I was puzzled late last year when a bit of hype emerged about the performance of South Australia’s “Big Battery” (Hornsdale Power Reserve) following the trip of a large coal-fired unit in Victoria early on the morning of December 14th.

An article appeared on the Reneweconomy energy news site which might have been read as crediting the “millisecond response” of the battery for saving the grid from some sort of catastrophe, and contrasting this with the slower response of “the generator contracted at that time to provide FCAS (frequency control and ancillary services), the Gladstone coal generator in Queensland”.

A few things here just didn’t sound right. For a start, at any one instant there are always many generators enabled in the NEM to provide FCAS – services which come in eight different flavours, from second-to-second frequency regulation, through to delayed-reaction contingency response over timeframes of up to five minutes, as explained in Jonathon Dyson’s excellent WattClarity articles on FCAS. So focusing on what Gladstone did or didn’t do clearly couldn’t tell the whole story. And whatever the speed of HPR’s response, being a much smaller facility (100 MW) than the tripped Loy Yang A unit 3 (560 MW) it was never going to be able to stabilise system frequency all by itself. That requires replacing essentially all the tripped unit’s lost power output with increased output from other sources.

At the time I was busy with other stuff and thought I’d go back and have a closer look a little later. Or a lot later as it turns out.

A chart in the original Reneweconomy article showed the timing of events with the Loy Yang A unit’s output falling away, and a sudden injection of power from the battery (HPR):

Source: Reneweconomy, 19 Dec 2017

Looks impressive, but a closer glance at the scaling of the chart shows that the maximum injection of power from HPR was less than one sixtieth of what was lost when Loy Yang A3 tripped. If we use a common scale for both facilities then the picture is a little different:

But for at least some Reneweconomy readers– see the voluminous comments thread under the article – this was unimportant detail and it was the speed of that response that must have been decisive. After all, according to the article “by the time that the contracted Gladstone coal unit had gotten out of bed and put its socks on so it can inject more into the grid – it is paid to respond in six seconds – the fall in frequency had already been arrested and was being reversed”. Now, speed of response is a factor in response to contingencies but it’s far from being the only one.

To be fair, a subsequent update to the article makes it clear that there was never an intention to suggest that the response of the battery “averted a blackout”, and that the author’s point was to show “what Tesla could do”.

But to see all this in better context, the point of this post is to show what the system as a whole does when a large generating unit trips, covering the response of a broad set of generators.

Doing this requires grappling with AEMO data that is a lot more obscure than the usual 5-minute and 30-minute market data dealt with by products like NEM-Watch and ez2view (as if that data isn’t obscure enough!). Another reason for the delay in getting this article together.

Product Manager’s Note: Coincident with Allan’s article here, we have been investigating the inclusion of this 4-second SCADA data into one or more of our products. It’s logged with an internal job number of TFS-9575. We’d like to speak with clients who would have an interest in using this data, in order that we can work through the complexities that this inclusion would necessitate. Please give us a call (+61 7 3368 4064).

A quirk of the FCAS markets means that AEMO publishes operational data from its Energy Management System which captures SCADA data every four seconds on generator outputs, system frequency, and other arcana. For very good reasons commercial electricity market viewer / analysis products don’t deal with this level of detail, but it’s the best available public source to understand what happens in response to a large unit trip. It’s this sub-5 minute world that FCAS is concerned with. I’m not sure if market operators anywhere else in the world routinely publish data at this level of operational detail, but I’d be surprised if many do.

For a start we can look at what NEM generation levels and system frequency were doing for the five minutes up to and just after the Loy Yang A3 trip:

Here we are looking at a single five-minute dispatch interval. The blue Generation line is essentially total scheduled generation in the NEM, and the red Target line is what AEMO has instructed that generation to produce by the end of the interval. The fact that generation exceeds the target level by around 80-100 MW for most of the interval could reflect a number of factors, for example scheduled demand being higher than forecast by AEMO (requiring some generators to be dispatched upwards in the raise regulation FCAS market, to maintain system frequency at 50 Hz).

The Frequency line shows system frequency which is nominally 50 Hz, with a normal tolerance range of +/- 0.15 Hz, indicated by the upper and lower reference lines. The grey shaded time interval covers just over 30 seconds (from 1:58:55 AM to 1:59:31 AM) during which the Loy Yang A unit’s output fell from nearly 550 MW to zero as the generator went offline.

You can clearly see the effect on system frequency – as input to the grid drops, its frequency begins to slow down, just like a vehicle travelling steadily up a hill would decelerate if its engine power fell. In fact power system frequency is the direct analogue of speed, because it reflects the rate of rotation of all the spinning synchronous generators and motors connected to the system.

You can also see a notch in the overall generation level corresponding to the falloff of the Loy Yang A unit, but interestingly this is nothing like as large as the 550 MW loss of generation from that unit, and quickly bounces back to close to where it was – so what’s going on? And finally as generation bounces back, system frequency stabilises although it still sits below the lower tolerance band.

We know the tripped Loy Yang A unit didn’t suddenly come back online, so other generators must have made up the deficiency – which ones? The next chart, covering 30 seconds either side of the trip, is a bit busy, but it highlights the fact that many generators across the NEM responded, prompted by the falling system frequency. This is a great example of FCAS in action:

The top panel is essentially a closeup of the same Reneweconomy chart that prompted all this, showing the rundown of the Loy Yang A unit (LH axis) and the response of the Tesla battery / Hornsdale Power Reserve (RH axis). The middle panel shows the increase in output from the whole subset of generators around the NEM that materially responded to the event (plenty didn’t). To be clear, except for the battery and the Jindabyne hydro unit in blue, these generators were already online and producing before the trip – they simply increased their output, using what’s known as “spinning reserve”. In terms of FCAS markets, this response would be primarily the fast raise (6 second) and slow raise (60 second) contingency services enabled by AEMO. For emphasis, I’ve highlighted in red the responses from the SA battery and also from Gladstone in Queensland. It’s very clear that they were only a small part of the overall story, nor was either the first to respond to the event. In fact most of the response came from old-fashioned coal-fired stations. That’s not at all surprising, because in this overnight timeframe with low demand it’s predominantly coal generation that’s online, and contrary to what you might expect from reading elsewhere, during low to moderate demand periods online coal fired generation can usually vary its output fast enough, in aggregate, to respond perfectly adequately to this kind of event.

[Finally a note on chronology – it’s curious that system frequency appears to start falling well before the major changes in generation, by about 12-20 seconds, and then to stabilise before the bulk of the generation response arrives. Whilst the data used in these charts is all timestamped according to AEMO’s public files, I strongly suspect there are some measurement lags or timing offsets present in this raw operational data, and it’s likely that in real time the inflection points in frequency and generation levels respectively line up much more closely than shown above.]