BILLINGGENDATA

BILLINGGENDATA shows the total energy sold and purchased per participant transmission connection point for a billing period. The table is populated for participants using Data Model 5.2. For participants using Data Model 5.3 or higher, BILLING_ENERGY_TRANSACTIONS AND BILLING_ENERGY_GENSET_DETAIL replace BILLINGGENDATA.

Deprecation Warning

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

BILLINGGENDATA data is confidential to the the relevant participant.

Source

BILLINGGENDATA is populated by the posting of a billing run, being several times each week.

Volume

The number of records depends on the number of transmission ConnectionPointIDs a Participant may have sold energy from per billrunno. An indicative maximum is approximately 15 records inserted per billrunno, or about 165 records inserted per week.

BILLINGGENDATA is confidential to the the relevant participant.

Column Schema

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.

CONNECTIONPOINTID

Connection point identifier

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

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.

AGGREGATEENERGY

Aggregate energy sold, in MWh

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

This column is not mandatory.

DUID

not populated

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

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

MDA

Metering Data Agent supplying data

SQL Type: VARCHAR2(10) Polars Type: str

This column is not mandatory.

PURCHASEDENERGY

Amount of energy purchased in MWh

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

This column is not mandatory.

PURCHASES

$ outgoing

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

This column is not mandatory.

SALES

$ income

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

This column is not mandatory.

STATIONID

not populated

For more information, see the dedicated documentation page.

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

This column is not mandatory.

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(
        {
            "BILLRUNNO": int,  # or pl.UInt16
            "CONNECTIONPOINTID": pl.Categorical,
            "CONTRACTYEAR": int,  # or pl.UInt16
            "PARTICIPANTID": pl.Categorical,
            "WEEKNO": int,  # or pl.UInt16
            "AGGREGATEENERGY": pl.Float64,
            "DUID": pl.Categorical,
            "MDA": str,
            "PURCHASEDENERGY": pl.Float64,
            "PURCHASES": pl.Float64,
            "SALES": pl.Float64,
            "STATIONID": pl.Categorical,
        }
    )
    # Deduplicate
    # We want one row per ['BILLRUNNO', 'CONNECTIONPOINTID', 'CONTRACTYEAR', 'PARTICIPANTID', 'WEEKNO']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(
        subset=[
            "BILLRUNNO",
            "CONNECTIONPOINTID",
            "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:

• BILLRUNNO

• CONNECTIONPOINTID

• 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	GENDATA	5

This means that the CSV files start with metadata columns:

I,BILLING,GENDATA,5,...
D,BILLING,GENDATA,5,...

Indexes

There is more than one index.

Index 1:

• LASTCHANGED

Index 2:

• PARTICIPANTID

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: HISTORICAL TABLES

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