Case Study of 19:25 on Thursday 5th October 2017 (aggregate Raw Off-Target +305MW for Semi-Scheduled units)

This is now the 9th Case Study in a series, with each looking at one or more of the 98 discrete Dispatch Intervals (2013 to 2019) identified as having extreme outcomes for aggregate under-performance or (more rarely) over-performance across all Semi-Scheduled plant:

1)  We’re using the Aggregate Raw Off-Target measure, which we analysed across 2019 in our Generator Statistical Digest 2019, and have embedded into our ez2view software.

2)  We’re specifically looking at values greater than 300MW:

(a)  Of the 98 dispatch intervals, only 5 were of collective over-performance;

(b)  Hence the remaining 93 dispatch intervals were of collective under-performance.

3)  The first 8 Case Studies covered 2013, 2014, 2015 and 2016, whilst this 9th (and this 10th – to come) Case Study will also cover 2017.

(a)  The bulk of the incidents ran from September 2018 through to December 2019 – noting we’ve not yet looked into 2020 data, which will be the focus of the GSD2020:


(b)  There is a clear escalation in extreme variability, and we’re walking through these Case Studies in order to understand why and what the implications are of this.

4)  We’re doing this given a push from the AER Issues Paper, but moreso inspired by the deliberations by the (now defunct) COAG Energy Council and (under threat?) ESB relating to ‘NEM 2.0’.


… so let’s look at this particular event:


(A)  Summary results for Thursday 5th October 2017

Again, we start with this summary table, highlighting the individual Raw Off-Target performance of all Semi-Scheduled units that were operational at the time:


There’s a few things that can be highlighted here:

1)  This period sees 30 DUIDs registered in total at this point in time:

(a)  4 of these are Solar Farms;

(b)  So the remaining 26 are Wind Farms.

2)  Across all of the DUIDs:

(a)  3 played no role (probably not operational)

(b)  14 DUIDs were under-producing, whilst 13 DUIDs were over-producing (i.e. in this case, almost even – which does not seem to happen too regularly in the samples we’ve looked at to date).

(c)  Interestingly, there was a cluster of 9 wind farms in South Australia over-producing (i.e. negative Raw Off-Target), but in all cases only slightly so.

(d)  In contrast, there are 5 Wind Farms significantly under-producing (the ARWF1 particularly so in this instance).


(B)  Looking at some specific DUIDs

… and so we will explore each of these below, working top-to-bottom on the table.  Again we can show this specific dispatch interval clearly with the ‘Unit Dashboard’ widget in ez2view, wound back 3 years ago through the powerful ‘Time Travel’ functionality:

(B1)  Gullen Range 1 Wind Farm in NSW

We see the GULLRWF1 was over-performing by 23MW:


(B2)  Ararat Wind Farm in VIC

ARWF1 was clearly the largest contributor (+212MW) to the aggregate under-performance across the Semi-Scheduled group of units (+305MW in total):


It seems here that the unit tripped for some reason.  A large reduction like that was certainly not forecast ahead of time in the AEMO’s P30 predispatch forecasts for the wind farm’s (energy constrained) Availability – as we can see from the ‘Forecast Convergence’ widget in ez2view focused on ARWF1 at the same point in time:


(B3)  Macarthur Wind Farm in VIC

We see at MACARTH1 (a frequent entry in these Case Studies, not least of which because of its large size) that the unit under-performed by 28MW.


The snapshot shows that deviations of around that much had been a frequent occurrence over the (3-hour) ‘look back’ selection in the window.

(B4)  Mt Mercer Wind Farm in VIC

The MERCER01 DUID shows an under-performance of 11MW


(B5)  Lake Bonney 2 Wind Farm in VIC

This was the second largest contributor (at +45MW – again an under-performance):



It’s worth noting in conclusion here that these 5 DUIDs were spread across a reasonably wide area of NSW, VIC and SA and that there was no unit that significantly over-performed at the same time to balance out the under-performance.  Worth keeping in mind, with respect to what the implications might be into the future….

About the Author

Paul McArdle
One of three founders of Global-Roam back in 2000, Paul has been CEO of the company since that time. As an author on WattClarity, Paul's focus has been to help make the electricity market more understandable.

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