SET_FCAS_REG_DEF_RESIDAMT
This report contains the FCAS Regulation Residue Amounts that include FPP Residual Amounts, Used Residual Amounts and Unused Residual Amounts calculated using the Energy Ratio for each Requirement Region and the Default Residual CF.
Column Schema
CONSTRAINTID
The FCAS Regulation Constraint ID
SQL Type: VARCHAR2(20)
Polars Type: pl.Categorical
This column is mandatory, and a primary key.
PARTICIPANTID
The Participant Id Identifier
For more information, see the dedicated documentation page.
SQL Type: VARCHAR2(20)
Polars Type: pl.Categorical
This column is mandatory, and a primary key.
PERIODID
The Period ID Identifier
SQL Type: NUMBER(3,0)
Polars Type: pl.Int16
This column is mandatory, and a primary key.
REGIONID
The RegionId used for the residual calculation. This is the Constraint Requirement Region.
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.
SETTLEMENTDATE
The Settlement Date of the Billing Week
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
VERSIONNO
The Settlement Run No
SQL Type: NUMBER(3,0)
Polars Type: pl.UInt16
This column is mandatory, and a primary key.
ACE_MWH
The ACE MWh value that is used for the Residual Calculation.(Excluding CPID with CF)
SQL Type: NUMBER(18,8)
Polars Type: pl.Float64
This column is not mandatory.
ASOE_MWH
The ASOE MWh value that is used for the Residual Calculation.(Excluding CPID with CF)
SQL Type: NUMBER(18,8)
Polars Type: pl.Float64
This column is not mandatory.
BIDTYPE
The BidType LOWERREG or RAISEREG for the Constraint ID
The SQL type is VARCHAR2(10).
This is actually a factor/enum, with values:
-
ENERGY -
LOWERREG -
LOWER60SEC -
RAISE5MIN -
LOWER5MIN -
LOWER6SEC -
RAISE60SEC -
RAISE6SEC -
RAISEREG -
RAISE1SEC -
LOWER1SEC
This column is not mandatory.
LASTCHANGED
The Last Changed Date time of the record.
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
RESIDUAL_MWH
Sum of ABS(ACE_MWh) + ASOE_MWh. The MWh is not netted for residual calculation.
SQL Type: NUMBER(18,8)
Polars Type: pl.Float64
This column is not mandatory.
UNUSED_ACE_AMOUNT
The Unused Recovery ACE Amount calculated using the ACE MWh value of the requirement regions.
SQL Type: NUMBER(18,8)
Polars Type: pl.Float64
This column is not mandatory.
UNUSED_ASOE_AMOUNT
The Unused Recovery ASOE Amount calculated using the ASOE MWh value of the requirement regions.
SQL Type: NUMBER(18,8)
Polars Type: pl.Float64
This column is not mandatory.
UNUSED_RESIDUAL_AMOUNT
Sum of UNUSED_ACE_AMOUNT + UNUSED_ASOE_AMOUNT.
SQL Type: NUMBER(18,8)
Polars Type: pl.Float64
This column is not mandatory.
New Data Timing
Daily Billing Run & Posting a PRELIM/FINAL and REVISE Billing Run
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("SETTLEMENTDATE", "LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
)
.cast(
{
"CONSTRAINTID": pl.Categorical,
"PARTICIPANTID": pl.Categorical,
"PERIODID": int, # or pl.Int16
"REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
"VERSIONNO": int, # or pl.UInt16
"ACE_MWH": pl.Float64,
"ASOE_MWH": pl.Float64,
"BIDTYPE": pl.Enum(
[
"ENERGY",
"LOWERREG",
"LOWER60SEC",
"RAISE5MIN",
"LOWER5MIN",
"LOWER6SEC",
"RAISE60SEC",
"RAISE6SEC",
"RAISEREG",
"RAISE1SEC",
"LOWER1SEC",
]
),
"RESIDUAL_MWH": pl.Float64,
"UNUSED_ACE_AMOUNT": pl.Float64,
"UNUSED_ASOE_AMOUNT": pl.Float64,
"UNUSED_RESIDUAL_AMOUNT": pl.Float64,
}
)
# Deduplicate
# We want one row per ['CONSTRAINTID', 'PARTICIPANTID', 'PERIODID', 'REGIONID', 'SETTLEMENTDATE', 'VERSIONNO']
# Tie-breaker: greatest value of LASTCHANGED
.sort(LASTCHANGED, descending=False)
.unique(
subset=[
"CONSTRAINTID",
"PARTICIPANTID",
"PERIODID",
"REGIONID",
"SETTLEMENTDATE",
"VERSIONNO",
],
keep="last",
)
)
Nemosis
This table is not yet available in Nemosis.
Availability
Visibility: Private
This table is not available in the public monthly MMSDM archive. Is is only available to registered market participants over FTP inside a VPN.
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.
| Report Name | Report Subtype | Version |
|---|---|---|
| SETTLEMENTS | FCAS_REG_DEF_RESIDAMT | 1 |
This means that the CSV files start with metadata columns:
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: SETTLEMENT_DATA