SETAGCPAYMENT

SETAGCPAYMENT sets out specific payment details for Automatic Generation Control (AGC) services by period.

Deprecation Warning

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

SETAGCPAYMENT data is confidential to the relevant participant

Source

SETAGCPAYMENT updates with each settlement run.

Column Schema

CONTRACTID

Contract Identifier

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

Settlement Period Identifier

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

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.

CLEAREDMW

Cleared MW of Unit in Enabled Dispatch period

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

This column is not mandatory.

CONTRACTVERSIONNO

AS contract version no

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

This column is not mandatory.

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 not mandatory.

Example: W/HOE#2

EBP

Eligible Bid Price

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

This column is not mandatory.

ENABLINGPAYMENT

Enabling Payment

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

This column is not mandatory.

INITIALMW

Initial MW of Unit in Enabled Dispatch period

Warning

In AEMO data, datetimes usually refer to the end of an interval. Power values usually do too. However this column refers to the start of the interval. So you need to subtract 5 minutes from SETTLEMENTDATE to align it with INITIALMW.

SQL Type: NUMBER(15,5) 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

OFFERDATE

Re-offer offer date

Warning

Despite what the name suggests, this is a datetime, not a date.

SQL Type: DATE Polars Type: pl.Datetime()

This column is not mandatory.

Example: 2026/01/20 00:05:00

OFFERVERSIONNO

Re-Offer Version No.

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

This column is not mandatory.

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.

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", "OFFERDATE").str.strptime(
            pl.Datetime, dt_s_fmt
        ),
    )
    .cast(
        {
            "CONTRACTID": pl.Categorical,
            "PARTICIPANTID": pl.Categorical,
            "PERIODID": int,  # or pl.Int16
            "VERSIONNO": int,  # or pl.UInt16
            "CLEAREDMW": pl.Float64,
            "CONTRACTVERSIONNO": int,  # or pl.UInt16
            "DUID": pl.Categorical,
            "EBP": pl.Float64,
            "ENABLINGPAYMENT": pl.Float64,
            "INITIALMW": pl.Float64,
            "OFFERVERSIONNO": int,  # or pl.UInt16
            "REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
            "RRP": pl.Float64,
            "TLF": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['CONTRACTID', 'PARTICIPANTID', 'PERIODID', 'SETTLEMENTDATE', 'VERSIONNO']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(
        subset=[
            "CONTRACTID",
            "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:

CONTRACTID
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.