FPP_P5_FWD_EST_COST
This report delivers the forward estimated unit cost based on P5min runs. These high-level estimates (i.e. assuming that all is unused FCAS) will be provided for each constraint for each 5 minute pre-dispatch interval.
Column Schema
CONSTRAINTID
Constraint ID (binding constraint ID from FCAS data used in FPP calculations)
SQL Type: VARCHAR2(20)
Polars Type: pl.Categorical
This column is mandatory, and a primary key.
FPP_UNITID
FPP Unit ID (registered DUID / TNI)
SQL Type: VARCHAR2(20)
Polars Type: str
This column is mandatory, and a primary key.
INTERVAL_DATETIME
Date and time of the trading interval (DD/MM/YYYY HH24:MI:SS) fixed to the UTC+10 time zone (NEM time)
SQL Type: DATE
Polars Type: pl.Datetime()
This column is mandatory, and a primary key.
Example: 2026/01/20 00:05:00
RUN_DATETIME
The run date and time of the 5 minute predispatch case that was the trigger for the constraint FCAS processor execution (as the FCAS requirement data is the basis of these forward estimates)
SQL Type: DATE
Polars Type: pl.Datetime()
This column is mandatory, and a primary key.
Example: 2026/01/20 00:05:00
RUNNO
The 5 minute predispatch case run number that was the trigger for the constraint FCAS processor execution (as the FCAS requirement data is the basis for these forward estimates)
SQL Type: NUMBER(5)
Polars Type: pl.UInt32
This column is mandatory, and a primary key.
VERSIONNO
The version number of the effective default contribution factor for the unit / constraint combination taken from the FPP_FORECAST_DEFAULT_CF table
SQL Type: NUMBER(5)
Polars Type: pl.UInt32
This column is mandatory, and a primary key.
BIDTYPE
Bid type (the bid type saved in relation to constraint ID from FCAS data used in FPP calculations)
The SQL type is VARCHAR2(10).
This is actually a factor/enum, with values:
-
ENERGY -
LOWERREG -
LOWER60SEC -
RAISE5MIN -
LOWER5MIN -
LOWER6SEC -
RAISE60SEC -
RAISE6SEC -
RAISEREG -
RAISE1SEC -
LOWER1SEC
This column is not mandatory.
EST_UNUSED_FCAS
Estimated unused recovery FCAS in AUD (the forward financial estimate of the recovery of unused FCAS, assuming that all is unused FCAS). This value will be either 0 (nil), or a negative value (debit) only. For details on the calculation, please see FPP procedure and supporting documentation. Please note that there may be cases where there is not yet a default contribution factor for the constraint (for example if there is a new constraint binding in p5min that has not yet bound in dispatch). In these scenarios there will be no EST_UNUSED_FCAS row sent for the constraint, as it is currently incalculable until a corresponding default contribution factor is calculated from a dispatch run with this constraint binding.
SQL Type: NUMBER(18,8)
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
PARTICIPANTID
Participant ID
For more information, see the dedicated documentation page.
SQL Type: VARCHAR2(20)
Polars Type: pl.Categorical
This column is not mandatory.
RELEVANT_REGIONS
Relevant regions (a comma separated list of the relevant regions for the constraint from FCAS data)
SQL Type: VARCHAR2(200)
Polars Type: str
This column is not mandatory.
New Data Timing
At the completion of a P5MIN estimation calculation run (i.e. every 5 mins)
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", "RUN_DATETIME", "LASTCHANGED").str.strptime(
pl.Datetime, dt_s_fmt
),
)
.cast(
{
"CONSTRAINTID": pl.Categorical,
"FPP_UNITID": str,
"RUNNO": int, # or pl.UInt32
"VERSIONNO": int, # or pl.UInt32
"BIDTYPE": pl.Enum(
[
"ENERGY",
"LOWERREG",
"LOWER60SEC",
"RAISE5MIN",
"LOWER5MIN",
"LOWER6SEC",
"RAISE60SEC",
"RAISE6SEC",
"RAISEREG",
"RAISE1SEC",
"LOWER1SEC",
]
),
"EST_UNUSED_FCAS": pl.Float64,
"PARTICIPANTID": pl.Categorical,
"RELEVANT_REGIONS": str,
}
)
# Deduplicate
# We want one row per ['CONSTRAINTID', 'FPP_UNITID', 'INTERVAL_DATETIME', 'RUN_DATETIME', 'RUNNO', 'VERSIONNO']
# Tie-breaker: greatest value of LASTCHANGED
.sort(LASTCHANGED, descending=False)
.unique(
subset=[
"CONSTRAINTID",
"FPP_UNITID",
"INTERVAL_DATETIME",
"RUN_DATETIME",
"RUNNO",
"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 |
|---|---|---|
| FPP | P5_FWD_EST_COST | 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: FPP