BILLINGIRAUCSURPLUS

BILLINGIRAUCSURPLUS supports the Settlements Residue Auction, by showing the weekly billing Interconnector Residue (IR) payments as calculated for each bill run for Network Service Providers (NSPs) from the amount not auctioned.

Source

Obsolete

Volume

This view contains a maximum of 30, 000 records per year.

Column Schema

BILLRUNNO

The sequential number of a billing run

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

This column is mandatory, and a primary key.

CONTRACTID

SRA Contract unique identifier

SQL Type: VARCHAR2(30) Polars Type: pl.Categorical

This column is mandatory, and a primary key.

CONTRACTYEAR

SRA Contracted Year (calendar year)

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

This column is mandatory, and a primary key.

FROMREGIONID

Nominated source region for Interconnector

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

This column is mandatory, and a primary key.

INTERCONNECTORID

Contracted Interconnector

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.

WEEKNO

Week no within the contract year. Week no 1 is the week containing 1st January

Warning

There may be 53 weeks in a year, depending on how weeks align to the year start/end.

SQL Type: NUMBER(2,0) Polars Type: pl.UInt8

This column is mandatory, and a primary key.

ADJUSTMENT

Adjustment allocated to participant

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

This column is not mandatory.

LASTCHANGED

Date and time this record was last modified

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

QUARTER

Residue Contract Quarter

SQL Type: NUMBER(2,0) Polars Type: pl.Int8

This column is not mandatory.

RESIDUEYEAR

Year of the Residue Contract; may differ from the calendar year at week 1.

SQL Type: NUMBER(4,0) Polars Type: pl.Int16

This column is not mandatory.

TOTALRESIDUES

Total residues allocated to participant

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

This column is not mandatory.

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("LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast(
        {
            "BILLRUNNO": int,  # or pl.UInt16
            "CONTRACTID": pl.Categorical,
            "CONTRACTYEAR": int,  # or pl.UInt16
            "FROMREGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1"]),
            "INTERCONNECTORID": pl.Categorical,
            "PARTICIPANTID": pl.Categorical,
            "WEEKNO": int,  # or pl.UInt8
            "ADJUSTMENT": pl.Float64,
            "QUARTER": int,  # or pl.Int8
            "RESIDUEYEAR": int,  # or pl.Int16
            "TOTALRESIDUES": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['BILLRUNNO', 'CONTRACTID', 'CONTRACTYEAR', 'FROMREGIONID', 'INTERCONNECTORID', 'PARTICIPANTID', 'WEEKNO']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(
        subset=[
            "BILLRUNNO",
            "CONTRACTID",
            "CONTRACTYEAR",
            "FROMREGIONID",
            "INTERCONNECTORID",
            "PARTICIPANTID",
            "WEEKNO",
        ],
        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:

• BILLRUNNO

• CONTRACTID

• CONTRACTYEAR

• FROMREGIONID

• INTERCONNECTORID

• PARTICIPANTID

• WEEKNO

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
BILLING	IRAUCSURPLUS	5

This means that the CSV files start with metadata columns:

I,BILLING,IRAUCSURPLUS,5,...
D,BILLING,IRAUCSURPLUS,5,...

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: BILLING_RUN

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