BIDOFFERFILETRK
BIDOFFERFILETRK shows an audit trail of all files submitted containing ENERGY/FCAS/MNSP bid, including corrupt bids and rebids.
BIDOFFERFILETRK data is confidential to the submitting participant.
The new ancillary service arrangements require availability and prices for each Frequency Control Ancillary Service to be bid on a similar basis to energy. Three new tables facilitate ancillary service bidding. The new tables (BIDOFFERFILETRK,
BIDDAYOFFERandBIDOFFERPERIOD) are similar in structure to energy bidding tables (OFFERFILETRK,DAYOFFERandPEROFFER). The significant differences with the new tables are.· The
OFFERDATEfield reflects the time the bid was loaded and this field alone provides the key for versioning of bids. The VERSIONNO field is retained for participant use as information only.· The new tables support bids for multiple services. The BIDTYPE field defines the service to which the bid applies.
· There are no default bids. In the absence of a bid for a specific settlement date, the latest bid submitted for a previous settlement date applies.
Source
This data is updated as bids are processed. It includes all bids submitted including corrupt bids.
Volume
Approximately 100,000 records per year
Note
Confirmation is via CSV bid acknowledgement file
Column Schema
OFFERDATE
Time this bid was processed and loaded
SQL Type: TIMESTAMP(3)
Polars Type: pl.Datetime()
This column is mandatory, and a primary key.
Unlike most datetime columns, this one includes a millisecond component.
e.g. 2029/03/15 00:00:00.123.
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.
AUTHORISEDBY
Participant agent who created the Offer
SQL Type: VARCHAR2(20)
Polars Type: str
This column is not mandatory.
AUTHORISEDDATE
When the Offer was processed - synonymous with LastChanged
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
COMMENTS
A participant provided comment
SQL Type: VARCHAR2(1000)
Polars Type: str
This column is not mandatory.
FILENAME
Submitted file name
SQL Type: VARCHAR2(80)
Polars Type: str
This column is mandatory (but not a primary key).
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
REFERENCE_ID
A participant provided reference, which is required to be unique per submission (for a PARTICIPANTID)
SQL Type: VARCHAR2(100)
Polars Type: str
This column is not mandatory.
STATUS
Load status [SUCCESSFUL/CORRUPT]
SQL Type: VARCHAR2(10)
Polars Type: str
This column is not mandatory.
SUBMISSION_METHOD
Method by which this submission was made typically FTP, API, WEB
SQL Type: VARCHAR2(20)
Polars Type: str
This column is not mandatory.
SUBMISSION_TIMESTAMP
The participant provided date/time for the submission
SQL Type: DATE
Polars Type: pl.Datetime()
This column is not mandatory.
Example: 2026/01/20 00:05:00
TRANSACTION_ID
A GUID used to identify the submission transaction in AEMOs systems
SQL Type: VARCHAR2(100)
Polars Type: str
This column is not mandatory.
New Data Timing
As bids are 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"
# for "TIMESTAMP" columns
# Note that Polars uses slightly different format strings for fractional seconds to `dt.strptime`
# e.g. "2029/03/15 00:00:00.123".
dt_ms_fmt = "%Y/%m/%d %H:%M:%S%.3f"
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", "SUBMISSION_TIMESTAMP").str.strptime(
pl.Datetime, dt_s_fmt
),
pl.col("OFFERDATE").str.strptime(pl.Datetime, dt_ms_fmt),
)
.cast(
{
"PARTICIPANTID": pl.Categorical,
"AUTHORISEDBY": str,
"COMMENTS": str,
"FILENAME": str,
"REFERENCE_ID": str,
"STATUS": str,
"SUBMISSION_METHOD": str,
"TRANSACTION_ID": str,
}
)
# Deduplicate
# We want one row per ['OFFERDATE', 'PARTICIPANTID']
# Tie-breaker: greatest value of ['LASTCHANGED']
.sort(["LASTCHANGED"], descending=False)
.unique(subset=["OFFERDATE", "PARTICIPANTID"], 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:
Tip
Bid deduplication is particularly tricky. See the guide.
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 |
|---|---|---|
| BIDS | BIDOFFERFILETRK | 1 |
| OFFER | BIDOFFERFILETRK | 1 |
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