DISPATCH_UNIT_CONFORMANCE

DISPATCH_UNIT_CONFORMANCE details the conformance of a scheduled units operation with respect to a cleared target on dispatch interval basis. Data is confidential

DISPATCH_UNIT_CONFORMANCE data is confidential.

Source

DISPATCH_UNIT_CONFORMANCE shows data for every 5 minutes for all scheduled units

Volume

Rows per day: 288 per scheduled unit

Tip

1 second, 6 second, 5 minute, 60 second and regulation refer to types of FCAS markets.

Column Schema

DUID

Dispatchable Unit Identifier, or Aggregate Dispatch Group identifier

Warning

This column may contain funny characters such as / (slash) or # (hash). This may break hive-style partitioning.

For more information, see the dedicated documentation page.

SQL Type: VARCHAR2(20) Polars Type: pl.Categorical

This column is mandatory, and a primary key.

Example: W/HOE#2

INTERVAL_DATETIME

Dispatch Interval that the conformance data applies to

SQL Type: DATE Polars Type: pl.Datetime()

This column is mandatory, and a primary key.

Example: 2026/01/20 00:05:00

ACTUALMW

Unit output measured at the conclusion of the dispatch interval - MW (MWB)

SQL Type: NUMBER(16,6) Polars Type: pl.Float64

This column is not mandatory.

ADG_ID

Aggregate Dispatch Group to which this dispatch unit belongs

SQL Type: VARCHAR2(20) Polars Type: str

This column is not mandatory.

AVAILABILITY

Offered unit capacity - MW (MWO)

SQL Type: NUMBER(16,6) Polars Type: pl.Float64

This column is not mandatory.

CONFORMANCE_MODE

For an individual unit in an aggregate dispatch group (where DUID <> ADG_ID), Mode specific to that unit. 0 - no monitoring, 1 - aggregate monitoring, 2 - individual monitoring due to constraint. For the aggregate dispatch group (where DUID = ADG_ID), 0 - no aggregate monitoring, 1 - aggregate monitoring

SQL Type: NUMBER(6,0) Polars Type: pl.Int32

This column is not mandatory.

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

LECOUNT

Large trigger error count. Reset when mwerror changes sign

SQL Type: NUMBER(6) Polars Type: pl.Int32

This column is not mandatory.

LOWERREG

Lower Regulation FCAS enabled - MW (FCL)

SQL Type: NUMBER(16,6) Polars Type: pl.Float64

This column is not mandatory.

LTRIGLM

Calculated large trigger error limit in MW

SQL Type: NUMBER(16,6) Polars Type: pl.Float64

This column is not mandatory.

MAX_MWERROR

Max of mwerror while that unit was not in a normal state

SQL Type: NUMBER(16,6) Polars Type: pl.Float64

This column is not mandatory.

MWERROR

Calculated actual error

SQL Type: NUMBER(16,6) Polars Type: pl.Float64

This column is not mandatory.

OPERATING_MODE

conformance operating mode MANUAL AUTO

SQL Type: VARCHAR2(20) Polars Type: str

This column is not mandatory.

PARTICIPANT_STATUS_ACTION

Participant action required in response to current STATUS

SQL Type: VARCHAR2(100) Polars Type: str

This column is not mandatory.

RAISEREG

Raise Regulation FCAS enabled - MW (FCR)

SQL Type: NUMBER(16,6) Polars Type: pl.Float64

This column is not mandatory.

ROC

Rate of change in direction of error MW per minute

SQL Type: NUMBER(16,6) Polars Type: pl.Float64

This column is not mandatory.

SECOUNT

Small trigger error count. Reset when mwerror changes sign

SQL Type: NUMBER(6) Polars Type: pl.Int32

This column is not mandatory.

SEMIDISPATCHCAP

Boolean representation flagging if the Target is capped

SQL Type: NUMBER(3,0) Polars Type: pl.Boolean

This column is not mandatory.

STATUS

Unit conformance status. NORMAL OFF-TARGET NOT-RESPONDING NC-PENDING NON-CONFORMING SUSPENDED

SQL Type: VARCHAR2(20) Polars Type: str

This column is not mandatory.

STRIGLM

Calculated small trigger error limit in MW

SQL Type: NUMBER(16,6) Polars Type: pl.Float64

This column is not mandatory.

TOTALCLEARED

Dispatch Target - MW

SQL Type: NUMBER(16,6) Polars Type: pl.Float64

This column is not mandatory.

New Data Timing

Every 5 minutes

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("INTERVAL_DATETIME", "LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
    )
    # Polars can't cast string straight to bool. Need to go via something else.
    .cast({"SEMIDISPATCHCAP": pl.UInt8})
    .cast(
        {
            "DUID": pl.Categorical,
            "ACTUALMW": pl.Float64,
            "ADG_ID": str,
            "AVAILABILITY": pl.Float64,
            "CONFORMANCE_MODE": int,  # or pl.Int32
            "LECOUNT": int,  # or pl.Int32
            "LOWERREG": pl.Float64,
            "LTRIGLM": pl.Float64,
            "MAX_MWERROR": pl.Float64,
            "MWERROR": pl.Float64,
            "OPERATING_MODE": str,
            "PARTICIPANT_STATUS_ACTION": str,
            "RAISEREG": pl.Float64,
            "ROC": pl.Float64,
            "SECOUNT": int,  # or pl.Int32
            "SEMIDISPATCHCAP": pl.Boolean,
            "STATUS": str,
            "STRIGLM": pl.Float64,
            "TOTALCLEARED": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['DUID', 'INTERVAL_DATETIME']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(subset=["DUID", "INTERVAL_DATETIME"], 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:

• DUID

• INTERVAL_DATETIME

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.

PDR Loader configuration is:

Report Name	Report Subtype	Version
DISPATCH	UNIT_CONFORMANCE	1
DISPATCH	UNIT_CONFORMANCE	2

This means that the CSV files start with metadata columns:

I,DISPATCH,UNIT_CONFORMANCE,1,...
D,DISPATCH,UNIT_CONFORMANCE,1,...

or

I,DISPATCH,UNIT_CONFORMANCE,2,...
D,DISPATCH,UNIT_CONFORMANCE,2,...

Indexes

• LASTCHANGED

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: DISPATCH

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