SET_APC_RECOVERY
APC Compensation recovery amounts in the Settlements timeframe
Warning
This is a different table to SETAPCRECOVERY
Column Schema
APEVENTID
AP Event Id
SQL Type: NUMBER(6)
Polars Type: pl.Int32
This column is mandatory, and a primary key.
CLAIMID
AP Event Claim Id
SQL Type: NUMBER(6)
Polars Type: pl.Int32
This column is mandatory, and a primary key.
PARTICIPANTID
Participant 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
Settlements Trading Interval.
SQL Type: NUMBER(3)
Polars Type: pl.Int16
This column is mandatory, and a primary key.
REGIONID
Region id for the recovery amount
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
Settlement run date
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
Settlement run number
SQL Type: NUMBER(3)
Polars Type: pl.UInt16
This column is mandatory, and a primary key.
RECOVERY_AMOUNT
Recovery amount in the region attributable to the participant for the event claim in this interval
SQL Type: NUMBER(18,8)
Polars Type: pl.Float64
This column is not mandatory.
REGION_RECOVERY_AMOUNT
Total Recovery amount in the region for the event claim in this interval
SQL Type: NUMBER(18,8)
Polars Type: pl.Float64
This column is not mandatory.
New Data Timing
After posting of a 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").str.strptime(pl.Datetime, dt_s_fmt),
)
.cast(
{
"APEVENTID": int, # or pl.Int32
"CLAIMID": int, # or pl.Int32
"PARTICIPANTID": pl.Categorical,
"PERIODID": int, # or pl.Int16
"REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
"VERSIONNO": int, # or pl.UInt16
"RECOVERY_AMOUNT": pl.Float64,
"REGION_RECOVERY_AMOUNT": pl.Float64,
}
)
# Deduplicate
# We want one row per ['APEVENTID', 'CLAIMID', 'PARTICIPANTID', 'PERIODID', 'REGIONID', 'SETTLEMENTDATE', 'VERSIONNO']
.unique(
subset=[
"APEVENTID",
"CLAIMID",
"PARTICIPANTID",
"PERIODID",
"REGIONID",
"SETTLEMENTDATE",
"VERSIONNO",
],
keep="any",
)
)
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:
PDR Loader configuration does not mention this table, so it is unclear how to choose which row to keep when there are multiple rows for a given set of primary keys.
Look at the data and use your best judgement.
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