MR_PEROFFER_STACK

MR_PEROFFER_STACK defines the accepted capacity on a period basis for the Acceptance Schedule, is a child table to MR_DayOffer_Stack and only includes records or units with accepted_capacity > 0 for the specific period.

Deprecation Warning

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

Once the offer cut off time has passed and as the schedule changes AEMO is obliged to accept MR capacity to meet the schedule in merit order according to the offers submitted. The relationship to a specific schedule, the merit order of submitted offers and accepted quantities for each trading interval are stored in MR_Event_Schedule, MR_DayOffer_Stack and MR_PerOffer_Stack.

MR_PEROFFER_STACK reports the accepted MR capacity (Accepted_Capacity) required from each unit for each trading interval. MR_PEROFFER_STACK is sparse so lists only units with accepted capacity > 0 for that trading interval. The Deducted_Capacity field allows the tracking and implementation of participant requested reductions to accepted MR capacity to be tracked and applied. MR_PEROFFER_STACK is reported confidentially to each participant to notify acceptance of an MR offer.

Source

MR_PEROFFER_STACK updates are ad hoc.

Volume

4800 rows per year

Column Schema

MR_DATE

Mandatory Restriction imposition date

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

PERIODID

Trade Period for the MR Offer

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

This column is mandatory, and a primary key.

REGIONID

Unique RegionID

For more information, see the dedicated documentation page.

The SQL type is VARCHAR2(10). This is actually a factor/enum, with values:

• QLD1

• VIC1

• TAS1

• NSW1

• SA1

• SNOWY1

This column is mandatory, and a primary key.

STACK_POSITION

LAOF Stack order

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

This column is mandatory, and a primary key.

VERSION_DATETIME

Allows many Period Stack versions for the one Scaling Factor stack

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

This column is mandatory, and a primary key.

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

ACCEPTED_CAPACITY

MR Capacity to be Dispatched

SQL Type: NUMBER(6,0) Polars Type: pl.Int32

This column is not mandatory.

DEDUCTED_CAPACITY

Requested capacity reduction amount

SQL Type: NUMBER(6,0) Polars Type: pl.Int32

This column is not mandatory.

DUID

Dispatchable Unit ID or LinkID. Only required here for CSV reports

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(10) Polars Type: pl.Categorical

This column is not mandatory.

Example: W/HOE#2

LASTCHANGED

Date and time the record was last inserted/modified

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

New Data Timing

Ad hoc

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("MR_DATE", "VERSION_DATETIME", "LASTCHANGED").str.strptime(
            pl.Datetime, dt_s_fmt
        ),
    )
    .cast(
        {
            "PERIODID": int,  # or pl.Int16
            "REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
            "STACK_POSITION": int,  # or pl.Int16
            "ACCEPTED_CAPACITY": int,  # or pl.Int32
            "DEDUCTED_CAPACITY": int,  # or pl.Int32
            "DUID": pl.Categorical,
        }
    )
    # Deduplicate
    # We want one row per ['MR_DATE', 'PERIODID', 'REGIONID', 'STACK_POSITION', 'VERSION_DATETIME']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(
        subset=[
            "MR_DATE",
            "PERIODID",
            "REGIONID",
            "STACK_POSITION",
            "VERSION_DATETIME",
        ],
        keep="last",
    )
)

Nemosis

This table is not yet available in Nemosis.

Availability

AEMO's documentation states that this table's visibility is "Private & Public Next-Day". 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:

• MR_DATE

• PERIODID

• REGIONID

• STACK_POSITION

• VERSION_DATETIME

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
MR	PEROFFER_STACK	1

This means that the CSV files start with metadata columns:

I,MR,PEROFFER_STACK,1,...
D,MR,PEROFFER_STACK,1,...

Indexes

• 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: HISTORICAL TABLES

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