SSM_INDICATIVE_ROLL_REGION
Indicative rolling Region schedule provides notice of system security status to service providers/TNSP. The intent is to provide the security strength status of a region
Column Schema
INTERVAL_DATETIME
Interval date time
SQL Type: DATE
Polars Type: pl.Datetime()
This column is mandatory, and a primary key.
Example: 2026/01/20 00:05:00
REGIONID
Region
For more information, see the dedicated documentation page.
The SQL type is VARCHAR2(20).
This is actually a factor/enum, with values:
-
QLD1 -
VIC1 -
TAS1 -
NSW1 -
SA1 -
SNOWY1
This column is mandatory, and a primary key.
VERSION_DATETIME
Version date time
SQL Type: DATE
Polars Type: pl.Datetime()
This column is mandatory, and a primary key.
Example: 2026/01/20 00:05:00
DUID_COUNT_FIRM
Sum of firm units enabled for the region with confirmed enablement instruction(s) sent
SQL Type: NUMBER(4,0)
Polars Type: pl.UInt16
This column is not mandatory.
DUID_COUNT_SPECULATIVE
Sum of proposed units enabled for the region where enablement instruction(s) not sent
SQL Type: NUMBER(4,0)
Polars Type: pl.UInt16
This column is not mandatory.
LASTCHANGED
Last changed date
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
REGION_STATUS
Secure, Resolved, Not Secure
SQL Type: VARCHAR2(40)
Polars Type: str
This column is not mandatory.
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", "VERSION_DATETIME", "LASTCHANGED").str.strptime(
pl.Datetime, dt_s_fmt
),
)
.cast(
{
"REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
"DUID_COUNT_FIRM": int, # or pl.UInt16
"DUID_COUNT_SPECULATIVE": int, # or pl.UInt16
"REGION_STATUS": str,
}
)
# Deduplicate
# We want one row per ['INTERVAL_DATETIME', 'REGIONID', 'VERSION_DATETIME']
# Tie-breaker: greatest value of LASTCHANGED
.sort(LASTCHANGED, descending=False)
.unique(subset=["INTERVAL_DATETIME", "REGIONID", "VERSION_DATETIME"], keep="last")
)
Nemosis
This table is not yet available in Nemosis.
Availability
AEMO's documentation states that this table's visibility is "Public". However, it is not available in the public monthly MMSDM archive. AEMO probably call this public because it is available to all market participants over private FTP.
There is a chance that is is still public to non-participants. It may only be available for some months; or (rarely) it may be available in a non-standard location.
Deduplication and PDR Loader Configuration
The primary keys are:
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.
| Report Name | Report Subtype | Version |
|---|---|---|
| SSM | INDICATIVE_ROLL_REGION | 1 |
This means that the CSV files start with metadata columns:
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: SYSTEM_SECURITY_MANAGEMENT