MTPASA_REGIONAVAIL_TRK

The tracking table to assist in versioning of the region-aggregate offered PASA Availability data published to the MTPASA_REGIONAVAILABILITY table.

Column Schema

PUBLISH_DATETIME

Date Time the report was published.

SQL Type: DATE Polars Type: pl.Datetime()

This column is mandatory, and a primary key.

Example: 2026/09/01 09:00:00

ENDDATE

Last date of the report inclusive.

SQL Type: DATE Polars Type: pl.Date()

This column is not mandatory.

Example: 2029/09/01 00:00:00

Note that AEMO represents dates as datetimes with a midnight time component.

LATEST_OFFER_DATETIME

Date Time of the latest offer used in the report.

SQL Type: DATE Polars Type: pl.Datetime()

This column is not mandatory.

Example: 2026/09/01 08:50:34

STARTDATE

First date of the report inclusive.

SQL Type: DATE Polars Type: pl.Date()

This column is not mandatory.

Example: 2026/09/06 00:00:00

Note that AEMO represents dates as datetimes with a midnight time component.

New Data Timing

After each MT PASA run (every 3 hours during market business hours)

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"

# Dates are represented as datetimes at midnight

d_fmt = "%Y/%m/%d 00:00:00"

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("PUBLISH_DATETIME", "LATEST_OFFER_DATETIME").str.strptime(
            pl.Datetime, dt_s_fmt
        ),
        pl.col("ENDDATE", "STARTDATE").str.strptime(pl.Date, d_fmt),
    )
    .cast({"ENDDATE": pl.Date(), "STARTDATE": pl.Date()})
    # Deduplicate
    # We want one row per ['PUBLISH_DATETIME']
    .unique(subset=["PUBLISH_DATETIME"], keep="any")
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_MTPASA_REGIONAVAIL_TRK_FILE01_202609010000.zip

Table without MetadataTable with MetadataRaw Text without Metadata

PUBLISH_DATETIME	STARTDATE	ENDDATE	LATEST_OFFER_DATETIME
2026/09/01 09:00:00	2026/09/06 00:00:00	2029/09/01 00:00:00	2026/09/01 08:50:34
2026/09/01 12:00:00	2026/09/06 00:00:00	2029/09/01 00:00:00	2026/09/01 11:40:07
2026/09/12 18:00:00	2026/09/13 00:00:00	2029/09/08 00:00:00	2026/09/12 17:35:46
2026/09/01 15:00:00	2026/09/06 00:00:00	2029/09/01 00:00:00	2026/09/01 14:36:17
2026/09/01 18:00:00	2026/09/06 00:00:00	2029/09/01 00:00:00	2026/09/01 17:36:14
2026/09/02 09:00:00	2026/09/06 00:00:00	2029/09/01 00:00:00	2026/09/02 08:41:59
2026/09/02 12:00:00	2026/09/06 00:00:00	2029/09/01 00:00:00	2026/09/02 11:36:18
2026/09/05 12:00:00	2026/09/06 00:00:00	2029/09/01 00:00:00	2026/09/05 11:42:21
2026/09/05 18:00:00	2026/09/06 00:00:00	2029/09/01 00:00:00	2026/09/05 17:39:09
2026/09/02 15:00:00	2026/09/06 00:00:00	2029/09/01 00:00:00	2026/09/02 14:36:12
2026/09/02 18:00:00	2026/09/06 00:00:00	2029/09/01 00:00:00	2026/09/02 17:35:59
...

C	SETP.WORLD	DVD_MTPASA_REGIONAVAIL_TRK	AEMO	PUBLIC	2026/10/09	15:55:15	001791525315785	MONTHLY_ARCHIVE	001791525315785
I	MTPASA	REGIONAVAILTRK	1	PUBLISH_DATETIME	STARTDATE	ENDDATE	LATEST_OFFER_DATETIME
D	MTPASA	REGIONAVAILTRK	1	2026/09/01 09:00:00	2026/09/06 00:00:00	2029/09/01 00:00:00	2026/09/01 08:50:34
D	MTPASA	REGIONAVAILTRK	1	2026/09/01 12:00:00	2026/09/06 00:00:00	2029/09/01 00:00:00	2026/09/01 11:40:07
D	MTPASA	REGIONAVAILTRK	1	2026/09/12 18:00:00	2026/09/13 00:00:00	2029/09/08 00:00:00	2026/09/12 17:35:46
D	MTPASA	REGIONAVAILTRK	1	2026/09/01 15:00:00	2026/09/06 00:00:00	2029/09/01 00:00:00	2026/09/01 14:36:17
D	MTPASA	REGIONAVAILTRK	1	2026/09/01 18:00:00	2026/09/06 00:00:00	2029/09/01 00:00:00	2026/09/01 17:36:14
D	MTPASA	REGIONAVAILTRK	1	2026/09/02 09:00:00	2026/09/06 00:00:00	2029/09/01 00:00:00	2026/09/02 08:41:59
D	MTPASA	REGIONAVAILTRK	1	2026/09/02 12:00:00	2026/09/06 00:00:00	2029/09/01 00:00:00	2026/09/02 11:36:18
D	MTPASA	REGIONAVAILTRK	1	2026/09/05 12:00:00	2026/09/06 00:00:00	2029/09/01 00:00:00	2026/09/05 11:42:21
D	MTPASA	REGIONAVAILTRK	1	2026/09/05 18:00:00	2026/09/06 00:00:00	2029/09/01 00:00:00	2026/09/05 17:39:09
D	MTPASA	REGIONAVAILTRK	1	2026/09/02 15:00:00	2026/09/06 00:00:00	2029/09/01 00:00:00	2026/09/02 14:36:12
D	MTPASA	REGIONAVAILTRK	1	2026/09/02 18:00:00	2026/09/06 00:00:00	2029/09/01 00:00:00	2026/09/02 17:35:59
...
C	END OF REPORT	108

PUBLISH_DATETIME,STARTDATE,ENDDATE,LATEST_OFFER_DATETIME
"2026/09/01 09:00:00","2026/09/06 00:00:00","2029/09/01 00:00:00","2026/09/01 08:50:34"
"2026/09/01 12:00:00","2026/09/06 00:00:00","2029/09/01 00:00:00","2026/09/01 11:40:07"
"2026/09/12 18:00:00","2026/09/13 00:00:00","2029/09/08 00:00:00","2026/09/12 17:35:46"
"2026/09/01 15:00:00","2026/09/06 00:00:00","2029/09/01 00:00:00","2026/09/01 14:36:17"
"2026/09/01 18:00:00","2026/09/06 00:00:00","2029/09/01 00:00:00","2026/09/01 17:36:14"
"2026/09/02 09:00:00","2026/09/06 00:00:00","2029/09/01 00:00:00","2026/09/02 08:41:59"
"2026/09/02 12:00:00","2026/09/06 00:00:00","2029/09/01 00:00:00","2026/09/02 11:36:18"
"2026/09/05 12:00:00","2026/09/06 00:00:00","2029/09/01 00:00:00","2026/09/05 11:42:21"
"2026/09/05 18:00:00","2026/09/06 00:00:00","2029/09/01 00:00:00","2026/09/05 17:39:09"
"2026/09/02 15:00:00","2026/09/06 00:00:00","2029/09/01 00:00:00","2026/09/02 14:36:12"
"2026/09/02 18:00:00","2026/09/06 00:00:00","2029/09/01 00:00:00","2026/09/02 17:35:59"
...

Raw Text with Metadata

C,SETP.WORLD,DVD_MTPASA_REGIONAVAIL_TRK,AEMO,PUBLIC,2026/10/09,15:55:15,001791525315785,MONTHLY_ARCHIVE,001791525315785
I,MTPASA,REGIONAVAILTRK,1,PUBLISH_DATETIME,STARTDATE,ENDDATE,LATEST_OFFER_DATETIME
D,MTPASA,REGIONAVAILTRK,1,"2026/09/01 09:00:00","2026/09/06 00:00:00","2029/09/01 00:00:00","2026/09/01 08:50:34"
D,MTPASA,REGIONAVAILTRK,1,"2026/09/01 12:00:00","2026/09/06 00:00:00","2029/09/01 00:00:00","2026/09/01 11:40:07"
D,MTPASA,REGIONAVAILTRK,1,"2026/09/12 18:00:00","2026/09/13 00:00:00","2029/09/08 00:00:00","2026/09/12 17:35:46"
D,MTPASA,REGIONAVAILTRK,1,"2026/09/01 15:00:00","2026/09/06 00:00:00","2029/09/01 00:00:00","2026/09/01 14:36:17"
D,MTPASA,REGIONAVAILTRK,1,"2026/09/01 18:00:00","2026/09/06 00:00:00","2029/09/01 00:00:00","2026/09/01 17:36:14"
D,MTPASA,REGIONAVAILTRK,1,"2026/09/02 09:00:00","2026/09/06 00:00:00","2029/09/01 00:00:00","2026/09/02 08:41:59"
D,MTPASA,REGIONAVAILTRK,1,"2026/09/02 12:00:00","2026/09/06 00:00:00","2029/09/01 00:00:00","2026/09/02 11:36:18"
D,MTPASA,REGIONAVAILTRK,1,"2026/09/05 12:00:00","2026/09/06 00:00:00","2029/09/01 00:00:00","2026/09/05 11:42:21"
D,MTPASA,REGIONAVAILTRK,1,"2026/09/05 18:00:00","2026/09/06 00:00:00","2029/09/01 00:00:00","2026/09/05 17:39:09"
D,MTPASA,REGIONAVAILTRK,1,"2026/09/02 15:00:00","2026/09/06 00:00:00","2029/09/01 00:00:00","2026/09/02 14:36:12"
D,MTPASA,REGIONAVAILTRK,1,"2026/09/02 18:00:00","2026/09/06 00:00:00","2029/09/01 00:00:00","2026/09/02 17:35:59"
...
C,"END OF REPORT",108

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 8 of 2024. The most recent available data is month 9 of 2026.

Every month in between has a file. (No gaps.)

Data Size

Each year of data is approximately 18 KB when zipped, 180 KB as unzipped CSV.

Deduplication and PDR Loader Configuration

The primary keys are:

PUBLISH_DATETIME

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
MTPASA	REGIONAVAILTRK	1

This means that the CSV files start with metadata columns:

I,MTPASA,REGIONAVAILTRK,1,...
D,MTPASA,REGIONAVAILTRK,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: MTPASA