DISPATCHABLEUNIT
DISPATCHABLEUNIT sets out the unit name and type of each dispatchable unit in the market.
DISPATCHABLEUNIT data is public data, and is available to all participants.
Source
DISPATCHABLEUNIT pdates as new units added or names changed.
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(10)
Polars Type: pl.Categorical
This column is mandatory, and a primary key.
Examples:
ADPBA1ADPBA1GADPBA1LADPMH1ADPPV1ADPPV2ADPPV3AGLHALAGLNOW1AGLSITA1
DUNAME
Dispatchable Unit full description
SQL Type: VARCHAR2(20)
Polars Type: str
This column is not mandatory.
Examples:
ACTEWAGL VPPADAP UNDER FREQ LADELAIDE DESAL BESSADELAIDE DESAL BG1ADELAIDE DESAL BL1ADELAIDE DESAL HYDROADELAIDE DESAL PV1ADELAIDE DESAL PV2ADELAIDE DESAL PV3AGG INTERRUP LOAD N1
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: 2004/06/22 10:59:26
UNITTYPE
Identifies LOAD, GENERATOR or BIDIRECTIONAL
SQL Type: VARCHAR2(20)
Polars Type: pl.Categorical
This column is not mandatory.
Examples:
BIDIRECTIONALGENERATORLOAD
New Data Timing
When new units are added or names are changed
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({"DUID": pl.Categorical, "DUNAME": str, "UNITTYPE": pl.Categorical})
# Deduplicate
# We want one row per ['DUID']
# Tie-breaker: greatest value of ['LASTCHANGED']
.sort(["LASTCHANGED"], descending=False)
.unique(subset=["DUID"], keep="last")
)
Nemosis
This table is not yet available in Nemosis.
Example File
From PUBLIC_ARCHIVE_DISPATCHABLEUNIT_FILE01_202609010000.zip
| DUID | DUNAME | UNITTYPE | LASTCHANGED |
| CALL_A_1 | CALL_A_1 CALLIDE | GENERATOR | 2018/03/09 13:45:31 |
| CALL_A_2 | CALL_A_2 CALLIDE | GENERATOR | 2018/03/09 13:45:31 |
| CALL_A_3 | CALL_A_3 CALLIDE | GENERATOR | 2018/03/09 13:45:31 |
| CALL_A_4 | CALL_A_4 CALLIDE | GENERATOR | 2011/05/16 09:55:13 |
| CALL_B_1 | CALL_B_1 CALLIDE | GENERATOR | 2005/06/08 15:37:19 |
| CALL_B_2 | CALL_B_2 CALLIDE | GENERATOR | 2005/06/08 15:37:19 |
| MRIDGE | MRIDGE SWANBANK | GENERATOR | 2006/05/19 11:34:07 |
| SWAN_A_1 | SWAN_A_1 SWANBANK | GENERATOR | 2006/05/26 14:18:55 |
| SWAN_A_2 | SWAN_A_2 SWANBANK | GENERATOR | 2006/05/26 14:18:55 |
| SWAN_A_3 | SWAN_A_3 SWANBANK | GENERATOR | 2006/05/26 14:18:56 |
| SWAN_A_4 | SWAN_A_4 SWANBANK | GENERATOR | 2006/05/26 14:18:56 |
| ... |
| C | SETP.WORLD | DVD_DISPATCHABLEUNIT | AEMO | PUBLIC | 2026/10/09 | 15:25:32 | 001791523532683 | MONTHLY_ARCHIVE | 001791523532683 |
| I | PARTICIPANT_REGISTRATION | DISPATCHABLEUNIT | 1 | DUID | DUNAME | UNITTYPE | LASTCHANGED | ||
| D | PARTICIPANT_REGISTRATION | DISPATCHABLEUNIT | 1 | CALL_A_1 | CALL_A_1 CALLIDE | GENERATOR | 2018/03/09 13:45:31 | ||
| D | PARTICIPANT_REGISTRATION | DISPATCHABLEUNIT | 1 | CALL_A_2 | CALL_A_2 CALLIDE | GENERATOR | 2018/03/09 13:45:31 | ||
| D | PARTICIPANT_REGISTRATION | DISPATCHABLEUNIT | 1 | CALL_A_3 | CALL_A_3 CALLIDE | GENERATOR | 2018/03/09 13:45:31 | ||
| D | PARTICIPANT_REGISTRATION | DISPATCHABLEUNIT | 1 | CALL_A_4 | CALL_A_4 CALLIDE | GENERATOR | 2011/05/16 09:55:13 | ||
| D | PARTICIPANT_REGISTRATION | DISPATCHABLEUNIT | 1 | CALL_B_1 | CALL_B_1 CALLIDE | GENERATOR | 2005/06/08 15:37:19 | ||
| D | PARTICIPANT_REGISTRATION | DISPATCHABLEUNIT | 1 | CALL_B_2 | CALL_B_2 CALLIDE | GENERATOR | 2005/06/08 15:37:19 | ||
| D | PARTICIPANT_REGISTRATION | DISPATCHABLEUNIT | 1 | MRIDGE | MRIDGE SWANBANK | GENERATOR | 2006/05/19 11:34:07 | ||
| D | PARTICIPANT_REGISTRATION | DISPATCHABLEUNIT | 1 | SWAN_A_1 | SWAN_A_1 SWANBANK | GENERATOR | 2006/05/26 14:18:55 | ||
| D | PARTICIPANT_REGISTRATION | DISPATCHABLEUNIT | 1 | SWAN_A_2 | SWAN_A_2 SWANBANK | GENERATOR | 2006/05/26 14:18:55 | ||
| D | PARTICIPANT_REGISTRATION | DISPATCHABLEUNIT | 1 | SWAN_A_3 | SWAN_A_3 SWANBANK | GENERATOR | 2006/05/26 14:18:56 | ||
| D | PARTICIPANT_REGISTRATION | DISPATCHABLEUNIT | 1 | SWAN_A_4 | SWAN_A_4 SWANBANK | GENERATOR | 2006/05/26 14:18:56 | ||
| ... | |||||||||
| C | END OF REPORT | 1003 |
DUID,DUNAME,UNITTYPE,LASTCHANGED
CALL_A_1,"CALL_A_1 CALLIDE",GENERATOR,"2018/03/09 13:45:31"
CALL_A_2,"CALL_A_2 CALLIDE",GENERATOR,"2018/03/09 13:45:31"
CALL_A_3,"CALL_A_3 CALLIDE",GENERATOR,"2018/03/09 13:45:31"
CALL_A_4,"CALL_A_4 CALLIDE",GENERATOR,"2011/05/16 09:55:13"
CALL_B_1,"CALL_B_1 CALLIDE",GENERATOR,"2005/06/08 15:37:19"
CALL_B_2,"CALL_B_2 CALLIDE",GENERATOR,"2005/06/08 15:37:19"
MRIDGE,"MRIDGE SWANBANK",GENERATOR,"2006/05/19 11:34:07"
SWAN_A_1,"SWAN_A_1 SWANBANK",GENERATOR,"2006/05/26 14:18:55"
SWAN_A_2,"SWAN_A_2 SWANBANK",GENERATOR,"2006/05/26 14:18:55"
SWAN_A_3,"SWAN_A_3 SWANBANK",GENERATOR,"2006/05/26 14:18:56"
SWAN_A_4,"SWAN_A_4 SWANBANK",GENERATOR,"2006/05/26 14:18:56"
...
C,SETP.WORLD,DVD_DISPATCHABLEUNIT,AEMO,PUBLIC,2026/10/09,15:25:32,001791523532683,MONTHLY_ARCHIVE,001791523532683
I,PARTICIPANT_REGISTRATION,DISPATCHABLEUNIT,1,DUID,DUNAME,UNITTYPE,LASTCHANGED
D,PARTICIPANT_REGISTRATION,DISPATCHABLEUNIT,1,CALL_A_1,"CALL_A_1 CALLIDE",GENERATOR,"2018/03/09 13:45:31"
D,PARTICIPANT_REGISTRATION,DISPATCHABLEUNIT,1,CALL_A_2,"CALL_A_2 CALLIDE",GENERATOR,"2018/03/09 13:45:31"
D,PARTICIPANT_REGISTRATION,DISPATCHABLEUNIT,1,CALL_A_3,"CALL_A_3 CALLIDE",GENERATOR,"2018/03/09 13:45:31"
D,PARTICIPANT_REGISTRATION,DISPATCHABLEUNIT,1,CALL_A_4,"CALL_A_4 CALLIDE",GENERATOR,"2011/05/16 09:55:13"
D,PARTICIPANT_REGISTRATION,DISPATCHABLEUNIT,1,CALL_B_1,"CALL_B_1 CALLIDE",GENERATOR,"2005/06/08 15:37:19"
D,PARTICIPANT_REGISTRATION,DISPATCHABLEUNIT,1,CALL_B_2,"CALL_B_2 CALLIDE",GENERATOR,"2005/06/08 15:37:19"
D,PARTICIPANT_REGISTRATION,DISPATCHABLEUNIT,1,MRIDGE,"MRIDGE SWANBANK",GENERATOR,"2006/05/19 11:34:07"
D,PARTICIPANT_REGISTRATION,DISPATCHABLEUNIT,1,SWAN_A_1,"SWAN_A_1 SWANBANK",GENERATOR,"2006/05/26 14:18:55"
D,PARTICIPANT_REGISTRATION,DISPATCHABLEUNIT,1,SWAN_A_2,"SWAN_A_2 SWANBANK",GENERATOR,"2006/05/26 14:18:55"
D,PARTICIPANT_REGISTRATION,DISPATCHABLEUNIT,1,SWAN_A_3,"SWAN_A_3 SWANBANK",GENERATOR,"2006/05/26 14:18:56"
D,PARTICIPANT_REGISTRATION,DISPATCHABLEUNIT,1,SWAN_A_4,"SWAN_A_4 SWANBANK",GENERATOR,"2006/05/26 14:18:56"
...
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 203 KB when zipped, 2 MB 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.
| Report Name | Report Subtype | Version |
|---|---|---|
| PARTICIPANT_REGISTRATION | DISPATCHABLEUNIT | 1 |
This means that the CSV files start with metadata columns:
ROW_FILTER_TYPE is LASTCHANGED
Indexes
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: PARTICIPANT_REGISTRATION