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FCAS_REGU_USAGE_FACTORS

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

BIDTYPE

The type of regulation FCAS service [RAISEREG,LOWERREG]

The SQL type is VARCHAR2(20). This is actually a factor/enum, with values:

  • ENERGY

  • LOWERREG

  • LOWER60SEC

  • RAISE5MIN

  • LOWER5MIN

  • LOWER6SEC

  • RAISE60SEC

  • RAISE6SEC

  • RAISEREG

  • RAISE1SEC

  • LOWER1SEC

This column is mandatory, and a primary key.

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.

PERIODID

The Period ID (1 - 48) within the calendar day to which this usage factor applies

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

This column is mandatory, and a primary key.

Examples: 1, 48

REGIONID

Unique RegionID

For more information, see the dedicated documentation page.

The SQL type is VARCHAR2(20). This is actually a factor/enum, with values:

  • QLD1

  • VIC1

  • TAS1

  • NSW1

  • SA1

  • SNOWY1

This column is mandatory, and a primary key.

VERSIONNO

Version with respect to effective date

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

This column is mandatory, and a primary key.

Example: 1

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

USAGE_FACTOR

The proportion of cleared regulation FCAS that is assumed to be used within a dispatch interval. Expressed as a fractional amount between 0 and 1

SQL Type: NUMBER(8,3) Polars Type: pl.Float64

This column is not mandatory.

Examples: 0.01, 0.33

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("LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
        pl.col("EFFECTIVEDATE").str.strptime(pl.Date, d_fmt),
    )
    .cast(
        {
            "BIDTYPE": pl.Enum(
                [
                    "ENERGY",
                    "LOWERREG",
                    "LOWER60SEC",
                    "RAISE5MIN",
                    "LOWER5MIN",
                    "LOWER6SEC",
                    "RAISE60SEC",
                    "RAISE6SEC",
                    "RAISEREG",
                    "RAISE1SEC",
                    "LOWER1SEC",
                ]
            ),
            "EFFECTIVEDATE": pl.Date(),
            "PERIODID": int,  # or pl.Int16
            "REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
            "VERSIONNO": int,  # or pl.UInt16
            "USAGE_FACTOR": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['BIDTYPE', 'EFFECTIVEDATE', 'PERIODID', 'REGIONID', 'VERSIONNO']
    # Tie-breaker: greatest value of LASTCHANGED
    .sort(LASTCHANGED, descending=False)
    .unique(
        subset=["BIDTYPE", "EFFECTIVEDATE", "PERIODID", "REGIONID", "VERSIONNO"],
        keep="last",
    )
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_FCAS_REGU_USAGE_FACTORS_FILE01_202607010000.zip

EFFECTIVEDATEVERSIONNOREGIONIDBIDTYPEPERIODIDUSAGE_FACTORLASTCHANGED
2024/02/29 00:00:001NSW1LOWERREG410.012024/03/04 10:00:00
2024/02/29 00:00:001NSW1LOWERREG420.012024/03/04 10:00:00
2024/02/29 00:00:001NSW1LOWERREG430.012024/03/04 10:00:00
2024/02/29 00:00:001NSW1LOWERREG440.012024/03/04 10:00:00
2024/02/29 00:00:001NSW1LOWERREG450.012024/03/04 10:00:00
2024/02/29 00:00:001NSW1LOWERREG460.022024/03/04 10:00:00
2024/02/29 00:00:001NSW1LOWERREG470.022024/03/04 10:00:00
2024/02/29 00:00:001NSW1LOWERREG480.032024/03/04 10:00:00
2024/02/29 00:00:001NSW1LOWERREG10.032024/03/04 10:00:00
2024/02/29 00:00:001NSW1LOWERREG20.042024/03/04 10:00:00
2024/02/29 00:00:001NSW1LOWERREG30.062024/03/04 10:00:00
...

CSETP.WORLDDVD_FCAS_REGU_USAGE_FACTORSAEMOPUBLIC2026/08/0814:16:19001786162579438MONTHLY_ARCHIVE001786162579438
IMARKET_CONFIGFCASREGUSEFACTORS1EFFECTIVEDATEVERSIONNOREGIONIDBIDTYPEPERIODIDUSAGE_FACTORLASTCHANGED
DMARKET_CONFIGFCASREGUSEFACTORS12024/02/29 00:00:001NSW1LOWERREG410.012024/03/04 10:00:00
DMARKET_CONFIGFCASREGUSEFACTORS12024/02/29 00:00:001NSW1LOWERREG420.012024/03/04 10:00:00
DMARKET_CONFIGFCASREGUSEFACTORS12024/02/29 00:00:001NSW1LOWERREG430.012024/03/04 10:00:00
DMARKET_CONFIGFCASREGUSEFACTORS12024/02/29 00:00:001NSW1LOWERREG440.012024/03/04 10:00:00
DMARKET_CONFIGFCASREGUSEFACTORS12024/02/29 00:00:001NSW1LOWERREG450.012024/03/04 10:00:00
DMARKET_CONFIGFCASREGUSEFACTORS12024/02/29 00:00:001NSW1LOWERREG460.022024/03/04 10:00:00
DMARKET_CONFIGFCASREGUSEFACTORS12024/02/29 00:00:001NSW1LOWERREG470.022024/03/04 10:00:00
DMARKET_CONFIGFCASREGUSEFACTORS12024/02/29 00:00:001NSW1LOWERREG480.032024/03/04 10:00:00
DMARKET_CONFIGFCASREGUSEFACTORS12024/02/29 00:00:001NSW1LOWERREG10.032024/03/04 10:00:00
DMARKET_CONFIGFCASREGUSEFACTORS12024/02/29 00:00:001NSW1LOWERREG20.042024/03/04 10:00:00
DMARKET_CONFIGFCASREGUSEFACTORS12024/02/29 00:00:001NSW1LOWERREG30.062024/03/04 10:00:00
...
CEND OF REPORT483

EFFECTIVEDATE,VERSIONNO,REGIONID,BIDTYPE,PERIODID,USAGE_FACTOR,LASTCHANGED
"2024/02/29 00:00:00",1,NSW1,LOWERREG,41,0.01,"2024/03/04 10:00:00"
"2024/02/29 00:00:00",1,NSW1,LOWERREG,42,0.01,"2024/03/04 10:00:00"
"2024/02/29 00:00:00",1,NSW1,LOWERREG,43,0.01,"2024/03/04 10:00:00"
"2024/02/29 00:00:00",1,NSW1,LOWERREG,44,0.01,"2024/03/04 10:00:00"
"2024/02/29 00:00:00",1,NSW1,LOWERREG,45,0.01,"2024/03/04 10:00:00"
"2024/02/29 00:00:00",1,NSW1,LOWERREG,46,0.02,"2024/03/04 10:00:00"
"2024/02/29 00:00:00",1,NSW1,LOWERREG,47,0.02,"2024/03/04 10:00:00"
"2024/02/29 00:00:00",1,NSW1,LOWERREG,48,0.03,"2024/03/04 10:00:00"
"2024/02/29 00:00:00",1,NSW1,LOWERREG,1,0.03,"2024/03/04 10:00:00"
"2024/02/29 00:00:00",1,NSW1,LOWERREG,2,0.04,"2024/03/04 10:00:00"
"2024/02/29 00:00:00",1,NSW1,LOWERREG,3,0.06,"2024/03/04 10:00:00"
...

C,SETP.WORLD,DVD_FCAS_REGU_USAGE_FACTORS,AEMO,PUBLIC,2026/08/08,14:16:19,001786162579438,MONTHLY_ARCHIVE,001786162579438
I,MARKET_CONFIG,FCASREGUSEFACTORS,1,EFFECTIVEDATE,VERSIONNO,REGIONID,BIDTYPE,PERIODID,USAGE_FACTOR,LASTCHANGED
D,MARKET_CONFIG,FCASREGUSEFACTORS,1,"2024/02/29 00:00:00",1,NSW1,LOWERREG,41,0.01,"2024/03/04 10:00:00"
D,MARKET_CONFIG,FCASREGUSEFACTORS,1,"2024/02/29 00:00:00",1,NSW1,LOWERREG,42,0.01,"2024/03/04 10:00:00"
D,MARKET_CONFIG,FCASREGUSEFACTORS,1,"2024/02/29 00:00:00",1,NSW1,LOWERREG,43,0.01,"2024/03/04 10:00:00"
D,MARKET_CONFIG,FCASREGUSEFACTORS,1,"2024/02/29 00:00:00",1,NSW1,LOWERREG,44,0.01,"2024/03/04 10:00:00"
D,MARKET_CONFIG,FCASREGUSEFACTORS,1,"2024/02/29 00:00:00",1,NSW1,LOWERREG,45,0.01,"2024/03/04 10:00:00"
D,MARKET_CONFIG,FCASREGUSEFACTORS,1,"2024/02/29 00:00:00",1,NSW1,LOWERREG,46,0.02,"2024/03/04 10:00:00"
D,MARKET_CONFIG,FCASREGUSEFACTORS,1,"2024/02/29 00:00:00",1,NSW1,LOWERREG,47,0.02,"2024/03/04 10:00:00"
D,MARKET_CONFIG,FCASREGUSEFACTORS,1,"2024/02/29 00:00:00",1,NSW1,LOWERREG,48,0.03,"2024/03/04 10:00:00"
D,MARKET_CONFIG,FCASREGUSEFACTORS,1,"2024/02/29 00:00:00",1,NSW1,LOWERREG,1,0.03,"2024/03/04 10:00:00"
D,MARKET_CONFIG,FCASREGUSEFACTORS,1,"2024/02/29 00:00:00",1,NSW1,LOWERREG,2,0.04,"2024/03/04 10:00:00"
D,MARKET_CONFIG,FCASREGUSEFACTORS,1,"2024/02/29 00:00:00",1,NSW1,LOWERREG,3,0.06,"2024/03/04 10:00:00"
...
C,"END OF REPORT",483

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 29 KB when zipped, 285 KB as unzipped CSV.

Deduplication and PDR Loader Configuration

The primary keys are:

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 1

This means that the CSV files start with metadata columns:

I,MARKET_CONFIG,FCASREGUSEFACTORS,1,...
D,MARKET_CONFIG,FCASREGUSEFACTORS,1,...

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