SETFCASCOMP

SETFCASCOMP shows the compensation details for Frequency Controlled Ancillary Services (FCAS). These compensation values are calculated by a separate “what if” run of the LP Solver and entered as an unconstrained MW value into settlements.

Deprecation Warning

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

SETFCASCOMP data is confidential to the relevant participant

Source

SETFCASCOMP updates with each Settlement run, if required.

Column Schema

DUID

Dispatchable Unit ID

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

PARTICIPANTID

Participant Identifier

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

Period Identifier

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.

CCPRICE

Compensation Cap

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

This column is not mandatory.

CLEAREDMW

Cleared MW of Unit in First Dispatch period in Trading Interval

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

This column is not mandatory.

EBP

Eligible Bid Price

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

This column is not mandatory.

EXCESSGEN

Excess Generation Payment in trading interval

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

This column is not mandatory.

FCASCOMP

Frequency Control AS Compensation payment to Generator

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

This column is not mandatory.

LASTCHANGED

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

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.

RRP

Regional Reference Price

For more information, see the dedicated documentation page.

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

This column is not mandatory.

TLF

Transmission Loss Factor of Unit

SQL Type: NUMBER(7,5) Polars Type: pl.Float64

This column is not mandatory.

UNCONSTRAINEDMW

Initial MW of Unit in First Dispatch period in Trading Interval

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(
        {
            "DUID": pl.Categorical,
            "PARTICIPANTID": pl.Categorical,
            "PERIODID": int,  # or pl.Int16
            "VERSIONNO": int,  # or pl.UInt16
            "CCPRICE": pl.Float64,
            "CLEAREDMW": pl.Float64,
            "EBP": pl.Float64,
            "EXCESSGEN": pl.Float64,
            "FCASCOMP": pl.Float64,
            "REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
            "RRP": pl.Float64,
            "TLF": pl.Float64,
            "UNCONSTRAINEDMW": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['DUID', 'PARTICIPANTID', 'PERIODID', 'SETTLEMENTDATE', 'VERSIONNO']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(
        subset=["DUID", "PARTICIPANTID", "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

• PARTICIPANTID

• PERIODID

• 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

Copy as Markdown