the day, using rooftop solar to recharge batteries may increase midday demand.
Its also of great interest to see that in Australia it’s the morning peak which is steepest.
The battery discharge profile we adopt is slightly scaled up from that presented by AEMO in its 2018 Statement of opportunities. This is for a non “big brother” household
We have approximately scaled up that charge discharge profile based on the total installed capacity by State estimated in Fig 4 and get the following data:
Had we used 13 KWh as AEMO assumes, rather than the 8 KWh that we assume to become the dominant size then you could pretty much double the impact.
On our numbers the impacts on demand are modest. We don’t see more than a 5% change for any half hour in any State. As I say if the batteries are bigger this would change. Vic/SA is the most impacted.
The really interesting, and I guess disappointing point is that if the batteries do have a charge/discharge profile as suggested they will make the morning peak worse not better. That’s because the batteries will reduce already low minimum demand in the morning.
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