REGION

REGION sets out valid region IDs.

REGION data is public, so is available to all participants.

Source

REGION updates if a change is ever made to a region. This table is static data and is likely to change very infrequently.

Column Schema

REGIONID

Differentiates this region from all other regions

For more information, see the dedicated documentation page.

The SQL type is VARCHAR2(10). This is actually a factor/enum, with values:

QLD1
VIC1
TAS1
NSW1
SA1
SNOWY1

This column is mandatory, and a primary key.

DESCRIPTION

Full description of region

SQL Type: VARCHAR2(64) Polars Type: str

This column is not mandatory.

Examples:

New South Wales
Queensland Region
Snowy Regions
South Australia Region
Tasmanian Region
Victoria

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: 2004/06/11 10:43:05

REGIONSTATUS

Status of the region e.g. working, inactive, archive.

SQL Type: VARCHAR2(8) Polars Type: pl.Categorical

This column is not mandatory.

Examples:

Active
INACTIVE

New Data Timing

When there is change to a region

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(
        {
            "REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
            "DESCRIPTION": str,
            "REGIONSTATUS": pl.Categorical,
        }
    )
    # Deduplicate
    # We want one row per ['REGIONID']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(subset=["REGIONID"], keep="last")
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_REGION_FILE01_202609010000.zip

Table without MetadataTable with MetadataRaw Text without Metadata

REGIONID	DESCRIPTION	REGIONSTATUS	LASTCHANGED
QLD1	Queensland Region	Active	2004/06/11 10:43:05
NSW1	New South Wales	Active	2004/06/11 10:43:05
VIC1	Victoria	Active	2004/06/11 10:43:05
SA1	South Australia Region	Active	2004/06/11 10:43:05
TAS1	Tasmanian Region	Active	2005/05/16 13:25:25
SNOWY1	Snowy Regions	INACTIVE	2004/06/11 10:43:05

C	SETP.WORLD	DVD_REGION	AEMO	PUBLIC	2026/10/09	16:28:01	001791527281444	MONTHLY_ARCHIVE	001791527281444
I	MARKET_CONFIG	REGION	1	REGIONID	DESCRIPTION	REGIONSTATUS	LASTCHANGED
D	MARKET_CONFIG	REGION	1	QLD1	Queensland Region	Active	2004/06/11 10:43:05
D	MARKET_CONFIG	REGION	1	NSW1	New South Wales	Active	2004/06/11 10:43:05
D	MARKET_CONFIG	REGION	1	VIC1	Victoria	Active	2004/06/11 10:43:05
D	MARKET_CONFIG	REGION	1	SA1	South Australia Region	Active	2004/06/11 10:43:05
D	MARKET_CONFIG	REGION	1	TAS1	Tasmanian Region	Active	2005/05/16 13:25:25
D	MARKET_CONFIG	REGION	1	SNOWY1	Snowy Regions	INACTIVE	2004/06/11 10:43:05
C	END OF REPORT	9

REGIONID,DESCRIPTION,REGIONSTATUS,LASTCHANGED
QLD1,"Queensland Region",Active,"2004/06/11 10:43:05"
NSW1,"New South Wales",Active,"2004/06/11 10:43:05"
VIC1,Victoria,Active,"2004/06/11 10:43:05"
SA1,"South Australia Region",Active,"2004/06/11 10:43:05"
TAS1,"Tasmanian Region",Active,"2005/05/16 13:25:25"
SNOWY1,"Snowy Regions",INACTIVE,"2004/06/11 10:43:05"

Raw Text with Metadata

C,SETP.WORLD,DVD_REGION,AEMO,PUBLIC,2026/10/09,16:28:01,001791527281444,MONTHLY_ARCHIVE,001791527281444
I,MARKET_CONFIG,REGION,1,REGIONID,DESCRIPTION,REGIONSTATUS,LASTCHANGED
D,MARKET_CONFIG,REGION,1,QLD1,"Queensland Region",Active,"2004/06/11 10:43:05"
D,MARKET_CONFIG,REGION,1,NSW1,"New South Wales",Active,"2004/06/11 10:43:05"
D,MARKET_CONFIG,REGION,1,VIC1,Victoria,Active,"2004/06/11 10:43:05"
D,MARKET_CONFIG,REGION,1,SA1,"South Australia Region",Active,"2004/06/11 10:43:05"
D,MARKET_CONFIG,REGION,1,TAS1,"Tasmanian Region",Active,"2005/05/16 13:25:25"
D,MARKET_CONFIG,REGION,1,SNOWY1,"Snowy Regions",INACTIVE,"2004/06/11 10:43:05"
C,"END OF REPORT",9

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 1 of 2015. 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 6 KB when zipped, 60 KB as unzipped CSV.

Deduplication and PDR Loader Configuration

The primary keys are:

REGIONID

The transaction type is "INSERT-UPDATE", which means that you will likely see duplicate data with different values.

You should deduplicate by sorting based on LASTCHANGED, within each group of primary keys.

PDR Loader configuration is:

Report Name	Report Subtype	Version
MARKET_CONFIG	REGION	1

This means that the CSV files start with metadata columns:

I,MARKET_CONFIG,REGION,1,...
D,MARKET_CONFIG,REGION,1,...

ROW_FILTER_TYPE is LASTCHANGED

Indexes

LASTCHANGED

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: MARKET_CONFIG