SET_FCAS_REG_DEF_AMT

This report contains the the FCAS Regulation Amounts that include FPP Amounts, Used Amounts and Unused Amounts calculated using the Default Contribution Factors. This is when FPP Factors are not available for a particular interval and system used DCF in the calculation.

Column Schema

CONSTRAINTID

The FCAS Regulation Constraint ID

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

This column is mandatory, and a primary key.

PERIODID

The Period ID Identifier

SQL Type: NUMBER(3,0) Polars Type: pl.Int16

This column is mandatory, and a primary key.

SETTLEMENTDATE

The Settlement Date of the Billing Week

Warning

Despite what the name suggests, this is a datetime, not a date.

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

This column is mandatory, and a primary key.

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

UNITID

The UnitId with CF factor assigned. This is a DUID or a TNI. Refer table FPP_CONTRIBUTION_FACTOR

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

This column is mandatory, and a primary key.

VERSIONNO

The Settlement Run No

SQL Type: NUMBER(3,0) Polars Type: pl.UInt16

This column is mandatory, and a primary key.

BIDTYPE

The BidType LOWERREG or RAISEREG for the Constraint ID

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.

LASTCHANGED

The Last Changed Date time of the record.

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

The Participant Id Identifier

For more information, see the dedicated documentation page.

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

This column is not mandatory.

UNUSED_AMOUNT

The Regulation Unused Recovery Amount from Eligible Units using DCF Value

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

This column is not mandatory.

New Data Timing

Daily Billing Run & Posting a PRELIM/FINAL and REVISE Billing Run

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",
    )
    .with_columns(
        pl.col("SETTLEMENTDATE", "LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast(
        {
            "CONSTRAINTID": pl.Categorical,
            "PERIODID": int,  # or pl.Int16
            "UNITID": pl.Categorical,
            "VERSIONNO": int,  # or pl.UInt16
            "BIDTYPE": pl.Enum(
                [
                    "ENERGY",
                    "LOWERREG",
                    "LOWER60SEC",
                    "RAISE5MIN",
                    "LOWER5MIN",
                    "LOWER6SEC",
                    "RAISE60SEC",
                    "RAISE6SEC",
                    "RAISEREG",
                    "RAISE1SEC",
                    "LOWER1SEC",
                ]
            ),
            "PARTICIPANTID": pl.Categorical,
            "UNUSED_AMOUNT": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['CONSTRAINTID', 'PERIODID', 'SETTLEMENTDATE', 'UNITID', 'VERSIONNO']
    # Tie-breaker: greatest value of LASTCHANGED
    .sort(LASTCHANGED, descending=False)
    .unique(
        subset=["CONSTRAINTID", "PERIODID", "SETTLEMENTDATE", "UNITID", "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.

Deduplication and PDR Loader Configuration

The primary keys are:

• CONSTRAINTID

• PERIODID

• SETTLEMENTDATE

• UNITID

• 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
SETTLEMENTS	FCAS_REG_DEF_AMT	1

This means that the CSV files start with metadata columns:

I,SETTLEMENTS,FCAS_REG_DEF_AMT,1,...
D,SETTLEMENTS,FCAS_REG_DEF_AMT,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: SETTLEMENT_DATA

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