CONTRACTRESTARTUNITS
CONTRACTRESTARTUNITS shows Restart units provided under a system restart contract. A service can have multiple units.
CONTRACTRESTARTUNITS data is confidential to each participant with a restart contract.
Source
CONTRACTRESTARTUNITS updates only where there is a contract variation.
Column Schema
CONTRACTID
Contract Identifier
SQL Type: VARCHAR2(10)
Polars Type: pl.Categorical
This column is mandatory, and a primary key.
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.
Example: W/HOE#2
VERSIONNO
Version No of contract
SQL Type: NUMBER(3,0)
Polars Type: pl.UInt16
This column is mandatory, and a primary key.
AUTHORISEDBY
SQL Type: VARCHAR2(15)
Polars Type: str
This column is not mandatory.
AUTHORISEDDATE
Warning
Despite what the name suggests, this is a datetime, not a date.
SQL Type: DATE
Polars Type: pl.Datetime()
This column is not mandatory.
Example: 2026/01/20 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: 2026/01/20 00:05:00
New Data Timing
When there is a restart contract
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("AUTHORISEDDATE", "LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
)
.cast(
{
"CONTRACTID": pl.Categorical,
"DUID": pl.Categorical,
"VERSIONNO": int, # or pl.UInt16
"AUTHORISEDBY": str,
}
)
# Deduplicate
# We want one row per ['CONTRACTID', 'DUID', 'VERSIONNO']
# Tie-breaker: greatest value of ['LASTCHANGED']
.sort(["LASTCHANGED"], descending=False)
.unique(subset=["CONTRACTID", "DUID", "VERSIONNO"], 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.
| Report Name | Report Subtype | Version |
|---|---|---|
| ANCILLIARY_SERVICES | CONTRACTRESTARTUNITS | 1 |
This means that the CSV files start with metadata columns:
ROW_FILTER_TYPE is LASTCHANGED
Indexes
There is more than one index.
Index 1:
Index 2:
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: ANCILLARY_SERVICES