A near 100% renewables grid is feasible.

From RenewEconomy

(I talked about this analyst's simulations here and here)

There have been many simulations of a 100% renewable electricity grid for Australia, including some ground-breaking studies from Beyond Zero Emissions, The University of New South Wales and the ANU.

Even the recently released Integrated System Plan from the Australian Energy Market Operator exceeds 97% renewable in the 2040s.

So, what is the point of another one?

Well, this simulation differs from the others in a couple of ways:It uses actual generation and demand data rather than relying on synthetic traces for those quantities
It is being conducted in near real-time

The benefit of using actual generation and demand data is that some people are sceptical of synthetic wind and solar traces. They may also be dubious when you start modifying demand.

The benefit of the near real-time modelling is that people tend to be more concerned about recent events. If a recent day had very little wind and solar generation, some will take that as proof that you cannot run an electricity grid on renewables. A study based on data from a few years ago is unlikely to change their minds.

Another aspect of near real-time modelling is that it is one thing to optimise a simulation when you have all the data in advance, it is another when you design the simulation before you get the data.

With that in mind, exactly one year ago I started running a simple simulation of Australia’s main electricity grid to show that it can get very close to 100% renewable electricity with approximately five hours of storage (24GW/120GWh).

Each week, I would download demand and generation data from OpenNEM. I left demand unchanged.

The generation data for wind, rooftop and utility solar data was rescaled to supply ~60%, 25% and 20% of demand respectively over the year. For example, over the last year utility solar generation has met 5% of demand. The target for utility solar was 20%, so I rescaled the last 7 days of utility solar data by 4x (ie, 20% divided by 5%).

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 with significant shortfalls during some months requiring huge amounts of storage or backup. As will be seen later in this article, this simulation ended up having 18% excess generation over the year.

The decision to use 60% wind, 45% solar was based on rough optimisation experiments. A mixture reasonably close to 50:50 takes advantage of the fact that 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. I have a bias to wind as it requires less short-term storage, which is used primarily to shift solar generation from the day to the evening and night.

My simulation used the 24GW/120GWh of assumed storage and existing hydro to firm up the wind and solar and match 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 on days 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’ was deliberately left undefined. It could be gas generation. Indeed, in the short to medium term it is likely to be existing gas 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 biofuels or green hydrogen, or it could be long-term storage such as Snowy 2.0. When calculating the renewable percentage of the simulation, I have assumed ‘other’ is not renewable, even though it is hoped that in the future ‘other’ will become renewable.

Each week I posted the results of the simulation of the previous seven days to my Twitter account. On Wednesday of this week, I posted the 52nd week, marking a full year of simulations.

I’ve copied the simulation below. It is fitting that the renewable penetration of 99% for the final week of the simulation very closely matched the renewable penetration over the entire 52-week period, 98.8%

Key results from the 52 weeks of simulations are summarised as follows:

• Renewables met 98.8% of demand over the year, with the remaining 1.2% met by ‘Other’
• ‘Other’ generation peaked at 6.59 GW on the night of July 12. Over the year its average capacity factor was 4.3%.
• Hydro met 6.9% of demand. This was lower than my target of 7.5%, and also less than actual hydro generation of 8%. This means that dam storage levels in my simulation would have ended the year higher than they did in the real world.
• 17% of the wind and solar generation was in excess of requirements and ended up being curtailed.
• 11% of wind and solar generation went into storage. Storage discharge met 10% of demand.
• 82% of demand was directly powered by wind and solar without having to pass through storage or be curtailed

The wind and solar generation ended up slightly exceeding the targets of 60%, 20% and 25% for wind, utility solar and rooftop solar respectively.

It is impossible to know in advance if the year would be above or below average, so it is not surprising that they did not exactly hit their target. However, the methodology used to rescale the wind and solar data meant that there was a high probability that they would exceed their targets.

The graph above shows the weekly fraction of demand that was met by ‘Other’. Levels of ‘Other’ were essentially zero for almost seven months from September to late March. However, by late April, the simulation started to become more ‘interesting’.

Most weeks from late April through to the present required some levels of ‘Other’, due to the inability of wind, solar, storage and hydro to entirely meet demand throughout the week. The week starting on June 29 proved to be the most difficult week of the simulation, with ‘Other’ having to provide 8.1% of demand that week.

The graph illustrates clearly that late autumn and winter will prove to be the most challenging periods for a mostly renewable grid in Australia. Solar generation in late June and early August can often be as low half the annual average.

And while wind tends to be above average during winter, there are often stretches of two or three days in a row that have significantly below average wind. This can leave a significant shortfall in generation that cannot be entirely filled by existing hydro.

The challenge of matching supply and demand during winter will be even more difficult as we start to electrify much more of the gas heating that is present in the southern states, particularly Victoria. Doing so will elevate winter demand much more than summer demand.

It is important to note that wind in Queensland is not well correlated with wind in the southern states. That means that when it is calm in South Australia, Victoria, Tasmania and NSW, it is often windier than average in Queensland. For this reason, it is unfortunate that wind only makes up 3% of Queensland demand, or about one-quarter of the NEM average of 12%.

More wind in QLD will greatly help to improve the geographic diversity of renewable generation, making it easier to match supply and demand over the year. However, it will not completely solve the problem. There will remain many days with poor renewable supply in both the southern states and in Queensland. Increases in Queensland wind generation will make it easier to get closer to 100% renewable electricity, but is unlikely to significantly reduce the peak requirements of ‘Other’.

It is interesting to note that the ISP is predicting that approximately 9GW of peaking gas or liquids will need to be retained in the NEM’s generation mix out to 2050. This is more than the 6.6GW required so far in this study.

However, the ISP is a much more sophisticated model than the simulation I have done here, with increased demand due to increased electrification. It has modelled many years of generation, ensuring that supply stays secure and reliable. It is quite likely that some winters may prove more challenging in a high renewable world than the winter of 2022 simulated here.

For countries without hydro, the results of this simulation for Australia suggest that to cover winter demand, "other" (natural gas, for now; SNG later) would need to be ±15% of the generation mix.  Of course, this would only be for part of the year.  The average over the year would still be modest, so even if we have to use gas, we would still cut emissions substantially.  But the simulation highlights the need for gas peaking/back-up.  If we use surplus green electricity to create synthetic natural gas (SNG) then we could in principle run a 100% green grid.