BIDTYPESTRK

BIDTYPESTRK, together with the associated data in BIDTYPES, define a set of ancillary services with bidding parameters from a given date.

BIDTYPESTRK is public to participants

Source

BIDTYPESTRK updates when the static data relating to an ancillary service type is modified.

Volume

Expect modifications to be rare. Allow for approximately 20 records per year.

Column Schema

EFFECTIVEDATE

Market date starting at 04:30 inclusive

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

This column is mandatory, and a primary key.

Example: 2000/09/12 00:00:00

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

VERSIONNO

Record version number

SQL Type: NUMBER(3,0) Polars Type: pl.UInt16

This column is mandatory, and a primary key.

Example: 1

AUTHORISEDBY

User that authorised record. A NULL value indicates the record is not authorised.

SQL Type: VARCHAR2(15) Polars Type: str

This column is not mandatory.

Examples:

• LUKEB
• SPAI

AUTHORISEDDATE

Date of record authorisation. A NULL value indicates the record is not authorised.

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: 2001/09/13 16:33:22

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: 2001/09/13 16:33:22

New Data Timing

When the ancillary service type static data is modified

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("AUTHORISEDDATE", "LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
        pl.col("EFFECTIVEDATE").str.strptime(pl.Date, d_fmt),
    )
    .cast(
        {
            "EFFECTIVEDATE": pl.Date(),
            "VERSIONNO": int,  # or pl.UInt16
            "AUTHORISEDBY": str,
        }
    )
    # Deduplicate
    # We want one row per ['EFFECTIVEDATE', 'VERSIONNO']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(subset=["EFFECTIVEDATE", "VERSIONNO"], keep="last")
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_BIDTYPESTRK_FILE01_202609010000.zip

Table without MetadataTable with MetadataRaw Text without Metadata

EFFECTIVEDATE	VERSIONNO	AUTHORISEDDATE	AUTHORISEDBY	LASTCHANGED
2000/09/12 00:00:00	1	2001/09/13 16:33:22	LUKEB	2001/09/13 16:33:22
2023/08/14 00:00:00	1	2023/08/14 11:52:34	SPAI	2023/08/14 11:52:34

C	SETP.WORLD	DVD_BIDTYPESTRK	AEMO	PUBLIC	2026/10/09	15:24:42	001791523482671	MONTHLY_ARCHIVE	001791523482671
I	MARKET_CONFIG	BIDTYPESTRK	1	EFFECTIVEDATE	VERSIONNO	AUTHORISEDDATE	AUTHORISEDBY	LASTCHANGED
D	MARKET_CONFIG	BIDTYPESTRK	1	2000/09/12 00:00:00	1	2001/09/13 16:33:22	LUKEB	2001/09/13 16:33:22
D	MARKET_CONFIG	BIDTYPESTRK	1	2023/08/14 00:00:00	1	2023/08/14 11:52:34	SPAI	2023/08/14 11:52:34
C	END OF REPORT	5

EFFECTIVEDATE,VERSIONNO,AUTHORISEDDATE,AUTHORISEDBY,LASTCHANGED
"2000/09/12 00:00:00",1,"2001/09/13 16:33:22",LUKEB,"2001/09/13 16:33:22"
"2023/08/14 00:00:00",1,"2023/08/14 11:52:34",SPAI,"2023/08/14 11:52:34"

Raw Text with Metadata

C,SETP.WORLD,DVD_BIDTYPESTRK,AEMO,PUBLIC,2026/10/09,15:24:42,001791523482671,MONTHLY_ARCHIVE,001791523482671
I,MARKET_CONFIG,BIDTYPESTRK,1,EFFECTIVEDATE,VERSIONNO,AUTHORISEDDATE,AUTHORISEDBY,LASTCHANGED
D,MARKET_CONFIG,BIDTYPESTRK,1,"2000/09/12 00:00:00",1,"2001/09/13 16:33:22",LUKEB,"2001/09/13 16:33:22"
D,MARKET_CONFIG,BIDTYPESTRK,1,"2023/08/14 00:00:00",1,"2023/08/14 11:52:34",SPAI,"2023/08/14 11:52:34"
C,"END OF REPORT",5

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.

Every month in between has a file. (No gaps.)

Data Size

Each year of data is approximately 5 KB when zipped, 53 KB as unzipped CSV.

Deduplication and PDR Loader Configuration

The primary keys are:

• EFFECTIVEDATE

• VERSIONNO

Tip

Bid deduplication is particularly tricky. See the guide.

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
MARKET_CONFIG	BIDTYPESTRK	1

This means that the CSV files start with metadata columns:

I,MARKET_CONFIG,BIDTYPESTRK,1,...
D,MARKET_CONFIG,BIDTYPESTRK,1,...

ROW_FILTER_TYPE is LASTCHANGED

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: MARKET_CONFIG

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