SET_APC_COMPENSATION

APC Compensation payment amounts in the Settlements timeframe

Warning

This is a different table to SETAPCCOMPENSATION

Column Schema

APEVENTID

AP Event Id

SQL Type: NUMBER(6) Polars Type: pl.Int32

This column is mandatory, and a primary key.

CLAIMID

AP Event Claim Id

SQL Type: NUMBER(6) Polars Type: pl.Int32

This column is mandatory, and a primary key.

PARTICIPANTID

Participant identifier

For more information, see the dedicated documentation page.

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

This column is mandatory, and a primary key.

PERIODID

Trading interval identifier

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

This column is mandatory, and a primary key.

SETTLEMENTDATE

Settlement run 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 number

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

This column is mandatory, and a primary key.

COMPENSATION_AMOUNT

Compensation amount for the event claim in this interval

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

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").str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast(
        {
            "APEVENTID": int,  # or pl.Int32
            "CLAIMID": int,  # or pl.Int32
            "PARTICIPANTID": pl.Categorical,
            "PERIODID": int,  # or pl.Int16
            "VERSIONNO": int,  # or pl.UInt16
            "COMPENSATION_AMOUNT": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['APEVENTID', 'CLAIMID', 'PARTICIPANTID', 'PERIODID', 'SETTLEMENTDATE', 'VERSIONNO']
    .unique(
        subset=[
            "APEVENTID",
            "CLAIMID",
            "PARTICIPANTID",
            "PERIODID",
            "SETTLEMENTDATE",
            "VERSIONNO",
        ],
        keep="any",
    )
)

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:

• APEVENTID

• CLAIMID

• 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.

Look at the data and use your best judgement.

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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