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.
Click Report to access the Performance Reports.
Benchmarking Algo ‘what if’ against Actual Manual
Initially performance will be measured by benchmarking Algo “what if” bids against actual manual bids. The main benchmark is to compare gross margin and the breakdown of gross margin for energy and FCAS. Other metrics such as the difference in volumes are also reported.
Note that as manual bids are replaced by Algo bids, benchmarking 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.
Using the Performance Report
Select a Date and a DUID
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.
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’ - Manual Actual”
Specific business case effects on gross margin that can be attributed to items specifically referenced in the original business plan, namely:
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.
Gross margin difference is attributed to raiseFCAS SAS when Algo expected energy target is less than manual expected energy target.
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 regulation volume is zero for Algo, and to avoid double counting for service allocation stack, the Algo and manual energy bid must be the same.
Click on the ‘greater and less than’ symbols to expand and contract components of gross margin
The unit of all values is $. The exception is volume which is MW/5min. Note that energy volume is the average totalcleared of the dispatch interval ending and beginning.
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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.
Gross Margin | Service Revenue | Change in Energy Revenue | Fuel Value | Other |
|---|---|---|---|---|
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 | 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