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AUCTION_CALENDAR

AUCTION_CALENDAR holds the definitions of each auction quarter in a contract year. AUCTION_CALENDAR supports the Settlement Residue Auction.

AUCTION_CALENDAR is public data, and is available to all participants.

Source

Updates are usually quarterly by the SRA team.

Volume

AUCTION_CALENDAR shows a maximum of 16 records per year.

Column Schema

CONTRACTYEAR

SRA Contracted Year

SQL Type: NUMBER(4,0) Polars Type: pl.UInt16

This column is mandatory, and a primary key.

Example: 2032

QUARTER

SRA Contracted Quarter

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

This column is mandatory, and a primary key.

Example: 4

ENDDATE

Last day of SRA Contract Quarter expressed as Date

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

This column is not mandatory.

Example: 2032/12/31 00:00:00

Note that AEMO represents dates as datetimes with a midnight time component.

FINALPROCEEDSSTMTDATE

The date the Final Proceeds Statement is generated

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

This column is not mandatory.

Example: 2032/10/20 00:00:00

Note that AEMO represents dates as datetimes with a midnight time component.

FINALPURCHASESTMTDATE

The date the Final Purchase Statement is generated

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

This column is not mandatory.

Example: 2032/10/18 00:00:00

Note that AEMO represents dates as datetimes with a midnight time component.

LASTCHANGED

Last date and time record changed

For more information, see the dedicated documentation page.

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

This column is not mandatory.

Example: 2026/10/01 00:00:00

NOTIFYDATE

Default notification date

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

This column is not mandatory.

Example: 2029/12/03 00:00:00

Note that AEMO represents dates as datetimes with a midnight time component.

PAYMENTDATE

Date for payment by Participant

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

This column is not mandatory.

Example: 2032/10/20 00:00:00

Note that AEMO represents dates as datetimes with a midnight time component.

PRELIMPROCEEDSSTMTDATE

The date the Prelim Proceeds Statement is generated

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

This column is not mandatory.

Example: 2032/10/07 00:00:00

Note that AEMO represents dates as datetimes with a midnight time component.

PRELIMPURCHASESTMTDATE

The date the Prelim Purchase Statement is generated

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

This column is not mandatory.

Example: 2032/10/07 00:00:00

Note that AEMO represents dates as datetimes with a midnight time component.

RECONCILIATIONDATE

Date of reconciliation for the quarter

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

This column is not mandatory.

Example: 2033/02/04 00:00:00

Note that AEMO represents dates as datetimes with a midnight time component.

STARTDATE

First day of SRA Contract Quarter expressed as Date

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

This column is not mandatory.

Example: 2032/10/01 00:00:00

Note that AEMO represents dates as datetimes with a midnight time component.

New Data Timing

Updated quarterly

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"

# Dates are represented as datetimes at midnight

d_fmt = "%Y/%m/%d 00:00:00"

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("LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
        pl.col(
            "ENDDATE",
            "FINALPROCEEDSSTMTDATE",
            "FINALPURCHASESTMTDATE",
            "NOTIFYDATE",
            "PAYMENTDATE",
            "PRELIMPROCEEDSSTMTDATE",
            "PRELIMPURCHASESTMTDATE",
            "RECONCILIATIONDATE",
            "STARTDATE",
        ).str.strptime(pl.Date, d_fmt),
    )
    .cast(
        {
            "CONTRACTYEAR": int,  # or pl.UInt16
            "QUARTER": int,  # or pl.Int8
            "ENDDATE": pl.Date(),
            "FINALPROCEEDSSTMTDATE": pl.Date(),
            "FINALPURCHASESTMTDATE": pl.Date(),
            "NOTIFYDATE": pl.Date(),
            "PAYMENTDATE": pl.Date(),
            "PRELIMPROCEEDSSTMTDATE": pl.Date(),
            "PRELIMPURCHASESTMTDATE": pl.Date(),
            "RECONCILIATIONDATE": pl.Date(),
            "STARTDATE": pl.Date(),
        }
    )
    # Deduplicate
    # We want one row per ['CONTRACTYEAR', 'QUARTER']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(subset=["CONTRACTYEAR", "QUARTER"], keep="last")
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_AUCTION_CALENDAR_FILE01_202609010000.zip

CONTRACTYEARQUARTERSTARTDATEENDDATENOTIFYDATEPAYMENTDATERECONCILIATIONDATELASTCHANGEDPRELIMPURCHASESTMTDATEPRELIMPROCEEDSSTMTDATEFINALPURCHASESTMTDATEFINALPROCEEDSSTMTDATE
203242032/10/01 00:00:002032/12/31 00:00:002029/12/03 00:00:002032/10/20 00:00:002033/02/04 00:00:002026/10/01 00:00:002032/10/07 00:00:002032/10/07 00:00:002032/10/18 00:00:002032/10/20 00:00:00

CSETP.WORLDDVD_AUCTION_CALENDARAEMOPUBLIC2026/10/0915:24:37001791523477897MONTHLY_ARCHIVE001791523477897
IIRAUCTION_CONFIGAUCTION_CALENDAR2CONTRACTYEARQUARTERSTARTDATEENDDATENOTIFYDATEPAYMENTDATERECONCILIATIONDATELASTCHANGEDPRELIMPURCHASESTMTDATEPRELIMPROCEEDSSTMTDATEFINALPURCHASESTMTDATEFINALPROCEEDSSTMTDATE
DIRAUCTION_CONFIGAUCTION_CALENDAR2203242032/10/01 00:00:002032/12/31 00:00:002029/12/03 00:00:002032/10/20 00:00:002033/02/04 00:00:002026/10/01 00:00:002032/10/07 00:00:002032/10/07 00:00:002032/10/18 00:00:002032/10/20 00:00:00
CEND OF REPORT4

CONTRACTYEAR,QUARTER,STARTDATE,ENDDATE,NOTIFYDATE,PAYMENTDATE,RECONCILIATIONDATE,LASTCHANGED,PRELIMPURCHASESTMTDATE,PRELIMPROCEEDSSTMTDATE,FINALPURCHASESTMTDATE,FINALPROCEEDSSTMTDATE
2032,4,"2032/10/01 00:00:00","2032/12/31 00:00:00","2029/12/03 00:00:00","2032/10/20 00:00:00","2033/02/04 00:00:00","2026/10/01 00:00:00","2032/10/07 00:00:00","2032/10/07 00:00:00","2032/10/18 00:00:00","2032/10/20 00:00:00"

C,SETP.WORLD,DVD_AUCTION_CALENDAR,AEMO,PUBLIC,2026/10/09,15:24:37,001791523477897,MONTHLY_ARCHIVE,001791523477897
I,IRAUCTION_CONFIG,AUCTION_CALENDAR,2,CONTRACTYEAR,QUARTER,STARTDATE,ENDDATE,NOTIFYDATE,PAYMENTDATE,RECONCILIATIONDATE,LASTCHANGED,PRELIMPURCHASESTMTDATE,PRELIMPROCEEDSSTMTDATE,FINALPURCHASESTMTDATE,FINALPROCEEDSSTMTDATE
D,IRAUCTION_CONFIG,AUCTION_CALENDAR,2,2032,4,"2032/10/01 00:00:00","2032/12/31 00:00:00","2029/12/03 00:00:00","2032/10/20 00:00:00","2033/02/04 00:00:00","2026/10/01 00:00:00","2032/10/07 00:00:00","2032/10/07 00:00:00","2032/10/18 00:00:00","2032/10/20 00:00:00"
C,"END OF REPORT",4

Availability

Visibility: Public

This table is in the public monthly MMSDM archive.

I have collated the list of all those files into one place, in this CSV metadata file. (Columns year, month, url, compressed_size in bytes).

The earliest available data is month 1 of 2015. The most recent available data is month 9 of 2026.

Warning

There are some months with no files.

Data Size

Each year of data is approximately 6 KB when zipped, 59 KB as unzipped CSV.

Deduplication and PDR Loader Configuration

The primary keys are:

The transaction type is "INSERT-UPDATE", which means that you will likely see duplicate data with different values.

You should deduplicate by sorting based on LASTCHANGED, within each group of primary keys.

PDR Loader configuration is:

Report Name Report Subtype Version
IRAUCTION_CONFIG AUCTION_CALENDAR 1
IRAUCTION_CONFIG AUCTION_CALENDAR 2

This means that the CSV files start with metadata columns:

I,IRAUCTION_CONFIG,AUCTION_CALENDAR,1,...
D,IRAUCTION_CONFIG,AUCTION_CALENDAR,1,...

or

I,IRAUCTION_CONFIG,AUCTION_CALENDAR,2,...
D,IRAUCTION_CONFIG,AUCTION_CALENDAR,2,...

ROW_FILTER_TYPE is 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: IRAUCTION