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BILLING_RES_TRADER_RECOVERY

Billing result table for reserve trader contract recovery

Deprecation Warning

This table is in the HISTORICAL TABLES package, so is likely deprecated.

Column Schema

BILLRUNNO

Billing run number

SQL Type: NUMBER(3) Polars Type: pl.UInt16

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 id for the aggregated 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.

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.

RECOVERY_AMOUNT

Payment amount to be recovered from the participant

SQL Type: NUMBER(18,8) Polars Type: pl.Float64

This column is not mandatory.

New Data Timing

When there is a reserve trader contract recovery

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
            "PARTICIPANTID": pl.Categorical,
            "REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
            "WEEKNO": int,  # or pl.UInt16
            "RECOVERY_AMOUNT": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['BILLRUNNO', 'CONTRACTYEAR', 'PARTICIPANTID', 'REGIONID', 'WEEKNO']
    .unique(
        subset=["BILLRUNNO", "CONTRACTYEAR", "PARTICIPANTID", "REGIONID", "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:

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.

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: HISTORICAL TABLES