MARKET_FEE_EXCLUSION

MARKET_FEE_EXCLUSION shows the list of market fees from which a participant is excluded from funding after a particular settlement date.

MARKET_FEE_EXCLUSION data is confidential to the relevant participant.

Source

MARKET_FEE_EXCLUSION updates only on change of participant configuration.

Column Schema

EFFECTIVEDATE

Date on which this data becomes effective

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

MARKETFEEID

Identifier for Market Fee

SQL Type: VARCHAR2(10) Polars Type: str

This column is mandatory, and a primary key.

PARTICIPANTID

Unique 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.

VERSIONNO

Version of fees for this ID

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

New Data Timing

When there is a participant configuration change

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("EFFECTIVEDATE", "LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast(
        {
            "MARKETFEEID": str,
            "PARTICIPANTID": pl.Categorical,
            "VERSIONNO": int,  # or pl.UInt16
        }
    )
    # Deduplicate
    # We want one row per ['EFFECTIVEDATE', 'MARKETFEEID', 'PARTICIPANTID', 'VERSIONNO']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(
        subset=["EFFECTIVEDATE", "MARKETFEEID", "PARTICIPANTID", "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:

• EFFECTIVEDATE

• MARKETFEEID

• PARTICIPANTID

• VERSIONNO

The transaction type is "INSERT-UPDATE", which means that you will likely see duplicate data with different values.

You should deduplicate by sorting based on LASTCHANGED, within each group of primary keys.

PDR Loader configuration is:

Report Name	Report Subtype	Version
SETTLEMENT_CONFIG	MARKET_FEE_EXCLUSION	1

This means that the CSV files start with metadata columns:

I,SETTLEMENT_CONFIG,MARKET_FEE_EXCLUSION,1,...
D,SETTLEMENT_CONFIG,MARKET_FEE_EXCLUSION,1,...

ROW_FILTER_TYPE is 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: SETTLEMENT_CONFIG

Copy as Markdown