BILLING_APC_RECOVERY
Billing result table for recovery of APC compensation payments
Updated with each billing run
Warning
This is a different table to BILLINGAPCRECOVERY
Column Schema
APEVENTID
AP Event Id
SQL Type: NUMBER(6)
Polars Type: pl.Int32
This column is mandatory, and a primary key.
BILLRUNNO
Billing run number
SQL Type: NUMBER(3)
Polars Type: pl.UInt16
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.
CONTRACTYEAR
Billing contract year
SQL Type: NUMBER(4)
Polars Type: pl.UInt16
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.
REGIONID
Region Identifier
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.
WEEKNO
Billing week number
Warning
There may be 53 weeks in a year, depending on how weeks align to the year start/end.
SQL Type: NUMBER(3)
Polars Type: pl.UInt16
This column is mandatory, and a primary key.
ELIGIBILITY_END_INTERVAL
The ending half hourly interval for the eligibility period for recovery of APC Payment
SQL Type: DATE
Polars Type: pl.Datetime()
This column is not mandatory.
Example: 2026/01/20 00:05:00
ELIGIBILITY_START_INTERVAL
The starting half hourly interval for the eligibility period for recovery of APC Payment
SQL Type: DATE
Polars Type: pl.Datetime()
This column is not mandatory.
Example: 2026/01/20 00:05:00
LASTCHANGED
The date and time of last changed 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
PARTICIPANT_ACE_MWH
The ACE MWh value of the participant from the Eligibility Interval used for the APC Recovery Calculation. If the Billing Week is prior to the IESS rule effective date, then value is Null.
SQL Type: NUMBER(18,8)
Polars Type: pl.Float64
This column is not mandatory.
PARTICIPANT_DEMAND
The participant demand in the cost recovery region
SQL Type: NUMBER(18,8)
Polars Type: pl.Float64
This column is not mandatory.
RECOVERY_AMOUNT
Recovery amount attributable to the participant in that region
SQL Type: NUMBER(18,8)
Polars Type: pl.Float64
This column is not mandatory.
REGION_ACE_MWH
The ACE MWh value of the Region from the Eligibility Interval used for the APC Recovery Calculation. This is the sum of the ACE MWh of all the participants in that recovery. If the Billing Week is prior to the IESS rule effective date, then value is Null.
SQL Type: NUMBER(18,8)
Polars Type: pl.Float64
This column is not mandatory.
REGION_DEMAND
The sum of demand of all participants in the cost recovery region (Region Sum)
SQL Type: NUMBER(18,8)
Polars Type: pl.Float64
This column is not mandatory.
New Data Timing
Populated by the posting of a billing run, being several times each week
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(
"ELIGIBILITY_END_INTERVAL", "ELIGIBILITY_START_INTERVAL", "LASTCHANGED"
).str.strptime(pl.Datetime, dt_s_fmt),
)
.cast(
{
"APEVENTID": int, # or pl.Int32
"BILLRUNNO": int, # or pl.UInt16
"CLAIMID": int, # or pl.Int32
"CONTRACTYEAR": int, # or pl.UInt16
"PARTICIPANTID": pl.Categorical,
"REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
"WEEKNO": int, # or pl.UInt16
"PARTICIPANT_ACE_MWH": pl.Float64,
"PARTICIPANT_DEMAND": pl.Float64,
"RECOVERY_AMOUNT": pl.Float64,
"REGION_ACE_MWH": pl.Float64,
"REGION_DEMAND": pl.Float64,
}
)
# Deduplicate
# We want one row per ['APEVENTID', 'BILLRUNNO', 'CLAIMID', 'CONTRACTYEAR', 'PARTICIPANTID', 'REGIONID', 'WEEKNO']
# Tie-breaker: greatest value of LASTCHANGED
.sort(LASTCHANGED, descending=False)
.unique(
subset=[
"APEVENTID",
"BILLRUNNO",
"CLAIMID",
"CONTRACTYEAR",
"PARTICIPANTID",
"REGIONID",
"WEEKNO",
],
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 |
|---|---|---|
| BILLING | APC_RECOVERY | 3 |
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: BILLING_RUN