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REALLOCATIONINTERVAL

30-minute or (5-minute for 5MS) data comprising a single reallocation transaction.

Column Schema

PERIODID

Trading Interval

SQL Type: NUMBER(3) Polars Type: pl.Int16

This column is mandatory, and a primary key.

REALLOCATIONID

Reallocation identifier

SQL Type: VARCHAR2(20) Polars Type: str

This column is mandatory, and a primary key.

LASTCHANGED

Last date and time record changed

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

NRP

Nominated Reallocation Price, only used in agreement types of SWAP, CAP and FLOOR, being the contract strike price in $/MWh

SQL Type: NUMBER(15,5) Polars Type: pl.Float64

This column is not mandatory.

VALUE

Reallocation value in the units of the agreement type

SQL Type: NUMBER(15,5) Polars Type: pl.Float64

This column is not mandatory.

New Data Timing

30-minute or (5-minute for 5MS) data

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(
        {
            "PERIODID": int,  # or pl.Int16
            "REALLOCATIONID": str,
            "NRP": pl.Float64,
            "VALUE": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['PERIODID', 'REALLOCATIONID']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(subset=["PERIODID", "REALLOCATIONID"], 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-UPDATE", which means that you will likely see duplicate data with different values.

You should deduplicate by sorting based on LASTCHANGED, within each group of primary keys.

PDR Loader configuration is:

Report Name Report Subtype Version
SETCFG REALLOCATIONINTERVAL 1

This means that the CSV files start with metadata columns:

I,SETCFG,REALLOCATIONINTERVAL,1,...
D,SETCFG,REALLOCATIONINTERVAL,1,...

ROW_FILTER_TYPE is LASTCHANGED

Indexes

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_CONFIG