PASA_CONTINGENCY_DEFINITION

PASA_CONTINGENCY_DEFINITION shows the contingency details used by PD and ST PASA.

Column Schema

CONTINGENCYID

The contingency identifier

SQL Type: VARCHAR2(20) Polars Type: str

This column is mandatory, and a primary key.

EFFECTIVEDATE

The effective date for this contingency

Warning

Despite what the name suggests, this is a datetime, not a date.

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

This column is mandatory, and a primary key.

Example: 2026/01/20 00:05:00

VERSIONNO

Version number for the Effective date

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

This column is mandatory, and a primary key.

CONTINGENCYDESCRIPTION

The description for this contingency

SQL Type: VARCHAR2(100) Polars Type: str

This column is not mandatory.

LASTCHANGED

Date time this record was created

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

New Data Timing

On Change

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("EFFECTIVEDATE", "LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast(
        {
            "CONTINGENCYID": str,
            "VERSIONNO": int,  # or pl.UInt16
            "CONTINGENCYDESCRIPTION": str,
        }
    )
    # Deduplicate
    # We want one row per ['CONTINGENCYID', 'EFFECTIVEDATE', 'VERSIONNO']
    # Tie-breaker: greatest value of LASTCHANGED
    .sort(LASTCHANGED, descending=False)
    .unique(subset=["CONTINGENCYID", "EFFECTIVEDATE", "VERSIONNO"], keep="last")
)

Nemosis

This table is not yet available in Nemosis.

Availability

AEMO's documentation states that this table's visibility is "Public". However, it is not available in the public monthly MMSDM archive. AEMO probably call this public because it is available to all market participants over private FTP.

There is a chance that is is still public to non-participants. It may only be available for some months; or (rarely) it may be available in a non-standard location.

Deduplication and PDR Loader Configuration

The primary keys are:

• CONTINGENCYID

• EFFECTIVEDATE

• VERSIONNO

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
GENERIC_CONSTRAINT	PASA_CONTINGENCY_DEFINITION	1

This means that the CSV files start with metadata columns:

I,GENERIC_CONSTRAINT,PASA_CONTINGENCY_DEFINITION,1,...
D,GENERIC_CONSTRAINT,PASA_CONTINGENCY_DEFINITION,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: GENERIC_CONSTRAINT

Copy as Markdown