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MTPASAREGIONSOLUTION_D

MTPASAREGIONSOLUTION_D shows region results for MT PASA, showing predicted demand and any capacity limits.

Deprecation Warning

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

MTPASAREGIONSOLUTION_D is public data.

Source

MTPASAREGIONSOLUTION_D updates weekly.

Volume

Each run overwrites data from previous runs for all future dates. Growth is one record per day per region.

Column Schema

DATETIME

Date of results. One record for each day for next two years.

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

This column is mandatory, and a primary key.

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

REGION_ID

Region Identifier

SQL Type: VARCHAR2(12) Polars Type: str

This column is mandatory, and a primary key.

CAPACITY_REQUIREMENT

Capacity in MW required to meet demand

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

This column is not mandatory.

CONSTRAINED_CAPACITY

The total regional capacity due to energy and network constraints

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

This column is not mandatory.

DEMAND_MINUS_SCHEDULED_LOAD

Regional demand minus the scheduled load value

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

This column is not mandatory.

ENERGY_REQUIREMENT_10POE

Regional energy required to meet demand

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

This column is not mandatory.

LASTCHANGED

Last change date 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

MINIMUM_RESERVE_REQUIREMENT

Minimum required regional reserve value

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

This column is not mandatory.

NET_INTERCHANGE

Regional net MW import via interconnectors

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

This column is not mandatory.

REGION_DEMAND_10POE

Regional 10% Probability of Exceedance demand forecast value

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

This column is not mandatory.

REPORTED_BLOCK_ID

The load duration curve block that is recorded in the report.

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

This column is not mandatory.

RESERVE_CONDITION

The regional reserve condition

SQL Type: VARCHAR2(50) Polars Type: str

This column is not mandatory.

RESERVE_SURPLUS

Regional reserve surplus value

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

This column is not mandatory.

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

UNCONSTRAINED_CAPACITY

The total regional capacity, subject to network constraints.

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

This column is not mandatory.

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(
        {
            "REGION_ID": str,
            "CAPACITY_REQUIREMENT": pl.Float64,
            "CONSTRAINED_CAPACITY": pl.Float64,
            "DEMAND_MINUS_SCHEDULED_LOAD": pl.Float64,
            "ENERGY_REQUIREMENT_10POE": pl.Float64,
            "MINIMUM_RESERVE_REQUIREMENT": pl.Float64,
            "NET_INTERCHANGE": pl.Float64,
            "REGION_DEMAND_10POE": pl.Float64,
            "REPORTED_BLOCK_ID": pl.Float64,
            "RESERVE_CONDITION": str,
            "RESERVE_SURPLUS": pl.Float64,
            "UNCONSTRAINED_CAPACITY": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['DATETIME', 'REGION_ID']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(subset=["DATETIME", "REGION_ID"], 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