I've talked about this simulation by David Osmond before.
From RenewEconomy
Three years ago, on August, 25 2021, I started running a weekly simulation of Australia’s main electricity grid. The intention was to show that it is possible to get close to 100% renewable electricity with just 24 GW / 120 GWh of storage, enough storage to supply average demand for 5-hours.
Each week, the simulation uses actual demand data from the previous week, with no modifications. It also uses actual wind and solar generation data, but they have been rescaled so that they provide a little over 60% and 45% of annual demand respectively.
The simulation uses the 120 GWh of storage and existing hydro to match supply and demand. If these are insufficient, then it uses something defined as ‘Other’, likely to be gas or diesel peaking generators in the short to medium term, though longer term there are clean options to replace these fossil fuels.
The following summarises some of the key results from the 3 years of simulations
– The simulation has averaged 98.4% renewable electricity. The remaining 1.6% is met by ‘Other.’
– Average supply from ‘Other’ was 373 MW but reached a maximum value of 9,043 MW on 4th July 2020.
– The simulation tends to be 100% renewable from September to March each year, but from mid-Autumn till the end of winter (April to August), it needs supplementation from ‘Other’ on a regular basis.
– The most challenging week was the week starting 12th June 2024, when ‘Other’ was required to supply 18.6% of demand. 2024 proved to be a far more challenging year than the previous 2 years, due to a sustained wind drought from early May till late June.
– 16% of the wind and solar generation was in excess of requirements and ended up being curtailed.– Only 10% of supply had to pass through storage. Wind and solar directly supplied 82% of demand without having to pass through storage or be curtailed. Another 7% came directly from hydro.
My simulation was based on the Australia’s National Electricity Market (the NEM), which represents approximately 80% of Australia’s electricity demand.
Each week, I would download demand and generation data from OpenNEM. I left demand unchanged. I rescaled generation data for wind, rooftop and utility solar by the factors that would get them to 60%, 25% and 20% of annual demand over the previous year respectively.
For example, over the last year rooftop PV generation has met 11.5% of NEM demand. The target for rooftop PV was 25%, so I rescaled the last 7 days of data by 2.2x (25% divided by 11.5%). The scale factors slowly reduce each week as more wind and solar is installed on the NEM.
Note that the sum of 60%, 25% and 20% is greater than 100%. This is important. Any optimised model of a highly renewable grid will have significant amounts of over-generation. It is better to over-generate and have some curtailment than to generate exactly what you need over the year, but with significant shortfalls during some periods which would require huge amounts of storage or fossil fuel backup. As will be seen later in this article the simulation ended up having 16% excess generation (or spilled energy) over the year.
The decision to use 60% wind and 45% solar was based on rough optimisation experiments that I did several years ago. A mixture reasonably close to 50:50 takes advantage of the fact that NEM wind and solar are negatively correlated with each other. Wind tends to generate above average during the night and during winter, complementing the solar generation.
My model has a bias to wind because wind requires less short-term storage, which is used primarily to shift solar generation from the day to the evening and night. Less storage helps keep the predicted cost down. Since I did those optimisation studies, the cost of solar and batteries has reduced far more than the cost of wind. Despite that, the 60:45 wind to solar ratio remains very close to optimal as I’ll discuss later.
The simulation is simple, particularly due to it not considering transmission constraints. That makes it overly optimistic, though in other ways it is conservative. Despite the lack of transmission constraints, it arrives at a similar result to AEMO’s 2024 Integrated System Plan (ISP).
The ISP averages 98% renewable in the 2nd half of the 2040s using approximately 640 GWh of storage. The ISP predicts demand to have doubled by this time. Thus, 640 GWh of storage is sufficient to provide average demand for 13 hours. Most of that storage comes from Snowy 2.0, which only provides a little over 1% of demand. If you remove the effect of Snowy2.0, then the ISP would achieve 97% renewable supply with under 6 hours of storage.
My simulation used 24 GW / 120 GWh of storage (5 hours at average demand) and existing hydro to firm up the wind and solar in order to match supply with demand. Both the hydro and storage were assumed highly flexible.
Note that I did not use the actual hydro generation data. I completely changed the dispatch of hydro so that it had minimal generation during periods when it wasn’t needed, and elevated levels whenever there was a day with significant shortfalls of wind and solar relative to demand. This is reasonable as most of the hydro capacity on the NEM is associated with large storage dams, making the hydro highly dispatchable. However, to maintain consistency with historical generation, hydro generation was also subject to the following constraints:
– Hydro generation was kept between 200 MW and 6,000 MW
– Weekly hydro generation was kept above 168 GWh
– Annual hydro generation was targeted at between 6% and 9% of demand, though ideally closer to 15,000 GWh, or about 7.5% of demand.
If the wind, solar, storage and hydro was unable to meet demand, then the model supplements generation with ‘Other’. ‘Other’ is deliberately left undefined. In the short to medium term, it is likely to be existing gas or diesel peakers that will help firm renewables along with storage and hydro.
But longer term, ‘Other’ could be a highly flexible dispatchable generator running on renewable fuels such as synthetic gas, biofuels or green hydrogen, or it could be long-term storage such as Snowy 2.0, iron air batteries or compressed air storage. When calculating the renewable percentage of the simulation, I have assumed ‘Other’ is not renewable, even though it is hoped that in the future it will become renewable.
[Read the rest of the article here]
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