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BILLING_NMAS_TST_RECVRY_TRK

BILLING_NMAS_TEST_RECVRY_TRK tracks the energy data used to allocate the test payment recovery over the recovery period. This Table may also be used for NSCAS and Type 1 transitional services procured by AEMO under the ISF framework during 2025 and prior to the implementation of all system changes. During this time descriptions in these tables may not be correct.

Column Schema

BILLRUNNO

The current Billing RunNo for the week

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

This column is mandatory, and a primary key.

Examples: 10, 16

CONTRACTYEAR

AEMO Contract Year number starting in week containing 1 January

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

This column is mandatory, and a primary key.

Example: 2026

RECOVERY_BILLRUNNO

Billing RunNo for energy data used in recovery calculation

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

This column is mandatory, and a primary key.

Examples: 11, 15

RECOVERY_CONTRACTYEAR

AEMO Contract Year for energy data used in recovery calculation

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

This column is mandatory, and a primary key.

Example: 2026

RECOVERY_WEEKNO

Week no for energy data used in recovery calculation

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

This column is mandatory, and a primary key.

Examples: 5, 27

WEEKNO

Week no within the contract year. Week no 1 is the week containing 1 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.

Examples: 28, 31

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
            "RECOVERY_BILLRUNNO": int,  # or pl.Int16
            "RECOVERY_CONTRACTYEAR": int,  # or pl.Int16
            "RECOVERY_WEEKNO": int,  # or pl.Int16
            "WEEKNO": int,  # or pl.UInt16
        }
    )
    # Deduplicate
    # We want one row per ['BILLRUNNO', 'CONTRACTYEAR', 'RECOVERY_BILLRUNNO', 'RECOVERY_CONTRACTYEAR', 'RECOVERY_WEEKNO', 'WEEKNO']
    .unique(
        subset=[
            "BILLRUNNO",
            "CONTRACTYEAR",
            "RECOVERY_BILLRUNNO",
            "RECOVERY_CONTRACTYEAR",
            "RECOVERY_WEEKNO",
            "WEEKNO",
        ],
        keep="any",
    )
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_BILLING_NMAS_TST_RECVRY_TRK_FILE01_202608010000.zip

CONTRACTYEARWEEKNOBILLRUNNORECOVERY_CONTRACTYEARRECOVERY_WEEKNORECOVERY_BILLRUNNO
2026281320262113
2026281320262212
2026281320262313
2026281320262414
2026281320262514
2026281320262613
2026281320262711
202631102026513
202631102026614
202631102026715
202631102026813
...

CSETP.WORLDDVD_BILLING_NMAS_TST_RECVRY_TRKAEMOPUBLIC2026/09/2513:27:42001790306862206MONTHLY_ARCHIVE001790306862206
IBILLINGNMAS_TST_RECVRY_TRK1CONTRACTYEARWEEKNOBILLRUNNORECOVERY_CONTRACTYEARRECOVERY_WEEKNORECOVERY_BILLRUNNO
DBILLINGNMAS_TST_RECVRY_TRK12026281320262113
DBILLINGNMAS_TST_RECVRY_TRK12026281320262212
DBILLINGNMAS_TST_RECVRY_TRK12026281320262313
DBILLINGNMAS_TST_RECVRY_TRK12026281320262414
DBILLINGNMAS_TST_RECVRY_TRK12026281320262514
DBILLINGNMAS_TST_RECVRY_TRK12026281320262613
DBILLINGNMAS_TST_RECVRY_TRK12026281320262711
DBILLINGNMAS_TST_RECVRY_TRK1202631102026513
DBILLINGNMAS_TST_RECVRY_TRK1202631102026614
DBILLINGNMAS_TST_RECVRY_TRK1202631102026715
DBILLINGNMAS_TST_RECVRY_TRK1202631102026813
...
CEND OF REPORT56

CONTRACTYEAR,WEEKNO,BILLRUNNO,RECOVERY_CONTRACTYEAR,RECOVERY_WEEKNO,RECOVERY_BILLRUNNO
2026,28,13,2026,21,13
2026,28,13,2026,22,12
2026,28,13,2026,23,13
2026,28,13,2026,24,14
2026,28,13,2026,25,14
2026,28,13,2026,26,13
2026,28,13,2026,27,11
2026,31,10,2026,5,13
2026,31,10,2026,6,14
2026,31,10,2026,7,15
2026,31,10,2026,8,13
...

C,SETP.WORLD,DVD_BILLING_NMAS_TST_RECVRY_TRK,AEMO,PUBLIC,2026/09/25,13:27:42,001790306862206,MONTHLY_ARCHIVE,001790306862206
I,BILLING,NMAS_TST_RECVRY_TRK,1,CONTRACTYEAR,WEEKNO,BILLRUNNO,RECOVERY_CONTRACTYEAR,RECOVERY_WEEKNO,RECOVERY_BILLRUNNO
D,BILLING,NMAS_TST_RECVRY_TRK,1,2026,28,13,2026,21,13
D,BILLING,NMAS_TST_RECVRY_TRK,1,2026,28,13,2026,22,12
D,BILLING,NMAS_TST_RECVRY_TRK,1,2026,28,13,2026,23,13
D,BILLING,NMAS_TST_RECVRY_TRK,1,2026,28,13,2026,24,14
D,BILLING,NMAS_TST_RECVRY_TRK,1,2026,28,13,2026,25,14
D,BILLING,NMAS_TST_RECVRY_TRK,1,2026,28,13,2026,26,13
D,BILLING,NMAS_TST_RECVRY_TRK,1,2026,28,13,2026,27,11
D,BILLING,NMAS_TST_RECVRY_TRK,1,2026,31,10,2026,5,13
D,BILLING,NMAS_TST_RECVRY_TRK,1,2026,31,10,2026,6,14
D,BILLING,NMAS_TST_RECVRY_TRK,1,2026,31,10,2026,7,15
D,BILLING,NMAS_TST_RECVRY_TRK,1,2026,31,10,2026,8,13
...
C,"END OF REPORT",56

Availability

Visibility: Public

This table is in the public monthly MMSDM archive.

I have collated the list of all those files into one place, in this CSV metadata file. (Columns year, month, url, compressed_size in bytes).

The earliest available data is month 12 of 2024. The most recent available data is month 8 of 2026.

Warning

There are some months with no files.

Data Size

Each year of data is approximately 7 KB when zipped, 74 KB as unzipped CSV.

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 NMAS_TST_RECVRY_TRK 1

This means that the CSV files start with metadata columns:

I,BILLING,NMAS_TST_RECVRY_TRK,1,...
D,BILLING,NMAS_TST_RECVRY_TRK,1,...

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