BILLING_WDR

Billing WDR Transaction Weekly Summary

Column Schema

BILLRUNNO

Billing run number identifier

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

This column is mandatory, and a primary key.

CONTRACTYEAR

Contract year of the Billing run

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

This column is mandatory, and a primary key.

PARTICIPANTID

DRSP or FRMP Participant Identifier

For more information, see the dedicated documentation page.

SQL Type: VARCHAR2(20) Polars Type: pl.Categorical

This column is mandatory, and a primary key.

WEEKNO

Week number of the Billing run

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.

WDR_CREDIT_AMOUNT

WDR credit transaction amount

SQL Type: NUMBER(18,8) Polars Type: pl.Float64

This column is not mandatory.

WDR_DEBIT_AMOUNT

WDR debit transaction amount

SQL Type: NUMBER(18,8) Polars Type: pl.Float64

This column is not mandatory.

New Data Timing

Posting of a billing 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

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",
    ).cast(
        {
            "BILLRUNNO": int,  # or pl.UInt16
            "CONTRACTYEAR": int,  # or pl.UInt16
            "PARTICIPANTID": pl.Categorical,
            "WEEKNO": int,  # or pl.UInt16
            "WDR_CREDIT_AMOUNT": pl.Float64,
            "WDR_DEBIT_AMOUNT": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['BILLRUNNO', 'CONTRACTYEAR', 'PARTICIPANTID', 'WEEKNO']
    .unique(subset=["BILLRUNNO", "CONTRACTYEAR", "PARTICIPANTID", "WEEKNO"], keep="any")
)

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

• CONTRACTYEAR

• 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	WDR	1

This means that the CSV files start with metadata columns:

I,BILLING,WDR,1,...
D,BILLING,WDR,1,...

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

Copy as Markdown