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MTPASA_CASE_SET

MTPASA_CASE_SET is obsolete from 2005 End of Year Release. The RUNTYPE added to the primary key of the detail tables for MTPASA allows for the different types of runs for each case. MTPASA_CASE_SET allows a MT PASA scenario to be linked across runs.

Deprecation Warning

This table is in the HISTORICAL TABLES package, so is likely deprecated.

Source

Update weekly.

Column Schema

RUN_DATETIME

Date processing of the run begins. Generated from the solution file CASEID.

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

This column is mandatory, and a primary key.

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

RUN_NO

Unique run id. Generated from the solution file CASEID.

SQL Type: NUMBER(3,0) Polars Type: pl.Int16

This column is mandatory, and a primary key.

CASESETID

Unique id to link a set of cases run from the same inputs

SQL Type: NUMBER(3,0) Polars Type: pl.Int16

This column is not mandatory.

LASTCHANGED

Date the solution was loaded

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

RUNTYPEID

Unique id for type of run, being either

SQL Type: NUMBER(1,0) Polars Type: pl.Int8

This column is not mandatory.

New Data Timing

Every MTPASA run (weekly)

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("RUN_DATETIME", "LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast(
        {
            "RUN_NO": int,  # or pl.Int16
            "CASESETID": int,  # or pl.Int16
            "RUNTYPEID": int,  # or pl.Int8
        }
    )
    # Deduplicate
    # We want one row per ['RUN_DATETIME', 'RUN_NO']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(subset=["RUN_DATETIME", "RUN_NO"], 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:

PDR Loader configuration does not mention this table, so it is unclear how to choose which row to keep when there are multiple rows for a given set of primary keys.

You should probably take the row with the maximum LASTCHANGED.

Indexes

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: HISTORICAL TABLES