INTERCONNECTORALLOC
INTERCONNECTORALLOC shows allocations of interconnector residues to Network Service Providers.
INTERCONNECTORALLOC data is confidential to the relevant participant.
Source
INTERCONNECTORALLOC changes infrequently, typically annually.
Column Schema
EFFECTIVEDATE
Effective Date of Allocation Details
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: 2026/01/20 00:05:00
INTERCONNECTORID
Interconnector identifier
SQL Type: VARCHAR2(10)
Polars Type: pl.Categorical
This column is mandatory, and a primary key.
PARTICIPANTID
Unique participant identifier
For more information, see the dedicated documentation page.
SQL Type: VARCHAR2(10)
Polars Type: pl.Categorical
This column is mandatory, and a primary key.
REGIONID
Region Identifier
For more information, see the dedicated documentation page.
The SQL type is VARCHAR2(10).
This is actually a factor/enum, with values:
-
QLD1 -
VIC1 -
TAS1 -
NSW1 -
SA1 -
SNOWY1
This column is mandatory, and a primary key.
VERSIONNO
Version No in respect to effective date
SQL Type: NUMBER(5,0)
Polars Type: pl.UInt32
This column is mandatory, and a primary key.
ALLOCATION
Allocation % / 100
SQL Type: NUMBER(12,5)
Polars Type: pl.Float64
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
New Data Timing
Usually annually
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("EFFECTIVEDATE", "LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
)
.cast(
{
"INTERCONNECTORID": pl.Categorical,
"PARTICIPANTID": pl.Categorical,
"REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
"VERSIONNO": int, # or pl.UInt32
"ALLOCATION": pl.Float64,
}
)
# Deduplicate
# We want one row per ['EFFECTIVEDATE', 'INTERCONNECTORID', 'PARTICIPANTID', 'REGIONID', 'VERSIONNO']
# Tie-breaker: greatest value of ['LASTCHANGED']
.sort(["LASTCHANGED"], descending=False)
.unique(
subset=[
"EFFECTIVEDATE",
"INTERCONNECTORID",
"PARTICIPANTID",
"REGIONID",
"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", 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 |
|---|---|---|
| MARKET_CONFIG | INTERCONNECTORALLOC | 1 |
This means that the CSV files start with metadata columns:
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: MARKET_CONFIG