MTPASA_INTERMITTENT_LIMIT

A submission of expected maximum availability for intermittent generators for use in MTPASA intermittent generation forecasts

Column Schema

DUID

Unique Identifier of Dispatchable Unit

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

OFFERDATETIME

Date time file processed

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

This column is mandatory, and a primary key.

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

TRADINGDATE

Trading Day for which this unit availability submission applies

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

AUTHORISEDBYPARTICIPANTID

Participant entering the unit availability submission

SQL Type: VARCHAR2(20) Polars Type: str

This column is not mandatory.

AUTHORISEDBYUSER

User entering the unit availability submission

SQL Type: VARCHAR2(20) Polars Type: str

This column is not mandatory.

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: 2026/01/20 00:05:00

UPPERMWLIMIT

Maximum imposed MW limit. Value between 0 and the registered DUID Maximum Capacity.Value = -1 means no limit applies.

SQL Type: NUMBER(6) Polars Type: pl.Int32

This column is not mandatory.

New Data Timing

On participant data submission

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("OFFERDATETIME", "TRADINGDATE", "LASTCHANGED").str.strptime(
            pl.Datetime, dt_s_fmt
        ),
    )
    .cast(
        {
            "DUID": pl.Categorical,
            "AUTHORISEDBYPARTICIPANTID": str,
            "AUTHORISEDBYUSER": str,
            "UPPERMWLIMIT": int,  # or pl.Int32
        }
    )
    # Deduplicate
    # We want one row per ['DUID', 'OFFERDATETIME', 'TRADINGDATE']
    # Tie-breaker: greatest value of LASTCHANGED
    .sort(LASTCHANGED, descending=False)
    .unique(subset=["DUID", "OFFERDATETIME", "TRADINGDATE"], 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:

• DUID

• OFFERDATETIME

• TRADINGDATE

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
DEMAND	MTPASA_INTERMITTENT_LIMIT	1

This means that the CSV files start with metadata columns:

I,DEMAND,MTPASA_INTERMITTENT_LIMIT,1,...
D,DEMAND,MTPASA_INTERMITTENT_LIMIT,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: DEMAND_FORECASTS

Copy as Markdown