FPP_EST_COST

This report delivers the estimated cost for each FPP unit for each constraint for each 5 minute trading interval

Column Schema

CONSTRAINTID

Constraint ID (binding constraint ID from FCAS data used in FPP calculations)

SQL Type: VARCHAR2(20) Polars Type: pl.Categorical

This column is mandatory, and a primary key.

FPP_UNITID

FPP Unit ID (registered DUID / TNI)

SQL Type: VARCHAR2(20) Polars Type: str

This column is mandatory, and a primary key.

INTERVAL_DATETIME

Date and time of the trading interval (DD/MM/YYYY HH24:MI:SS) fixed to the UTC+10 time zone (NEM time)

SQL Type: DATE Polars Type: pl.Datetime()

This column is mandatory, and a primary key.

Example: 2026/01/20 00:05:00

VERSIONNO

The version number. In most cases this version will be the FPP run number from the FPP database, however there are some cases (like a new pricing run of the constraint FCAS processor received by the FPP system) where the version number listed here will be the financial estimate run number from the FPP database (this number is a different sequence from the FPP run number because there is no recalculation of performance or contribution, just changes to pricing / p regulation hence only the financial estimation is performed).

SQL Type: NUMBER(10) Polars Type: pl.UInt64

This column is mandatory, and a primary key.

BIDTYPE

Bid type (the bid type saved in relation to constraint ID from FCAS data used in FPP calculations)

The SQL type is VARCHAR2(10). This is actually a factor/enum, with values:

• ENERGY

• LOWERREG

• LOWER60SEC

• RAISE5MIN

• LOWER5MIN

• LOWER6SEC

• RAISE60SEC

• RAISE6SEC

• RAISEREG

• RAISE1SEC

• LOWER1SEC

This column is not mandatory.

FPP

FPP in AUD (the financial estimate of frequency performance payment calculated for the constraint / bid type / unit). This value can be either positive (credit) or negative (debit). For details on the calculation, please see FPP procedure and supporting documentation.

SQL Type: NUMBER(18,8) Polars Type: pl.Float64

This column is not mandatory.

LASTCHANGED

Last date and time record changed

For more information, see the dedicated documentation page.

SQL Type: DATE Polars Type: pl.Datetime()

This column is not mandatory.

Example: 2026/01/20 00:05:00

PARTICIPANTID

Participant ID

For more information, see the dedicated documentation page.

SQL Type: VARCHAR2(20) Polars Type: pl.Categorical

This column is not mandatory.

RELEVANT_REGIONS

Relevant regions (a comma separated list of the relevant regions for the constraint from FCAS data)

SQL Type: VARCHAR2(200) Polars Type: str

This column is not mandatory.

UNUSED_FCAS

Unused recovery FCAS in AUD (the financial estimate of the recovery of unused FCAS calculated for the constraint / bid type / unit). This value will be either 0 (nil), or a negative value (debit) only. For details on the calculation, please see FPP procedure and supporting documentation.

SQL Type: NUMBER(18,8) Polars Type: pl.Float64

This column is not mandatory.

USED_FCAS

Used recovery FCAS in AUD (the financial estimate of the recovery of used FCAS calculated for the constraint / bid type / unit). This value will be either 0 (nil), or a negative value (debit) only. For details on the calculation, please see FPP procedure and supporting documentation.

SQL Type: NUMBER(18,8) Polars Type: pl.Float64

This column is not mandatory.

New Data Timing

At the completion of an FPP calculation run for a closed trading interval (i.e. every 5 minutes)

Code Examples

Polars

Casting is enums, categoricals etc is not essential, but may speed up parsing. Swapping int for pl.UInt16, pl.Int32 would also speed up parsing. However some of the datatypes are inferred, so they may be wrong.

This code deduplicates for you. Deduplication is normally only required across multiple months.

This code assumes that all columns are present, which may not be true for older files.

import polars as pl
import polars.selectors as cs

# e.g. "2029/03/15 00:05:00"
dt_s_fmt = "%Y/%m/%d %H:%M:%S"

csv_paths = ["SOMEFILE.CSV", "OTHERFILE.CSV"]

lf = (
    pl.concat(
        [
            pl.scan_csv(
                csv_path,  # must be unzipped beforehand
                skip_rows=1,  # skip the header
                infer_schema=False,  # much faster
            )
            .filter(cs.by_index(0) == "D")  # drop the footer
            .select(~cs.by_index(range(4)))  # drop metadata columns
            for csv_path in csv_paths
        ],
        how="diagonal_relaxed",
    )
    .rename(
        {
            # These columns have names that conflict with metadata columns
            "FPP_duplicated_0": "FPP"
        }
    )
    .with_columns(
        pl.col("INTERVAL_DATETIME", "LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast(
        {
            "CONSTRAINTID": pl.Categorical,
            "FPP_UNITID": str,
            "VERSIONNO": int,  # or pl.UInt64
            "BIDTYPE": pl.Enum(
                [
                    "ENERGY",
                    "LOWERREG",
                    "LOWER60SEC",
                    "RAISE5MIN",
                    "LOWER5MIN",
                    "LOWER6SEC",
                    "RAISE60SEC",
                    "RAISE6SEC",
                    "RAISEREG",
                    "RAISE1SEC",
                    "LOWER1SEC",
                ]
            ),
            "FPP": pl.Float64,
            "PARTICIPANTID": pl.Categorical,
            "RELEVANT_REGIONS": str,
            "UNUSED_FCAS": pl.Float64,
            "USED_FCAS": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['CONSTRAINTID', 'FPP_UNITID', 'INTERVAL_DATETIME', 'VERSIONNO']
    # Tie-breaker: greatest value of LASTCHANGED
    .sort(LASTCHANGED, descending=False)
    .unique(
        subset=["CONSTRAINTID", "FPP_UNITID", "INTERVAL_DATETIME", "VERSIONNO"],
        keep="last",
    )
)

Nemosis

This table is not yet available in Nemosis.

Availability

Visibility: Private

This table is not available in the public monthly MMSDM archive. Is is only available to registered market participants over FTP inside a VPN.

Warning

The REPORT_NAME metadata column has the same column name as an actual columns FPP. So the raw CSVs have duplicate column names. You will need to be careful when reading this CSV.

Deduplication and PDR Loader Configuration

The primary keys are:

• CONSTRAINTID

• FPP_UNITID

• INTERVAL_DATETIME

• VERSIONNO

The transaction type is "insert", which means that you will usually not see duplicate data. If you do, you can deduplicate based on the primary keys, without sorting first. Rows from different files with the same primary keys should have the same values.

PDR Loader configuration is:

Report Name	Report Subtype	Version
FPP	EST_COST	1

This means that the CSV files start with metadata columns:

I,FPP,EST_COST,1,...
D,FPP,EST_COST,1,...

Links

This page as JSON (schema documented here, but may change)

Download a JSON Schema specification for this table.

Official AEMO schema documentation

Other Information

Package: FPP

Copy as Markdown