MR_EVENT_SCHEDULE

MR_EVENT_SCHEDULE defines the Stack version of the Acceptance Schedule and is the parent table to MR_DayOffer_Stack and MR_PerOffer_Stack.

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 the MR_Event_Schedule, MR_DayOffer_Stack and MR_PerOffer_Stack table.

The MR_EVENT_SCHEDULE table determines the existence of an MR offer acceptance stack for a specific MR schedule of an MR event. The MR_EVENT_SCHEDULE table also tracks the time each stack is exercised. MR_EVENT_SCHEDULE is public and notifies the market that a new offer stack has been created.

Source

MR_EVENT_SCHEDULE updates are ad hoc.

Volume

2 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

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.

VERSION_DATETIME

Effective Date/Time of Schedule; Allows many Stack versions

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

This column is mandatory, and a primary key.

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

AUTHORISEDBY

Authorised person confirming Offer Stack (AKA Acceptance)

SQL Type: VARCHAR2(20) Polars Type: str

This column is not mandatory.

AUTHORISEDDATE

Date and time the Offer Stack confirmed

Warning

Despite what the name suggests, this is a datetime, not a date.

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

This column is not mandatory.

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

DEMAND_EFFECTIVEDATE

Foreign key reference to ResDemandTrk.EffectiveDate

Warning

Despite what the name suggests, this is a datetime, not a date.

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

This column is not mandatory.

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

DEMAND_OFFERDATE

Foreign key reference to ResDemandTrk.OfferDate

Warning

Despite what the name suggests, this is a datetime, not a date.

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

This column is not mandatory.

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

DEMAND_VERSIONNO

Foreign key reference to ResDemandTrk.VersionNo

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

This column is not mandatory.

LASTCHANGED

Date and time the record was 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",
            "AUTHORISEDDATE",
            "DEMAND_EFFECTIVEDATE",
            "DEMAND_OFFERDATE",
            "LASTCHANGED",
        ).str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast(
        {
            "REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
            "AUTHORISEDBY": str,
            "DEMAND_VERSIONNO": int,  # or pl.Int16
        }
    )
    # Deduplicate
    # We want one row per ['MR_DATE', 'REGIONID', 'VERSION_DATETIME']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(subset=["MR_DATE", "REGIONID", "VERSION_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:

• MR_DATE

• REGIONID

• 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	EVENT_SCHEDULE	1

This means that the CSV files start with metadata columns:

I,MR,EVENT_SCHEDULE,1,...
D,MR,EVENT_SCHEDULE,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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