BILLADJUSTMENTS

Deprecation Warning

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

BILLADJUSTMENTS is confidential, and is available only to the relevant participant.

Source

Ad hoc

Column Schema

ADJBILLRUNNO

Billing run number of the new revised billing run for this adjustment

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

This column is mandatory, and a primary key.

ADJCONTRACTYEAR

The contract year of the new revised billing run for this adjustment

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

This column is mandatory, and a primary key.

ADJWEEKNO

Week number of the new revised billing run for this adjustment

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

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

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.

ADJAMOUNT

The total bill figure for the new revised billing run

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

This column is not mandatory.

BILLRUNNO

The sequential number of a billing run

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

This column is not mandatory.

INTERESTAMOUNT

The total interest payable for this adjustment

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

This column is not mandatory.

IRN

Interest rate applying to the new amount

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

This column is not mandatory.

IRP

Interest rate applying to the principal amount

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

This column is not mandatory.

LASTCHANGED

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

LRS

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

This column is not mandatory.

OFS

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

This column is not mandatory.

PARTICIPANTTYPE

Participant type Generator/Customer

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

This column is not mandatory.

PREVAMOUNT

Prior account

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

This column is not mandatory.

PRS

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

This column is not mandatory.

New Data Timing

Ad hoc

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(
        {
            "ADJBILLRUNNO": int,  # or pl.Int16
            "ADJCONTRACTYEAR": int,  # or pl.Int16
            "ADJWEEKNO": int,  # or pl.Int16
            "CONTRACTYEAR": int,  # or pl.UInt16
            "PARTICIPANTID": pl.Categorical,
            "WEEKNO": int,  # or pl.UInt16
            "ADJAMOUNT": pl.Float64,
            "BILLRUNNO": int,  # or pl.UInt16
            "INTERESTAMOUNT": pl.Float64,
            "IRN": pl.Float64,
            "IRP": pl.Float64,
            "LRS": pl.Float64,
            "OFS": pl.Float64,
            "PARTICIPANTTYPE": pl.Categorical,
            "PREVAMOUNT": pl.Float64,
            "PRS": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['ADJBILLRUNNO', 'ADJCONTRACTYEAR', 'ADJWEEKNO', 'CONTRACTYEAR', 'PARTICIPANTID', 'WEEKNO']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(
        subset=[
            "ADJBILLRUNNO",
            "ADJCONTRACTYEAR",
            "ADJWEEKNO",
            "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:

ADJBILLRUNNO
ADJCONTRACTYEAR
ADJWEEKNO
CONTRACTYEAR
PARTICIPANTID
WEEKNO

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.