SETMARKETFEES

SETMARKETFEES shows payments for market fees for each settlement date.

SETMARKETFEES is confidential data.

Source

SETMARKETFEES updates with each settlement run.

Column Schema

MARKETFEEID

Market fee identifier (e.g. V_EST)

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

This column is mandatory, and a primary key.

PARTICIPANTCATEGORYID

The participant category that the market fee recovery pertains to. Corresponds to the PARTICIPANTCATEGORYID column of the PARTICIPANT_BANDFEE_CATEGORYALLOC_C_V view for BAND$ type fees, or to the MARKETFEETYPE column of the MARKETFEE_P_V view for all other fee types.

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

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.

PERIODID

Settlements Trading Interval.

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

This column is mandatory, and a primary key.

RUNNO

Settlement run no

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

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

ENERGY

Energy amount for variable fees

SQL Type: NUMBER(16,6) Polars Type: pl.Float64

This column is not mandatory.

FEERATE

The rate applied to this fee for the participant at the settlement date

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

This column is not mandatory.

FEEUNITS

The number of units applicable to this fee for the participant, in the trading interval.

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

This column is not mandatory.

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

MARKETFEEVALUE

Fee charge

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

This column is not mandatory.

METER_SUBTYPE

The Meter Sub Type values are ACE, ASOE or ALL. ACE represent ACE_MWH value or ASOE represent ASOE_MWH value and ALL represent sum of ACE_MWh and ASOE_MWh

The SQL type is VARCHAR2(20). This is actually a factor/enum, with values:

• ACE

• ASOE

• ALL

This column is not mandatory.

METER_TYPE

The Energy Type for the Market Fees Calculation. E.g of Meter Types are CUSTOMER, GENERATOR, NREG, BDU etc. If Meter Type is mentioned as ALL then all the Meter Types for that Participant Category will be used in the Fee calculation

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

This column is not mandatory.

New Data Timing

Daily Billing Run & Posting a PRELIM/FINAL and REVISE 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(
        {
            "MARKETFEEID": pl.Categorical,
            "PARTICIPANTCATEGORYID": pl.Categorical,
            "PARTICIPANTID": pl.Categorical,
            "PERIODID": int,  # or pl.Int16
            "RUNNO": int,  # or pl.UInt16
            "ENERGY": pl.Float64,
            "FEERATE": pl.Float64,
            "FEEUNITS": pl.Float64,
            "MARKETFEEVALUE": pl.Float64,
            "METER_SUBTYPE": pl.Enum(["ACE", "ASOE", "ALL"]),
            "METER_TYPE": pl.Categorical,
        }
    )
    # Deduplicate
    # We want one row per ['MARKETFEEID', 'PARTICIPANTCATEGORYID', 'PARTICIPANTID', 'PERIODID', 'RUNNO', 'SETTLEMENTDATE']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(
        subset=[
            "MARKETFEEID",
            "PARTICIPANTCATEGORYID",
            "PARTICIPANTID",
            "PERIODID",
            "RUNNO",
            "SETTLEMENTDATE",
        ],
        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:

• MARKETFEEID

• PARTICIPANTCATEGORYID

• PARTICIPANTID

• PERIODID

• RUNNO

• SETTLEMENTDATE

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	MARKETFEES	7

This means that the CSV files start with metadata columns:

I,SETTLEMENTS,MARKETFEES,7,...
D,SETTLEMENTS,MARKETFEES,7,...

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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