BILLING_WDR_DETAIL
Billing WDR transaction detail summary
Column Schema
BILLRUNNO
Billing run number identifier
SQL Type: NUMBER(3,0)
Polars Type: pl.UInt16
This column is mandatory, and a primary key.
CONTRACTYEAR
Contract year of the Billing run
SQL Type: NUMBER(4,0)
Polars Type: pl.UInt16
This column is mandatory, and a primary key.
DRSP
Demand Response Service Provider Identifier
SQL Type: VARCHAR2(20)
Polars Type: str
This column is mandatory, and a primary key.
FRMP
Financial Responsible Market Participant Identifier
SQL Type: VARCHAR2(20)
Polars Type: str
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.
WDRRRPERIOD
Unique identifier for the period to which the WDRRR applies. For quarter-based periods, this will be equal to YYYY[Q]NN, for example, 2020Q3 for 2020 Quarter 3.
SQL Type: VARCHAR2(20)
Polars Type: str
This column is mandatory, and a primary key.
WEEKNO
Week number of the Billing run
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.
WDRRR
WDR reimbursement rate in $/MWh
SQL Type: NUMBER(18,8)
Polars Type: pl.Float64
This column is not mandatory.
WDRSQ
WDR Settlement Quantity capped in MWh
SQL Type: NUMBER(18,8)
Polars Type: pl.Float64
This column is not mandatory.
WDRTA
WDR transaction amount in $ for demand response
SQL Type: NUMBER(18,8)
Polars Type: pl.Float64
This column is not mandatory.
New Data Timing
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
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",
).cast(
{
"BILLRUNNO": int, # or pl.UInt16
"CONTRACTYEAR": int, # or pl.UInt16
"DRSP": str,
"FRMP": str,
"REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
"WDRRRPERIOD": str,
"WEEKNO": int, # or pl.UInt16
"WDRRR": pl.Float64,
"WDRSQ": pl.Float64,
"WDRTA": pl.Float64,
}
)
# Deduplicate
# We want one row per ['BILLRUNNO', 'CONTRACTYEAR', 'DRSP', 'FRMP', 'REGIONID', 'WDRRRPERIOD', 'WEEKNO']
.unique(
subset=[
"BILLRUNNO",
"CONTRACTYEAR",
"DRSP",
"FRMP",
"REGIONID",
"WDRRRPERIOD",
"WEEKNO",
],
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:
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 | WDR_DETAIL | 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