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DISPATCH_UNIT_SCADA

Dispatchable unit MW from SCADA at the start of the dispatch interval. The table includes all scheduled and semi-scheduled (and non-scheduled units where SCADA is available)

DISPATCH_UNIT_SCADA data is public data, and is available to all participants.

Source

DISPATCH_UNIT_SCADA shows data for every 5 minutes for all scheduled units

Volume

Rows per day: 288 per each scheduled, semi-scheduled (and non-scheduled unit where SCADA is available)

Tip

Table DISPATCHLOAD is similar but contains even more detail (but is larger).

Column Schema

DUID

Dispatchable Unit 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.

Examples:

  • ADPBA1
  • ADPPV1
  • AGLHAL
  • AGLSOM
  • ALDGASF1
  • ANGAST1
  • ARWF1
  • AVLSF1
  • BALB1
  • BALDHWF1

SETTLEMENTDATE

Date Time of the Dispatch Interval

Warning

Despite what the name suggests, this is a datetime, not a date.

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

This column is mandatory, and a primary key.

Example: 2026/09/01 00:05:00

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: 2026/09/01 00:00:13

SCADAVALUE

Instantaneous MW reading from SCADA at the start of the Dispatch interval

Warning

In AEMO data, datetimes usually refer to the end of an interval. Power values usually do too. However this column refers to the start of the interval. So you need to subtract 5 minutes from SETTLEMENTDATE to align it with INITIALMW.

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

This column is not mandatory.

Examples: -0.02, 0.00001, 0, 531

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("SETTLEMENTDATE", "LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast({"DUID": pl.Categorical, "SCADAVALUE": pl.Float64})
    # Deduplicate
    # We want one row per ['DUID', 'SETTLEMENTDATE']
    # Tie-breaker: greatest value of ['SETTLEMENTDATE', 'DUID']
    .sort(["SETTLEMENTDATE", "DUID"], descending=False)
    .unique(subset=["DUID", "SETTLEMENTDATE"], keep="last")
)

Nemosis

from nemosis import dynamic_data_compiler

df = dynamic_data_compiler(
    '2024/01/01 00:00:00',
    '2024/01/31 00:00:00',
    "DISPATCH_UNIT_SCADA",
    "data/"
)

Nemosis only includes a subset of columns by default, as an optimisation. (i.e. the commonly used columns.) To include others:

from nemosis import defaults

defaults.table_columns["DISPATCH_UNIT_SCADA"] += ["LASTCHANGED"]
df = dynamic_data_compiler(...)

Example File

From PUBLIC_ARCHIVE_DISPATCH_UNIT_SCADA_FILE01_202609010000.zip

SETTLEMENTDATEDUIDSCADAVALUELASTCHANGED
2026/09/01 00:05:00ADPBA10.0062026/09/01 00:00:13
2026/09/01 00:05:00ADPPV102026/09/01 00:00:13
2026/09/01 00:05:00AGLHAL02026/09/01 00:00:13
2026/09/01 00:05:00AGLSOM02026/09/01 00:00:13
2026/09/01 00:05:00ALDGASF102026/09/01 00:00:13
2026/09/01 00:05:00ANGAST1-0.22026/09/01 00:00:13
2026/09/01 00:05:00ARWF183.32026/09/01 00:00:13
2026/09/01 00:05:00AVLSF102026/09/01 00:00:13
2026/09/01 00:05:00BALB102026/09/01 00:00:13
2026/09/01 00:05:00BALDHWF133.9512026/09/01 00:00:13
2026/09/01 00:05:00BANGOWF102026/09/01 00:00:13
...

CSETP.WORLDDVD_DISPATCH_UNIT_SCADAAEMOPUBLIC2026/10/0915:25:08001791523508739MONTHLY_ARCHIVE001791523508739
IDISPATCHUNIT_SCADA1SETTLEMENTDATEDUIDSCADAVALUELASTCHANGED
DDISPATCHUNIT_SCADA12026/09/01 00:05:00ADPBA10.0062026/09/01 00:00:13
DDISPATCHUNIT_SCADA12026/09/01 00:05:00ADPPV102026/09/01 00:00:13
DDISPATCHUNIT_SCADA12026/09/01 00:05:00AGLHAL02026/09/01 00:00:13
DDISPATCHUNIT_SCADA12026/09/01 00:05:00AGLSOM02026/09/01 00:00:13
DDISPATCHUNIT_SCADA12026/09/01 00:05:00ALDGASF102026/09/01 00:00:13
DDISPATCHUNIT_SCADA12026/09/01 00:05:00ANGAST1-0.22026/09/01 00:00:13
DDISPATCHUNIT_SCADA12026/09/01 00:05:00ARWF183.32026/09/01 00:00:13
DDISPATCHUNIT_SCADA12026/09/01 00:05:00AVLSF102026/09/01 00:00:13
DDISPATCHUNIT_SCADA12026/09/01 00:05:00BALB102026/09/01 00:00:13
DDISPATCHUNIT_SCADA12026/09/01 00:05:00BALDHWF133.9512026/09/01 00:00:13
DDISPATCHUNIT_SCADA12026/09/01 00:05:00BANGOWF102026/09/01 00:00:13
...
CEND OF REPORT1003

SETTLEMENTDATE,DUID,SCADAVALUE,LASTCHANGED
"2026/09/01 00:05:00",ADPBA1,0.006,"2026/09/01 00:00:13"
"2026/09/01 00:05:00",ADPPV1,0,"2026/09/01 00:00:13"
"2026/09/01 00:05:00",AGLHAL,0,"2026/09/01 00:00:13"
"2026/09/01 00:05:00",AGLSOM,0,"2026/09/01 00:00:13"
"2026/09/01 00:05:00",ALDGASF1,0,"2026/09/01 00:00:13"
"2026/09/01 00:05:00",ANGAST1,-0.2,"2026/09/01 00:00:13"
"2026/09/01 00:05:00",ARWF1,83.3,"2026/09/01 00:00:13"
"2026/09/01 00:05:00",AVLSF1,0,"2026/09/01 00:00:13"
"2026/09/01 00:05:00",BALB1,0,"2026/09/01 00:00:13"
"2026/09/01 00:05:00",BALDHWF1,33.951,"2026/09/01 00:00:13"
"2026/09/01 00:05:00",BANGOWF1,0,"2026/09/01 00:00:13"
...

C,SETP.WORLD,DVD_DISPATCH_UNIT_SCADA,AEMO,PUBLIC,2026/10/09,15:25:08,001791523508739,MONTHLY_ARCHIVE,001791523508739
I,DISPATCH,UNIT_SCADA,1,SETTLEMENTDATE,DUID,SCADAVALUE,LASTCHANGED
D,DISPATCH,UNIT_SCADA,1,"2026/09/01 00:05:00",ADPBA1,0.006,"2026/09/01 00:00:13"
D,DISPATCH,UNIT_SCADA,1,"2026/09/01 00:05:00",ADPPV1,0,"2026/09/01 00:00:13"
D,DISPATCH,UNIT_SCADA,1,"2026/09/01 00:05:00",AGLHAL,0,"2026/09/01 00:00:13"
D,DISPATCH,UNIT_SCADA,1,"2026/09/01 00:05:00",AGLSOM,0,"2026/09/01 00:00:13"
D,DISPATCH,UNIT_SCADA,1,"2026/09/01 00:05:00",ALDGASF1,0,"2026/09/01 00:00:13"
D,DISPATCH,UNIT_SCADA,1,"2026/09/01 00:05:00",ANGAST1,-0.2,"2026/09/01 00:00:13"
D,DISPATCH,UNIT_SCADA,1,"2026/09/01 00:05:00",ARWF1,83.3,"2026/09/01 00:00:13"
D,DISPATCH,UNIT_SCADA,1,"2026/09/01 00:05:00",AVLSF1,0,"2026/09/01 00:00:13"
D,DISPATCH,UNIT_SCADA,1,"2026/09/01 00:05:00",BALB1,0,"2026/09/01 00:00:13"
D,DISPATCH,UNIT_SCADA,1,"2026/09/01 00:05:00",BALDHWF1,33.951,"2026/09/01 00:00:13"
D,DISPATCH,UNIT_SCADA,1,"2026/09/01 00:05:00",BANGOWF1,0,"2026/09/01 00:00:13"
...
C,"END OF REPORT",1003

Availability

Visibility: Public

This table is in the public monthly MMSDM archive.

I have collated the list of all those files into one place, in this CSV metadata file. (Columns year, month, url, compressed_size in bytes).

The earliest available data is month 1 of 2015. The most recent available data is month 9 of 2026.

Every month in between has a file. (No gaps.)

Data Size

Each year of data is approximately 397 MB when zipped, 4 GB as unzipped CSV.

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
DISPATCH UNIT_SCADA 1

This means that the CSV files start with metadata columns:

I,DISPATCH,UNIT_SCADA,1,...
D,DISPATCH,UNIT_SCADA,1,...

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: DISPATCH