Summary
Benchmarking performance is key to understanding the efficacy of your decision making and enables you to analyse, and thereby improve, the decision making process.
Performance metrics change and evolve as manual bids transition to Algo bids and as the sophistication of Algo bids improves. Therefore the number and types of reports will change over time.
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Using a Report Page
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Select a Date, DUID(s) and Algo (if more than one is available)
If more than one DUID is selected then all values are aggregated in the report.
The Report will automatically update as the date is changed or the selected duid(s) change.
The Refresh button’s sole function is to update a current day report to include new dispatch intervals as they materialise.
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The unit of all values is $. The exception is volume which is MW.
Energy volume is the average totalcleared of the dispatch interval ending and beginning.
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Report Types
Benchmarking reports evolve over time as reporting requirements change. For example as manual bids are replaced by Algo bids a new report may 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.
Actual (Manual) vs Algo ‘what if’
This 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.
Content
A break down of the gross margin by service for both the Algo ‘what if’ and the Actual (Manual) bid
A description of gross margin is below
The difference between Algo ‘what if’ and the Actual (Manual) bid gross margin.
Note that the difference is defined as “Algo ‘what if’ minus Manual Actual”
Specific effects on gross margin that can be attributed to particular market conditions. These are:
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. Under certain market conditions the algorithms increase energy generation in order to increase lowerFCAS enablement 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. Under certain market conditions the algorithms decrease energy generation in order to increase raiseFCAS enablement 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 Algos avoiding negative gross margin if the optimised regulation volume is zero, and to avoid double counting for SAS (above), the Algo and manual energy bid must be the same.
Definitions
Gross Margin
As a good 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 plus any other impacts.
Gross Margin = Service Revenue + Impact on Energy Revenue + Impact on Fuel Value + Other
Service | Service Revenue | Impact on Energy Revenue | Impact on Fuel Value | Other |
|---|---|---|---|---|
Energy | Average Totalcleared * RRP | N/A this value is factored into service revenue | Average Totalcleared * Fuel Cost 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) |
1 Utilisation is the effective volume of FCAS enablement that is actually utilised in providing the service. For regulation FCAS this is caused by the unit responding to AGC regulation component signals that are sent by AEMO.
For a detailed derivation of gross margin refer to pdView’s FCASpays knowledge base linked here