MTPASA_OFFERDATA
Participant submitted Offers for MTPASA process
Column Schema
EFFECTIVEDATE
trade date when the offer becomes effective
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
OFFERDATETIME
Date time file processed
SQL Type: DATE
Polars Type: pl.Datetime()
This column is mandatory, and a primary key.
Example: 2026/01/20 00:05:00
PARTICIPANTID
Unique participant identifier
For more information, see the dedicated documentation page.
SQL Type: VARCHAR2(20)
Polars Type: pl.Categorical
This column is mandatory, and a primary key.
UNITID
either duid or mnsp linkid
SQL Type: VARCHAR2(20)
Polars Type: pl.Categorical
This column is mandatory, and a primary key.
CAPACITY1
capacity value day 1 (sunday)
SQL Type: NUMBER(9)
Polars Type: pl.Int32
This column is not mandatory.
CAPACITY2
capacity value day 2 (monday)
SQL Type: NUMBER(9)
Polars Type: pl.Int32
This column is not mandatory.
CAPACITY3
capacity value day 3 (tuesday)
SQL Type: NUMBER(9)
Polars Type: pl.Int32
This column is not mandatory.
CAPACITY4
capacity value day 4 (wednesday)
SQL Type: NUMBER(9)
Polars Type: pl.Int32
This column is not mandatory.
CAPACITY5
capacity value day 5 (thursday)
SQL Type: NUMBER(9)
Polars Type: pl.Int32
This column is not mandatory.
CAPACITY6
capacity value day 6 (friday)
SQL Type: NUMBER(9)
Polars Type: pl.Int32
This column is not mandatory.
CAPACITY7
capacity value day 7 (saturday)
SQL Type: NUMBER(9)
Polars Type: pl.Int32
This column is not mandatory.
ENERGY
weekly energy constraint value
SQL Type: NUMBER(9)
Polars Type: pl.Int32
This column is not mandatory.
LASTCHANGED
timestamp when record last 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
RECALLTIME1
The recall time associated with the unit state for day 1 Sunday
SQL Type: NUMBER(4)
Polars Type: pl.Int16
This column is not mandatory.
RECALLTIME2
The recall time associated with the unit state for day 2 Monday
SQL Type: NUMBER(4)
Polars Type: pl.Int16
This column is not mandatory.
RECALLTIME3
The recall time associated with the unit state for day 3 Tuesday
SQL Type: NUMBER(4)
Polars Type: pl.Int16
This column is not mandatory.
RECALLTIME4
The recall time associated with the unit state for day 4 Wednesday
SQL Type: NUMBER(4)
Polars Type: pl.Int16
This column is not mandatory.
RECALLTIME5
The recall time associated with the unit state for day 5 Thursday
SQL Type: NUMBER(4)
Polars Type: pl.Int16
This column is not mandatory.
RECALLTIME6
The recall time associated with the unit state for day 6 Friday
SQL Type: NUMBER(4)
Polars Type: pl.Int16
This column is not mandatory.
RECALLTIME7
The recall time associated with the unit state for day 7 Saturday
SQL Type: NUMBER(4)
Polars Type: pl.Int16
This column is not mandatory.
UNITSTATE1
The unit state value for day 1 Sunday
SQL Type: VARCHAR2(20)
Polars Type: str
This column is not mandatory.
UNITSTATE2
The unit state value for day 2 Monday
SQL Type: VARCHAR2(20)
Polars Type: str
This column is not mandatory.
UNITSTATE3
The unit state value for day 3 Tuesday
SQL Type: VARCHAR2(20)
Polars Type: str
This column is not mandatory.
UNITSTATE4
The unit state value for 4 Wednesday
SQL Type: VARCHAR2(20)
Polars Type: str
This column is not mandatory.
UNITSTATE5
The unit state value for day 5 Thursday
SQL Type: VARCHAR2(20)
Polars Type: str
This column is not mandatory.
UNITSTATE6
The unit state value for day 6 Friday
SQL Type: VARCHAR2(20)
Polars Type: str
This column is not mandatory.
UNITSTATE7
The unit state value for day 7 Saturday
SQL Type: VARCHAR2(20)
Polars Type: str
This column is not mandatory.
New Data Timing
When a participant MTPASA bid is processed
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", "OFFERDATETIME", "LASTCHANGED").str.strptime(
pl.Datetime, dt_s_fmt
),
)
.cast(
{
"PARTICIPANTID": pl.Categorical,
"UNITID": pl.Categorical,
"CAPACITY1": int, # or pl.Int32
"CAPACITY2": int, # or pl.Int32
"CAPACITY3": int, # or pl.Int32
"CAPACITY4": int, # or pl.Int32
"CAPACITY5": int, # or pl.Int32
"CAPACITY6": int, # or pl.Int32
"CAPACITY7": int, # or pl.Int32
"ENERGY": int, # or pl.Int32
"RECALLTIME1": int, # or pl.Int16
"RECALLTIME2": int, # or pl.Int16
"RECALLTIME3": int, # or pl.Int16
"RECALLTIME4": int, # or pl.Int16
"RECALLTIME5": int, # or pl.Int16
"RECALLTIME6": int, # or pl.Int16
"RECALLTIME7": int, # or pl.Int16
"UNITSTATE1": str,
"UNITSTATE2": str,
"UNITSTATE3": str,
"UNITSTATE4": str,
"UNITSTATE5": str,
"UNITSTATE6": str,
"UNITSTATE7": str,
}
)
# Deduplicate
# We want one row per ['EFFECTIVEDATE', 'OFFERDATETIME', 'PARTICIPANTID', 'UNITID']
# Tie-breaker: greatest value of ['LASTCHANGED']
.sort(["LASTCHANGED"], descending=False)
.unique(
subset=["EFFECTIVEDATE", "OFFERDATETIME", "PARTICIPANTID", "UNITID"],
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 |
|---|---|---|
| OFFER | MTPASA_OFFERDATA | 1 |
| OFFER | MTPASA_OFFERDATA | 2 |
This means that the CSV files start with metadata columns:
or
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: BIDS