BILLING_ENERGY_TRAN_SAPS

The SAP Billing Transaction Details for the Participants

Column Schema

BILLRUNNO

The Billing RunNo

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

This column is mandatory, and a primary key.

CONTRACTYEAR

The Billing Contract Year

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

This column is mandatory, and a primary key.

PARTICIPANTID

The SAP Participant ID

For more information, see the dedicated documentation page.

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

This column is mandatory, and a primary key.

TNI

The SAPS Connection Point ID

SQL Type: VARCHAR2(20) Polars Type: str

This column is mandatory, and a primary key.

WEEKNO

The Billing 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.

CONSUMED_ENERGY_COST

The Cost of the Consumed Energy

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

This column is not mandatory.

CONSUMED_ENERGY_MWH

The Energy MWh Consumed for that TNI for the Participant Id in that Billing Week

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

This column is not mandatory.

LASTCHANGED

The Last datetime record is updated

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

REGIONID

The Region ID associated with the TNI

For more information, see the dedicated documentation page.

The SQL type is VARCHAR2(20). This is actually a factor/enum, with values:

• QLD1

• VIC1

• TAS1

• NSW1

• SA1

• SNOWY1

This column is not mandatory.

SENTOUT_ENERGY_COST

The Cost of the Sent Out Energy

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

This column is not mandatory.

SENTOUT_ENERGY_MWH

The Energy MWh Sent Out for the TNI for the Participant Id in that Billing Week

SQL Type: NUMBER(18,8) 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
            "CONTRACTYEAR": int,  # or pl.UInt16
            "PARTICIPANTID": pl.Categorical,
            "TNI": str,
            "WEEKNO": int,  # or pl.UInt16
            "CONSUMED_ENERGY_COST": pl.Float64,
            "CONSUMED_ENERGY_MWH": pl.Float64,
            "REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
            "SENTOUT_ENERGY_COST": pl.Float64,
            "SENTOUT_ENERGY_MWH": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['BILLRUNNO', 'CONTRACTYEAR', 'PARTICIPANTID', 'TNI', 'WEEKNO']
    # Tie-breaker: greatest value of LASTCHANGED
    .sort(LASTCHANGED, descending=False)
    .unique(
        subset=["BILLRUNNO", "CONTRACTYEAR", "PARTICIPANTID", "TNI", "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

• CONTRACTYEAR

• PARTICIPANTID

• TNI

• 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	ENERGY_TRAN_SAPS	1

This means that the CSV files start with metadata columns:

I,BILLING,ENERGY_TRAN_SAPS,1,...
D,BILLING,ENERGY_TRAN_SAPS,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

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