SET_FCAS_PAYMENT

SET_FCAS_PAYMENT sets out the enabling payment details for frequency controlled Ancillary Services.

SET_FCAS_PAYMENT data is confidential to the relevant participant.

Volume

Approximately 150,000 per week.

Tip

1 second, 6 second, 5 minute, 60 second and regulation refer to types of FCAS markets.

Column Schema

DUID

Dispatchable unit identifier

Warning

This column may contain funny characters such as / (slash) or # (hash). This may break hive-style partitioning.

For more information, see the dedicated documentation page.

SQL Type: VARCHAR2(10) Polars Type: pl.Categorical

This column is mandatory, and a primary key.

Example: W/HOE#2

PERIODID

Settlements Trading Interval.

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

This column is mandatory, and a primary key.

SETTLEMENTDATE

Settlement Date

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

VERSIONNO

Settlement Run No

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

This column is mandatory, and a primary key.

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

LOWER1SEC_PAYMENT

Payment amount for the very fast lower service

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

This column is not mandatory.

LOWER5MIN_PAYMENT

Lower 5 Minute Payment

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

This column is not mandatory.

LOWER6SEC_PAYMENT

Lower 6 Second Payment

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

This column is not mandatory.

LOWER60SEC_PAYMENT

Lower 60 Second Payment

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

This column is not mandatory.

LOWERREG_PAYMENT

Lower 5 Minute Regulation Payment

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

This column is not mandatory.

PARTICIPANTID

Participant identifier

For more information, see the dedicated documentation page.

SQL Type: VARCHAR2(10) Polars Type: pl.Categorical

This column is not mandatory.

RAISE1SEC_PAYMENT

Payment amount for the very fast raise service

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

This column is not mandatory.

RAISE5MIN_PAYMENT

Raise 5 Minute Payment

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

This column is not mandatory.

RAISE6SEC_PAYMENT

Raise 6 Second Payment

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

This column is not mandatory.

RAISE60SEC_PAYMENT

Raise 60 Second Payment

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

This column is not mandatory.

RAISEREG_PAYMENT

Raise 5 Minute Regulation Payment

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

This column is not mandatory.

REGIONID

Region Identifier

For more information, see the dedicated documentation page.

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

• QLD1

• VIC1

• TAS1

• NSW1

• SA1

• SNOWY1

This column is not mandatory.

New Data Timing

After posting of a 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(
        {
            "DUID": pl.Categorical,
            "PERIODID": int,  # or pl.Int16
            "VERSIONNO": int,  # or pl.UInt16
            "LOWER1SEC_PAYMENT": pl.Float64,
            "LOWER5MIN_PAYMENT": pl.Float64,
            "LOWER6SEC_PAYMENT": pl.Float64,
            "LOWER60SEC_PAYMENT": pl.Float64,
            "LOWERREG_PAYMENT": pl.Float64,
            "PARTICIPANTID": pl.Categorical,
            "RAISE1SEC_PAYMENT": pl.Float64,
            "RAISE5MIN_PAYMENT": pl.Float64,
            "RAISE6SEC_PAYMENT": pl.Float64,
            "RAISE60SEC_PAYMENT": pl.Float64,
            "RAISEREG_PAYMENT": pl.Float64,
            "REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
        }
    )
    # Deduplicate
    # We want one row per ['DUID', 'PERIODID', 'SETTLEMENTDATE', 'VERSIONNO']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(subset=["DUID", "PERIODID", "SETTLEMENTDATE", "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:

• DUID

• PERIODID

• SETTLEMENTDATE

• 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_PAYMENT	6

This means that the CSV files start with metadata columns:

I,SETTLEMENTS,FCAS_PAYMENT,6,...
D,SETTLEMENTS,FCAS_PAYMENT,6,...

Indexes

• LASTCHANGED

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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