SETFCASRECOVERY

SETFCASERECOVERY shows reimbursements for the Frequency Control Ancillary Services compensation.

Deprecation Warning

This table is in the HISTORICAL TABLES package, so is likely deprecated.

Status

SETFCASERECOVERY is obsolete since the implementation of Ancillary Services Review. For more details, see Change Notice 126.

Confidential to the participant

Source

This view is updated with each Settlement run.

Warning

This is a different table to SET_FCAS_RECOVERY

Column Schema

PARTICIPANTID

Participant to pay recovery

For more information, see the dedicated documentation page.

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

This column is mandatory, and a primary key.

PERIODID

Settlement Period identifier

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

This column is mandatory, and a primary key.

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 mandatory, and a primary key.

SETTLEMENTDATE

Calendar 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.

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 not mandatory.

Example: W/HOE#2

FCASCOMP

Frequency Control Ancillary Service Compensation Payment

SQL Type: NUMBER(15,5) Polars Type: pl.Float64

This column is not mandatory.

FCASRECOVERY

Frequency Control Ancillary Service recovery amount.

SQL Type: NUMBER(15,5) Polars Type: pl.Float64

This column is not mandatory.

FCASRECOVERY_GEN

Frequency Control Ancillary Service recovery amount for Generator

SQL Type: NUMBER(15,5) Polars Type: pl.Float64

This column is not mandatory.

LASTCHANGED

Date and Time last 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

PARTICIPANTDEMAND

Participant Demand in Region

SQL Type: NUMBER(15,5) Polars Type: pl.Float64

This column is not mandatory.

PARTICIPANTDEMAND_GEN

Participant Demand in Region for Generator

SQL Type: NUMBER(15,5) Polars Type: pl.Float64

This column is not mandatory.

REGIONDEMAND

Total Regional demand

SQL Type: NUMBER(15,5) Polars Type: pl.Float64

This column is not mandatory.

REGIONDEMAND_GEN

Total Regional Demand for Generator

SQL Type: NUMBER(15,5) Polars Type: pl.Float64

This column is not mandatory.

New Data Timing

Each settlement 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(
        {
            "PARTICIPANTID": pl.Categorical,
            "PERIODID": int,  # or pl.Int16
            "REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
            "VERSIONNO": int,  # or pl.UInt16
            "DUID": pl.Categorical,
            "FCASCOMP": pl.Float64,
            "FCASRECOVERY": pl.Float64,
            "FCASRECOVERY_GEN": pl.Float64,
            "PARTICIPANTDEMAND": pl.Float64,
            "PARTICIPANTDEMAND_GEN": pl.Float64,
            "REGIONDEMAND": pl.Float64,
            "REGIONDEMAND_GEN": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['PARTICIPANTID', 'PERIODID', 'REGIONID', 'SETTLEMENTDATE', 'VERSIONNO']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(
        subset=["PARTICIPANTID", "PERIODID", "REGIONID", "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:

• PARTICIPANTID

• PERIODID

• REGIONID

• SETTLEMENTDATE

• VERSIONNO

PDR Loader configuration does not mention this table, so it is unclear how to choose which row to keep when there are multiple rows for a given set of primary keys.

You should probably take the row with the maximum LASTCHANGED.

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: HISTORICAL TABLES

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