As noted here on this main page, the Generator Report Card (our 20-year review with data to 31st December 2018) has been developed with a number of different parts, or sections – including (in Part 5) a lengthy Glossary of terms used in the analytical process, and in the report card.
Inside the Glossary, we explain (amongst other things) the Metrics used to assess a range of different aspects of performance of the generators over up to 20 years of NEM history. Dozens of different Metrics have been identified and are being progressively compiled for inclusion in the Generator Report Card:
1) Some are being included in trended raw form in Part 3 (Results by Station) of the Generator Report Card
2) Others are being analysed for the discussion included in Part 2 (Key Insights) and in Part 4 (Results by Aggregation).
3) As the Metrics are identified, and added to the Glossary, they are assigned their own ID number for internal purposes.
The following diagram has been drawn to convey the large scale of the project that has been undertaken across 3 dimensions:
From time to time we have discussed some particular Metrics within more regular articles on WattClarity site. As time permits (and if we remember to do so) we will link them in to the following table:
(note that we have added the Metric number in here for our future reference – it’s probably not going to be too much use to you, other than to help you understand the number of metrics we’ve identified)
|(#) and Metric Name||Discussion for this metric on WattClarity ®|
|(1) Energy-Constrained Generator Availability||Availability data is used in many articles throughout WattClarity. In the Generator Report Card, we’re taking a long-range view (not quite 20 years, as data was not published at the start of the NEM) to see what has been changing over time, and how significantly.
On 7th February 2019, AGL Energy provided it’s 2019 half-year results, including a note about the availability of its aggregated portfolio.
Given we were working on the Availability Metric at the time, we posted this view of availability at its three main thermal assets – Loy Yang A, Bayswater and Liddell.
|(59) Hours for which Connection Point Subject to Bound Constraint||In this article on 1st January (which also flagged the commencement of detailed analysis) we flagged some preliminary results that indicated that there were up to 200 DUIDs at connection points affected by constraints in any given months. This showed a percentage of DUIDs affected between 30% and 50% each month.
For clarity, note that the specific DUIDs affected will vary from one month to the next, depending on the specifics of the constraint equations that have bound.
It’s for this reason that we have invested time in exploring constraints and their affect on generators (at both macro and micro levels) within the Generator Report Card.
|(67) Instantaneous Reserve Plant Margin (IRPM)||The very tight supply/demand balance experienced across Victoria and South Australia on Thursday 24th and Friday 25th January attracted much commentary, including here on WattClarity.
In this particular article reviewing what happened on 24th January the Instantaneous Reserve Plant Margin (IRPM) metric that’s been featured in NEMwatch for a number of years was featured – and proved a very useful non-price indicator of the balance between supply and demand.
It’s for the same reason we’re trending distributions of IRPM over time as a macro indicator of the balance between supply and demand to provide some context to the more detailed analysis.
|(17) Marginal Loss Factor (MLF)||A Marginal Loss Factor (MLF) represents the AEMO’s calculations of the average marginal losses expected from the point of generation to the relevant Regional Reverence Node. This is used in calculation of Spot Revenues.
Back in July 2018, we posted this article exploring the surprise expressed by a range of people when confronted with significant changes in their MLF from one year to the next – which, if the loss factor had dropped, directly represented a significant drop in top-line spot revenue figures.
In his “Lessons from the trenches” article from September 2018, Jonathon Dyson notes (about MLFs) that:
In March 2019 we again see surprise expressed when MLFs drop as a result of more generation crowding into oversupplied areas remote from the customer. In response to this, we posted this article.
It’s for this reason that we are investing considerable time in the exploration of MLF for all power stations over a 20-year period.
|(20) Generator Off-Target||On 7th January 2019 we posted this article that flagged how we were conducting detailed analysis (for every DUID across all Dispatch Intervals they were operational for) to identify which generators had been significantly Off-Target.
This is one of the metrics that are useful in understanding which generators are more dependable than others in their output. We flagged that units might be Off-Target for a number of reasons, including:
We also mentioned the tabulated list of “worst performers” in this article on 7th March.
|(26) Actual Output, in MW||Naturally, the Generator Report Card will contain much analysis of actual levels of Actual Output – including the trend of ranges in output over time.|
|(60) RRP Contribution, and RRP Contribution Factor, derived from Price Setter||In this “Intermediate Guide to how Prices are Set” from on 28th February, we talked about the complexities involved in the price setting process, and hence in the interpretation of the Price Setter files published by the AEMO.
Because of these complexities we are exploring several different methods for providing a longitudinal answer to the question that is being increasingly asked “who’s set the price?”
As the this article helps to explain, this is not as simple to answer as it might initially seem.
|(27) Production (As Generated), in MWh||Naturally, the Generator Report Card will contain much analysis of actual levels of Production – including the trend of ranges in output over time.|
|(29) Number of Rebids in Total||On 13th February 2019, we posted this article highlighting preliminary analysis of a trend of total number of rebids across all DUIDs in the NEM.|
|(34) Number of Rebids Not Well Formed||As part of that same article highlighting overall number of bids we took a look at AER’s revised version of the “Rebidding and Technical Parameters Guideline” to categorize all rebids under three general categories:
Category 1 = “Well Formed”, applying a strict interpretation of the AER Guidelines
Category 2 = Not in Category 1, but “Well Formed”, if applying a looser interpretation of the AER Guidelines
Category 3 = “Not Well Formed”, even with a looser interpretation of the
We posted some preliminary analysis that suggested a non-trivial number of rebids still “Not Well Formed” .
In the Generator Report Card, we are taking this analysis further.
|(48) Number of Starts||As part of the analytical process leading supporting the Generator Report Card, we have identified all occasions when every DUID in the NEM started (i.e. increased Actual Output from 0MW to something positive). We’ve done this for a number of reasons, including the following:
1) It can help us understand changes in patterns of operations for the grid/market as a whole – as discussed in this article on 7th March about “the death of baseload and rise of cycling”.
2) At a more micro level, as well, it can help us understand degradation patterns and trends – hence being able to understand for every single generator:
|(72) Possible Trip – Method 2 (AEMO Logic)||Power station technical performance is of great interest to many people – hence the effort we’ve invested in the Generator Report Card. One of the technical parameters of interest is reliability and, as a subset of this, the number of trips.
On February 13th the AEMO published its “Quarterly Energy Dynamics – Q4 2018” (the PDF is here). In particular this report noted that:
In this particular report, the AEMO defines a “sudden trip” as:
We’re taking this definition and having a look at what the performance has been, using this metric, over the longer-term.
Note that identifying trips is not as easy as it might initially seem when scanning over 20 years of data for hundreds of DUIDs. There are a number of different algorithms that might be coded to produce a result – but (to date) none have proven themselves to be of high enough accuracy for us to be perfectly comfortable.
|(N) To be Added||Will link in more use of the Metrics from the Generator Report Card more broadly in articles on WattClarity as we add more articles (and if we remember to link them in here!)
This page will be fleshed out sporadically, as time permits. However readers should understand that our main focus is on completing the Generator Report Card itself.
Don’t forget that you can secure the early-bird pricing if you submit your order prior to the release of the Report Card.