FCAS_REGU_USAGE_FACTORS_TRK

Stores the proportion of enabled regulation FCAS dispatch that is typically consumed for frequency regulation. Used to calculate the projected state of charge for energy storage systems.

Column Schema

EFFECTIVEDATE

The effective date for this regulation FCAS usage factor

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

This column is mandatory, and a primary key.

Example: 2024/02/29 00:00:00

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

VERSIONNO

Version of the date with respect to effective date

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

This column is mandatory, and a primary key.

Example: 1

AUTHORISEDDATE

The date time that this set of usage factors was 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: 2024/03/04 10:00:00

LASTCHANGED

The last time the data has been changed/updated

For more information, see the dedicated documentation page.

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

This column is not mandatory.

Example: 2024/03/04 10:00:00

New Data Timing

Updates whenever FCAS_REGU_USAGE_FACTORS data is updated

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
    # 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_FCAS_REGU_USAGE_FACTORS_TRK_FILE01_202607010000.zip

Table without MetadataTable with MetadataRaw Text without Metadata

EFFECTIVEDATE	VERSIONNO	AUTHORISEDDATE	LASTCHANGED
2024/02/29 00:00:00	1	2024/03/04 10:00:00	2024/03/04 10:00:00

C	SETP.WORLD	DVD_FCAS_REGU_USAGE_FACTORS_TRK	AEMO	PUBLIC	2026/08/08	14:16:18	001786162578755	MONTHLY_ARCHIVE	001786162578755
I	MARKET_CONFIG	FCASREGUSEFACTORS_TRK	1	EFFECTIVEDATE	VERSIONNO	AUTHORISEDDATE	LASTCHANGED
D	MARKET_CONFIG	FCASREGUSEFACTORS_TRK	1	2024/02/29 00:00:00	1	2024/03/04 10:00:00	2024/03/04 10:00:00
C	END OF REPORT	4

EFFECTIVEDATE,VERSIONNO,AUTHORISEDDATE,LASTCHANGED
"2024/02/29 00:00:00",1,"2024/03/04 10:00:00","2024/03/04 10:00:00"

Raw Text with Metadata

C,SETP.WORLD,DVD_FCAS_REGU_USAGE_FACTORS_TRK,AEMO,PUBLIC,2026/08/08,14:16:18,001786162578755,MONTHLY_ARCHIVE,001786162578755
I,MARKET_CONFIG,FCASREGUSEFACTORS_TRK,1,EFFECTIVEDATE,VERSIONNO,AUTHORISEDDATE,LASTCHANGED
D,MARKET_CONFIG,FCASREGUSEFACTORS_TRK,1,"2024/02/29 00:00:00",1,"2024/03/04 10:00:00","2024/03/04 10:00:00"
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 8 of 2024. The most recent available data is month 7 of 2026.

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

Data Size

Each year of data is approximately 5 KB when zipped, 54 KB as unzipped CSV.

Deduplication and PDR Loader Configuration

The primary keys are:

• EFFECTIVEDATE

• VERSIONNO

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
MARKET_CONFIG	FCASREGUSEFACTORS_TRK	1

This means that the CSV files start with metadata columns:

I,MARKET_CONFIG,FCASREGUSEFACTORS_TRK,1,...
D,MARKET_CONFIG,FCASREGUSEFACTORS_TRK,1,...

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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