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Summary
Benchmarking performance is key to understanding the efficacy of your decision making and enables you to interrogate, and thereby improve, the decision making process.
Performance metrics will evolve as manual bids transition to algo bids and as the sophistication of algo bids improves. Therefore the number and types of reports will develop over time.
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Click Report to access the Performance Reports. |
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Benchmarking Algo ‘what if’ against Actual Manual
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Hover over Report and click the report you wish to view |
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Actual (Manual) vs Algo ‘what if’
The first report compares the gross margin of Actual Manual bids against Algo “what if” bids. Other metrics such as the difference in volumes are also reported.
Note that as manual bids are replaced by Algo bids, new benchmarking reports will then compare Algo actual gross margin against Algo “perfect hindsight” gross margin where perfect hindsight is calculated by rerunning the Algos using actual price outcomes to formulate optimal volumes from which gross margin is derived.
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Using the Performance Report
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Select a Date and a DUID |
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Report content
A break down of the gross margin by service for both the Algo ‘what if’ and the manual actual bid (see gross margin description below).
The difference between these values. Note that the difference is defined as “Algo ‘what if’ - minus Manual Actual”
Specific business case effects on gross margin that can be attributed to items specifically referenced in the original business plan, namelycertain market conditions:
The service allocation stack (SAS) for lowerFCAS and RaiseFCAS.
Gross margin difference is attributed to lowerFCAS SAS when Algo expected energy target is greater than manual expected energy target. Therefore the algorithms increase energy generation in order to increase lowerFCAS gross margin resulting in overall greater gross margin.
Gross margin difference is attributed to raiseFCAS SAS when Algo expected energy target is less than manual expected energy target. Therefore the algorithms decrease energy generation in order to increase raiseFCAS gross margin resulting in overall greater gross margin.
Avoiding negative regulation gross margin for either lowerReg or raiseReg due to the change in generated energy.
Gross margin difference is attributed to Algo avoiding negative gross margin if the Algo regulation volume is zero for Algo, and to avoid double counting for service allocation stackSAS (above), the Algo and manual energy bid must be the same.
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Click on the ‘greater and less than’ symbols to expand and contract components of gross margin |
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You may ‘Click and Hold’ on any column heading to then move the column with you mouse |
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Click on ‘column’ to list all the columns. Check or uncheck to hide or make visible. |
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Gross Margin Description
As a first order approximation, total gross margin includes the revenue gained from providing a service plus the impact on the energy produced and the fuel used in providing an FCAS service.
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Gross Margin = Service Revenue + Impact on Energy Revenue + Impact on Fuel Value + Other |
Service | Service Revenue | Change in Impact on Energy Revenue | Impact on Fuel Value | Other | ||
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Energy | Average Totalcleared * RRP | N/A this value is factored into service revenue | Average Totalcleared * Fuel Cost | contingency RaiseFCAS liability | Note: fuel cost is assumed to be constant | contingency RaiseFCAS liability refer to AEMO literature for a derivation |
LowerReg | LowerRegEnablement * LowerRegRRP | - Utilisation1 * LowerRegEnabled * energyRRP | + Utilisation * LowerRegEnabled * Fuel Cost | |||
RaiseReg | RaiseRegEnablement * RaiseRegRRP | + Utilisation * RaiseRegEnabled * energyRRP | - Utilisation * RaiseRegEnabled * Fuel Cost | |||
Sum of Contingency FCAS | Sum of (ContingencyFCASEnablement * ContingencyFCASRRP) | Contingency FCAS utilisation is currently considered zero however this may change (particularly 5min FCAS) |
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