SSM_SCHEDULED_AVAILABILITY

Scheduled SSM availability

Column Schema

AVAILABLE_START_INTERVAL

The first Dispatch Interval the unit is available.

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

This column is mandatory, and a primary key.

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

CONTRACT_ID

Unique Contract Identifier

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

This column is mandatory, and a primary key.

DUID

Dispatchable Unit Identifier

Warning

This column may contain funny characters such as / (slash) or # (hash). This may break hive-style partitioning.

For more information, see the dedicated documentation page.

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

This column is mandatory, and a primary key.

Example: W/HOE#2

AVAILABLE_END_INTERVAL

The last Dispatch Interval the unit is available.

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

This column is not mandatory.

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

LASTCHANGED

Last time record was changed.

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

TNSP_PARTICIPANTID

TNSP Participant ID

SQL Type: VARCHAR2(10) Polars Type: pl.Categorical

This column is not mandatory.

New Data Timing

Timed trigger once per week for the previous week

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(
            "AVAILABLE_START_INTERVAL", "AVAILABLE_END_INTERVAL", "LASTCHANGED"
        ).str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast(
        {
            "CONTRACT_ID": pl.Categorical,
            "DUID": pl.Categorical,
            "TNSP_PARTICIPANTID": pl.Categorical,
        }
    )
    # Deduplicate
    # We want one row per ['AVAILABLE_START_INTERVAL', 'CONTRACT_ID', 'DUID']
    # Tie-breaker: greatest value of LASTCHANGED
    .sort(LASTCHANGED, descending=False)
    .unique(subset=["AVAILABLE_START_INTERVAL", "CONTRACT_ID", "DUID"], 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:

• AVAILABLE_START_INTERVAL

• CONTRACT_ID

• DUID

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
SSM	SCHEDULED_AVAILABILITY	1

This means that the CSV files start with metadata columns:

I,SSM,SCHEDULED_AVAILABILITY,1,...
D,SSM,SCHEDULED_AVAILABILITY,1,...

ROW_FILTER_TYPE is 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: SYSTEM_SECURITY_MANAGEMENT

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