BILLING_ENERGY_TRANSACTIONS
The Billing Energy Transactions is the summary of the Settlement Energy Transactions that has the ACE and ASOE MWh and Dollar values that is used for the Statement
Column Schema
BILLRUNNO
The Billing RunNo
SQL Type: NUMBER(4,0)
Polars Type: pl.UInt16
This column is mandatory, and a primary key.
CONNECTIONPOINTID
The ConnectionPoint Id for the Billing Aggregation for the Participant Id.
SQL Type: VARCHAR2(20)
Polars Type: pl.Categorical
This column is mandatory, and a primary key.
CONTRACTYEAR
The Billing Contract Year
SQL Type: NUMBER(4,0)
Polars Type: pl.UInt16
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.
REGIONID
The Region Id 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
The Billing WeekNo
Warning
There may be 53 weeks in a year, depending on how weeks align to the year start/end.
SQL Type: NUMBER(3,0)
Polars Type: pl.UInt16
This column is mandatory, and a primary key.
ACE_AMOUNT
The Adjusted Consumed Energy Dollar Amount for that Connection Point for the Participant Id in that Billing Week
SQL Type: NUMBER(18,8)
Polars Type: pl.Float64
This column is not mandatory.
ACE_MWH
The Adjusted Consumed Energy MWh Consumed for that Connection Point for the Participant Id in that Billing Week
SQL Type: NUMBER(18,8)
Polars Type: pl.Float64
This column is not mandatory.
ASOE_AMOUNT
The Adjusted Sent Out Energy Dollar Amount for that Connection Point for the Participant Id in that Billing Week
SQL Type: NUMBER(18,8)
Polars Type: pl.Float64
This column is not mandatory.
ASOE_MWH
The Adjusted Sent Out Energy MWh Consumed for that Connection Point for the Participant Id in that Billing Week
SQL Type: NUMBER(18,8)
Polars Type: pl.Float64
This column is not mandatory.
CE_MWH
The Consumed Energy MWh Consumed for that Connection Point for the Participant Id in that Billing Week
SQL Type: NUMBER(18,8)
Polars Type: pl.Float64
This column is not mandatory.
DME_MWH
The DME MWh for that Connection Point for the Participant Id in that Billing Week. This is the MWh value that is used for the UFEA Allocation.
SQL Type: NUMBER(18,8)
Polars Type: pl.Float64
This column is not mandatory.
LASTCHANGED
The Last Changed date time for 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
TOTAL_AMOUNT
The Total Amount(ACE_Amount + ASOE_Amount) for that Connection Point for the Participant Id in that Billing Week
SQL Type: NUMBER(18,8)
Polars Type: pl.Float64
This column is not mandatory.
TOTAL_MWH
The Total MWh(ACE_MWh + ASOE_MWh) for that Connection Point for the Participant Id in that Billing Week
SQL Type: NUMBER(18,8)
Polars Type: pl.Float64
This column is not mandatory.
UFEA_MWH
The UFEA Energy MWh Consumed for that Connection Point for the Participant Id in that Billing Week
SQL Type: NUMBER(18,8)
Polars Type: pl.Float64
This column is not mandatory.
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("LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
)
.cast(
{
"BILLRUNNO": int, # or pl.UInt16
"CONNECTIONPOINTID": pl.Categorical,
"CONTRACTYEAR": int, # or pl.UInt16
"PARTICIPANTID": pl.Categorical,
"REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
"WEEKNO": int, # or pl.UInt16
"ACE_AMOUNT": pl.Float64,
"ACE_MWH": pl.Float64,
"ASOE_AMOUNT": pl.Float64,
"ASOE_MWH": pl.Float64,
"CE_MWH": pl.Float64,
"DME_MWH": pl.Float64,
"TOTAL_AMOUNT": pl.Float64,
"TOTAL_MWH": pl.Float64,
"UFEA_MWH": pl.Float64,
}
)
# Deduplicate
# We want one row per ['BILLRUNNO', 'CONNECTIONPOINTID', 'CONTRACTYEAR', 'PARTICIPANTID', 'REGIONID', 'WEEKNO']
# Tie-breaker: greatest value of LASTCHANGED
.sort(LASTCHANGED, descending=False)
.unique(
subset=[
"BILLRUNNO",
"CONNECTIONPOINTID",
"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 | ENERGY_TRANSACTION | 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: BILLING_RUN