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MTPASACONSTRAINTSOLUTION_D

MTPASACONSTRAINTSOLUTION_D sets out MT PASA constraint solution results, where constraints are binding.

Deprecation Warning

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

MTPASACONSTRAINTSOLUTION_D is public data.

Source

MTPASACONSTRAINTSOLUTION_D updates weekly.

Volume

Each run overwrites data from previous runs for all future dates. Growth is one record per newly effective constraint.

Column Schema

CONSTRAINT_ID

Constraint Identifier

SQL Type: VARCHAR2(20) Polars Type: str

This column is mandatory, and a primary key.

DATETIME

Date constraint is binding

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

This column is mandatory, and a primary key.

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

DEGREE_OF_VIOLATION

The degree in MW by which the constraint would be violated if the solution could not solve. This could be due to incorrect penalties etc. This figure should always be 0.

SQL Type: NUMBER(16,6) Polars Type: pl.Float64

This column is not mandatory.

LASTCHANGED

Last changed data and time.

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

RUN_DATETIME

The run date and time

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

This column is not mandatory.

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

New Data Timing

On MTPASA solution published to the market (usually once per 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("DATETIME", "LASTCHANGED", "RUN_DATETIME").str.strptime(
            pl.Datetime, dt_s_fmt
        ),
    )
    .cast({"CONSTRAINT_ID": str, "DEGREE_OF_VIOLATION": pl.Float64})
    # Deduplicate
    # We want one row per ['CONSTRAINT_ID', 'DATETIME']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(subset=["CONSTRAINT_ID", "DATETIME"], 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