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BILLINGFINANCIALADJUSTMENTS

BILLINGFINANCIALADJUSTMENTS contains any manual adjustments included in the billing run.

BILLINGFINANCIALADJUSTMENTS data is confidential to the relevant participant.

Source

BILLINGFINANCIALADJUSTMENTS is populated by the posting of a billing run, being several times each week. The insertion of a manual adjustment in a billing run is infrequent.

Volume

Infrequent and, if included in a billing run, low volume. An indicative maximum is 15 records inserted.

Column Schema

ADJUSTMENTITEM

Description of the adjustment being made

SQL Type: VARCHAR2(64) Polars Type: str

This column is mandatory, and a primary key.

BILLRUNNO

Unique run no within a given contract year and week no

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

This column is mandatory, and a primary key.

CONTRACTYEAR

AEMO Contract Year number starting in week containing 1st January

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

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(3,0) Polars Type: pl.UInt16

This column is mandatory, and a primary key.

AMOUNT

The amount of the manual adjustment line item

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

This column is not mandatory.

BAS_CLASS

The BAS classification of the manual adjustment line item.

SQL Type: VARCHAR2(30) Polars Type: str

This column is not mandatory.

FINANCIALCODE

The GL financial code of the manual adjustment line item. Used internally by AEMO systems.

SQL Type: NUMBER(10,0) Polars Type: pl.Int64

This column is not mandatory.

LASTCHANGED

Last date and time the 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

PARTICIPANTTYPE

Not Used

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

This column is unused.

VALUE

Not Used

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

This column is unused.

New Data Timing

After posting of a billing run (several times each week)

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(
        {
            "ADJUSTMENTITEM": str,
            "BILLRUNNO": int,  # or pl.UInt16
            "CONTRACTYEAR": int,  # or pl.UInt16
            "PARTICIPANTID": pl.Categorical,
            "WEEKNO": int,  # or pl.UInt16
            "AMOUNT": pl.Float64,
            "BAS_CLASS": str,
            "FINANCIALCODE": int,  # or pl.Int64
            "PARTICIPANTTYPE": pl.Categorical,
            "VALUE": pl.Float64,
        }
    )
    # Drop deprecated/unused columns
    .drop("PARTICIPANTTYPE", "VALUE")
    # Deduplicate
    # We want one row per ['ADJUSTMENTITEM', 'BILLRUNNO', 'CONTRACTYEAR', 'PARTICIPANTID', 'WEEKNO']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(
        subset=[
            "ADJUSTMENTITEM",
            "BILLRUNNO",
            "CONTRACTYEAR",
            "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:

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 FINANCIALADJUSTMENTS 5

This means that the CSV files start with metadata columns:

I,BILLING,FINANCIALADJUSTMENTS,5,...
D,BILLING,FINANCIALADJUSTMENTS,5,...

Indexes

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