SETLULOADPAYMENT

SETLULOADPAYMENT shows specific payment details for rapid unit load services by period.

Deprecation Warning

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

SETLULOADPAYMENT became unused when Ancillary Services Review was implemented. For more details, see Change Notice 126.

SETLULOADPAYMENT data is confidential to each participant.

Source

SETLULOADPAYMENT is unused; was updated with each settlement run.

Column Schema

CONTRACTID

AS Contract Identifier

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

This column is mandatory, and a primary key.

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

Trading Interval

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.

ACR

Dispatch target

SQL Type: NUMBER(6,2) Polars Type: pl.Float64

This column is not mandatory.

BLOCKSIZE

Indicates how much of the unit at one given time is available for the ancillary service.

SQL Type: NUMBER(4,0) Polars Type: pl.UInt16

This column is not mandatory.

CCPRICE

Compensation Cap

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

This column is not mandatory.

COMPENSATIONPAYMENT

Compensation Payment

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

This column is not mandatory.

CONTRACTVERSIONNO

Contract Version No.

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

This column is not mandatory.

DUID

Dispatchable Unit Identifier

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.

ENABLINGPRICE

Enabling Price

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

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

This column is not mandatory.

UNITEXCESSGEN

Excess Generation

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

This column is not mandatory.

UNITOUTPUT

Unit output.

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

This column is not mandatory.

USAGEPAYMENT

Usage Payment

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

This column is not mandatory.

USAGEPRICE

Usage Price

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", "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
            "ACR": pl.Float64,
            "BLOCKSIZE": int,  # or pl.UInt16
            "CCPRICE": pl.Float64,
            "COMPENSATIONPAYMENT": pl.Float64,
            "CONTRACTVERSIONNO": int,  # or pl.UInt16
            "DUID": pl.Categorical,
            "EBP": pl.Float64,
            "ENABLINGPAYMENT": pl.Float64,
            "ENABLINGPRICE": pl.Float64,
            "OFFERVERSIONNO": int,  # or pl.UInt16
            "REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
            "RRP": pl.Float64,
            "TLF": pl.Float64,
            "UNITEXCESSGEN": pl.Float64,
            "UNITOUTPUT": pl.Float64,
            "USAGEPAYMENT": pl.Float64,
            "USAGEPRICE": 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.