Kogan Creek trip on 9th October 2019 delivers highest ‘aggregate Raw Off-Target’ for the 2019 year for ‘all Coal’


Today (Tue 5th May) I posted the lengthy article ‘Balancing Supply and Demand …. in the fast approaching ‘NEM 2.0’ world’.

I invested the time to do that because we see some important questions in there that need to be answered in order to make a ‘NEM 2.0’ market actually deliver on what it needs to deliver for the energy transition to succeed.  There were other reasons as well, not least of which was because thinking through changes and challenges like these stands us in much better stead to be ready to continue serving our growing client base well beyond the 2025 start date for NEM 2.0.

(A)  Context…

This (much shorter) Case Study follows from this, with the objective being to illustrate the relevance of this kind of analysis using examples of extremes.

(A1)  … about aggregate Raw Off-Target

Dispatch Intervals in which there is a large aggregate ‘Raw Off-Target’ will be dispatch intervals in which there will be a higher cost imposed on the market for dealing with these imbalances. For those who want to understand more, suggest you read the article.

In highlighting the ‘worst’ dispatch intervals through CAL 2019 for both the ‘all Coal’ and ‘all Wind’ groupings in Tuesday’s lengthy article, I published a summary table – which is copied in here:

. Aggregating All SS Wind
39 units
Aggregating All Coal
48 units
Under-Performance
(including trips)
Aggregate Raw Off-Target >>  0MW

The worst dispatch interval for wind was 02:25 on 1st September 2019.

On this occasion, the ‘all wind’ group saw an aggregate Raw Off-Target was +375MW.  Several other things that could be noted:
(i)  Compared to aggregate Dispatch Target for ‘all wind’ (1,977MW at the time) this represents a deviation of 19.0%; and
(ii)  The timing of this extreme (02:25) was in the middle of the night, so presumably around the time of minimum demand over the day.

This will be discussed in a Case Study here.

The worst dispatch interval for coal was 08:05 on 9th October 2019.

On this instance, the ‘all coal’ group saw an aggregate Raw Off-Target as high as +709MW:
(i)  That’s just under double the deviation/discrepancy in the ‘all wind’ group
(ii)  Compared to the aggregate Dispatch Target for the group (14,232MW) this represents a deviation of 5.0%

This is the subject of this particular Case Study.

Over-Performance
Raw Off-Target << 0MW

The worst dispatch interval for wind (-301MW) was 19:25 on 31st October 2019.

In this instance:
(i)  This was roughly the same as the extreme for coal,
(ii)  But it’s on a much lower aggregate Dispatch Target (2,545MW) – which represents a much larger deviation (of 11.8%).

This will be discussed in a Case Study here.

The worst dispatch interval for coal was 15:05 on 12th October 2019.

This was just 3 days after the worst outcome at the opposite extreme!
(i)  The amount was –357MW
(ii)  On this occasion the deviation was 2.6% on a slightly lower aggregate Dispatch Target (13,766MW) for all coal.

This will be discussed in a Case Study here.

This article today is the first of four case studies.

(A1)  … about why we invest time in these Case Studies

Also worth reiterating some of the reasons why we significant invest time in delving into specific instances through case studies like these (some of which make their way onto WattClarity).

Do you know of others who can help us expand our capability?

 

(B)  A focused look at this Dispatch Interval

In this particular Case Study, we focus on the ‘worst’ example of an aggregate Raw Off-Target for ‘all coal’ units well above 0MW (i.e. where these units were, collectively, under-performing) – specifically this occurred at 08:05 on Wednesday 9th October 2019.

 

(B1)  All Coal Units

In the table below I have compiled the relevant data for all of the 48 x coal-fired units at that dispatch interval (with those offline on outage indicated):

2020-05-05-WattClarity-Table-CoalHigh

Click on the image for a larger view.

We can see that Kogan Creek was the stand-out contributor to what was the largest positive aggregate Raw Off-Target value for the ‘all Coal’ grouping through the whole of calendar 2019….

(B2)  Somewhat unexpected trip at Kogan Creek as the main culprit

… so we’d like to take a closer look.

Thankfully, it is quite easy (using the ‘Time-Travel’ function in our ez2view software) to wind back the clock and review what the start of the market was at that time – using the ‘Unit Dashboard’ widget shown here:

2019-10-09-at-08-05-ez2view-KPP_1

Click on the image for a larger view.

As noted in the image, Kogan had been running close to ‘flat out’ around 730MW for the prior 3 hours (which is what could be expected, given the lower-cost nature of its operations compared to other coal plant in the QLD region).   AEMO had provided it with the ‘#KPP_1_E’ quick constraint, asking for it to limit its output to 730MW – which meant a request to reduce its output by only 2MW.

Unfortunately something had emerged, so a rebid had been received at AEMO at 07:37 talking about ‘Technical Issues – AVR Repairs’ (i.e. 25 minutes or so prior to the trip).  We can use the ‘Bids & Offers’ widget to launch a ‘Bid Details’ widget to compare the prior bid (received at 06:56) with this rebid (received at 07:27, when the technical issues were detected):

2019-10-09-at-08-05-ez2view-Kogan-BidComparison

As can be seen, rather than the prior plan of reducing output through the day the operator (CS Energy) had already advised the AEMO in the rebid received at 07:37 that they would be taking Kogan out of service, and that it would be fully offline by 09:00.

I flag this sequence of rebids to note that the AEMO would have been aware, over the preceding half an hour, that Kogan had issues and was in the process of coming offline.  The trip in the 08:05 dispatch interval (offline an hour earlier than the AEMO was expecting) was not exactly the timing they would have been expecting – but still not* totally unexpected.

*  those kinds of ‘totally unexpected’ trips do happen sometimes.  The numbers work out such that none of those trips contributed to an ‘aggregate Raw Off-Target’ for ‘all Coal’ greater than the extreme showed in this dispatch interval through 2019, however.

(B3)  Some other coal units

In the table above I have also included some other notes for other units – so thought it might be useful to include a few other snapshots from ez2view.  In particular, I have pulled out the two units which had (apart from Kogan) the biggest ‘Raw Off-Target’ value for the dispatch interval in question.

In the case of one other unit (as with Kogan above) it was derived in ez2view as being declared as ‘Off-target’ by the AEMO (i.e. first rung on the ladder to being declared ‘Non-Conforming’ by the AEMO, which is something all generators should seek to avoid).

B3a)  Callide B2

In the case of Callide B2, the unit was derived as ‘Off-Target’ because (even allowing for some deviations with enablement for Regulation FCAS) it was too high in output compared with what its Dispatch Target had been for this Dispatch Interval:

2019-10-09-at-08-05-ez2view-CALL_B_2

More can be deduced from this widget – however the fundamental point was that (with the Raw Off-Target being negative in this case – i.e. over-performing) this unit was inadvertently working to reduce the aggregate discrepancy for the ‘all coal’ grouping in this Dispatch Interval.

B3b)  Eraring unit 4

I’ve also added in this snapshot from Eraring Unit 4:

2019-10-09-at-08-05-ez2view-ER04

In this case, the unit was dispatched up another 15MW in response to 25MW of additional capacity being available for the 08:30 trading period (i.e. first dispatch interval 08:05).  However the unit did not respond, for reasons I have not explored.

———–

That’s all I have time for today.  The other 3 Case Studies will follow as time permits.

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.

Leave a comment

Your email address will not be published.


*