Traditional AUFLS scheme design has focused on two criteria:
ensuring coverage for the biggest event
maintaining the frequency between those two limits of 47 and 52 Hz.
But it’s a complex area, because the events where you risk over-shedding aren’t necessarily the largest ones and in fact are different for each scheme design.
We needed to come up with a new way to look at scheme performance to capture the full range of events and magnitude of disturbances that involve significant shedding.
We call this the sawtooth analysis.
Let’s return to the graph, which shows frequency on the vertical axis with time on the bottom.
Over on the seesaw, we’ve shown a medium-load scenario.
You can see: Interruptible Load at 360 MW
and two AUFLS blocks of 590 MW each.
This is the amount of load we can control and disconnect from the grid to tackle any size loss in net injection.
These will stay fixed throughout the analysis.
To observe the range of performance, we model incremental losses in generation and simulate how the frequency responds.
The first line shows a loss of 330 MW,
and you can see that 360 MW of Interruptible Load triggers.
There’s a nice, smooth, controlled response, and frequency is restored.
Next we show further losses in 10-MW increments.
Notice how the frequency is starting to get lower and lower as the size of the generation loss increases.
The events being shown on the seesaw and graph are referred to as contingent events.
To cover these events, we rely on purchasing Interruptible Load and Generator Reserves.
This prevents the frequency falling below 48 Hz and returns it to the normal range.
These aren’t really the events that we are concerned about with AUFLS.
As soon the frequency goes below 48 Hz, we are in AUFLS territory.
Here is the first event that triggers AUFLS Block 1.
We have a 500-MW loss of generation which results in 950 MW total load being shed.
This includes both Interruptible Load and AUFLS.
It is worth noting that during an event, the system is not static and most generators are increasing their output in response to the frequency falling.
The amount of load-shed needed is unlikely to equal the size of the event exactly.
In other words, it’s not one for one.
As you can see from this seesaw we are shedding quite a bit more than the system needs, which results in something we call ‘frequency overshoot’.
In this case, the frequency rises above 50 Hz and is heading up towards 52 Hz before it’s pulled back down.
And that’s the result of disconnecting too much demand from the system.
You can see that as the event size increases, you get closer to the ideal in terms of the amount of load-shed the system needs.
Notice that although the generation loss is increasing, the load-shed hasn’t changed.
This is because the blocks are fixed.
In this medium-load example, you can see that although your maximum frequency is coming down, the minimum frequency is continuing to drop.
You are getting closer to triggering AUFLS Block 2.
In the simulation, 770 MW was the last size event that triggered only Block 1.
An increase in trip size of only 10 MW of generation triggers AUFLS Block 2.
From that 10-MW increase in trip size, we have an additional 590 MW in load being shed, bringing the total load-shed to 1,540 MW.
You can probably imagine what is going to happen as a result.
Just as if someone jumped off the seesaw really quickly, you now have overshoot.
We’ve reached that top limit of 52 Hz and have gone into the over-frequency range.
In terms of scheme performance that’s what we call non-performance.
The scheme was unable to prevent the frequency from rising above 52 Hz, which could result in loss of additional generation and system blackout.
This is clearly a situation we want to avoid.
As the size in generation loss increases, the amount of over-shed decreases and the frequency stays within the desired range.
This analysis gives us an overview of how the scheme performs over a range of results.
It enables us to see when we are shedding too much and sending the frequency higher than 52 Hz,
and it also gives us a picture of how the AUFLS scheme performs for larger events such as a 1,200-MW loss.
The drawback is that these traces are complex to read and interpret
this graph alone has around 90 frequency traces on it.
As a result, our engineers did some thinking.
The engineers identified two key performance indicators:
what was the minimum point that the frequency reached and what was the maximum point that it reached, following AUFLS operation?
By adding trip size to the equation, they were able to see how big the event was and how the system responded.
The graph starts to tell more of a story.
They realised they didn’t need the time axis.
We care about the minimum and maximum frequency points, which results in this picture.
What you can now see along the bottom is the amount of net injection that was disconnected
that’s how much generation you lost from your seesaw – and then the frequency that resulted.
The blue line shows the minimum frequency following the event.
The white line is your maximum frequency after AUFLS is shed.
This new improved graph, which we call the ‘sawtooth’,
gives us a picture of how the scheme performs across the range of event sizes.
Let’s now look at how to interpret this graph, which features heavily in the Scheme Reports accessible on the System Operator website.
We will look at what it tells you about scheme performance.
The blue block we’ve highlighted shows event sizes that haven’t fallen below the trigger frequency line.
They haven’t triggered AUFLS, and are managed using Interruptible Load and generator reserves.
Once the minimum frequency falls below the AUFLS settings shown by the blue arrows, then the AUFLS blocks will trigger.
This results in a ‘tooth’ that is reflected on the white maximum-frequency line.
This sharp tooth shows the result of the over-shedding that occurred.
Viewing the events in this way allows us to focus on areas of concern.
In this example, an event of around 800 MW is going to result in overshoot above the 52 Hz limit.
A larger trip size of 1,200 MW results in the frequency going below 47 Hz.
Both of these scenarios result in non-performance, which we want to avoid.
The sawtooth is a huge leap forward for our analysis and gives a much better picture of how the scheme performs across different event sizes.
With this new method of scheme analysis, we were able to compare the existing scheme with a range of scheme designs.
We compared six in total.
On the left we have the existing scheme, with two blocks of 16 per cent.
You can see on the sawtooth that there are two points where the frequency goes outside the acceptable range.
On the right is the winning scheme, with four blocks – 10, 10, 6 and 6 per cent.
Importantly, on the sawtooth you can see the results stay within the acceptable range.
This comparison highlights what we have gained through changing the scheme.
As you can imagine, there are many complex factors that contributed to this scheme design being chosen, and we will look at these next.