PD_FCAS_REQ_CONSTRAINT

The constraint level FCAS cost / price details for constraint FCAS processor runs. This enhanced output table format is established for the constraint FCAS processor release required for the Frequency Performance Payments (FPP) release. This enhanced output is hierarchical in nature, with the parent *_FCAS_REQ_RUN table holding the details about the triggering case run and time, and the child *_FCAS_REQ_CONSTRAINT table holding the constraint level details of FCAS costs / prices.

Column Schema

BIDTYPE

DUID offered type

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 mandatory, and a primary key.

CONSTRAINTID

ConstraintID join to table GenConData

SQL Type: VARCHAR2(20) Polars Type: pl.Categorical

This column is mandatory, and a primary key.

Examples:

D_I+BIP_ML2_L1
D_I+NIL_MG2_R1
D_Q+BIP_ML_L1
D_Q+BIP_ML_L6
D_T+NIL_BB_TG_R1
D_T+NIL_MG_R1
D_T+NIL_ML_L1
D_T+NIL_ML_RECL_L1
D_T+NIL_WF_TG_R1
F_I+BIP_ML_L1

INTERVAL_DATETIME

The 30 minute interval date and time of the 30 minute predispatch interval that was processed by the constraint FCAS processor

SQL Type: DATE Polars Type: pl.Datetime()

This column is mandatory, and a primary key.

Example: 2026/09/24 13:00:00

PREDISPATCHSEQNO

Predispatch sequence number for the 30 minute predispatch case that triggers the constraint FCAS processor run

SQL Type: VARCHAR2(20) Polars Type: str

This column is mandatory, and a primary key.

Examples:

2026092417
2026092418

REGIONID

Region identifier

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.

RUN_DATETIME

The run date and time of the 30 minute predispatch case that triggers the constraint FCAS processor run

SQL Type: DATE Polars Type: pl.Datetime()

This column is mandatory, and a primary key.

Example: 2026/09/24 12:30:00

RUNNO

The 30 minute predispatch case run number that has triggers the constraint FCAS processor run

SQL Type: NUMBER(5) Polars Type: pl.UInt32

This column is mandatory, and a primary key.

Example: 1

ADJUSTED_COST

The adjusted cost of the constraint for this service, after the regulation/contingency split

SQL Type: NUMBER(18,8) Polars Type: pl.Float64

This column is not mandatory.

Examples: 0.189, 0, 78, 203.15

BASE_COST

The base cost of the constraint, before the regulation/contingency split

SQL Type: NUMBER(18,8) Polars Type: pl.Float64

This column is not mandatory.

Examples: 0.189, 0, 78, 203.15

CONSTRAINT_ENABLEMENT

MW enabled for this bid type within the constraint

SQL Type: NUMBER(15,5) Polars Type: pl.Float64

This column is not mandatory.

Examples: 22.48, 0, 35, 437

LHS

Constraints summed LHS - aggregate LHS Solution values from the physical run from the PREDISPATCHCONSTRAINT table

SQL Type: NUMBER(15,5) Polars Type: pl.Float64

This column is not mandatory.

Examples: -0.2657, 21.68445, 0, 815

MARGINALVALUE

Shadow price of constraint from the PREDISPATCHCONSTRAINT table from the physical run.

SQL Type: NUMBER(15,5) Polars Type: pl.Float64

This column is not mandatory.

Examples: 0.01, 0, 3.12

P_REGULATION

The adjusted marginal value of the constraint for FPP recovery (blank for constraints without REG terms)

SQL Type: NUMBER(18,8) Polars Type: pl.Float64

This column is not mandatory.

Examples: 0.34, 0, 3.12

REGION_BASE_COST

The regional payment allocated to the constraint for the interval pro-rated based on marginal value ratios over the binding constraints for that service in that region (this is an intermediate calculation to get to the base cost)

SQL Type: NUMBER(18,8) Polars Type: pl.Float64

This column is not mandatory.

Examples: 0.01, 0, 78, 101.575

REGIONAL_ENABLEMENT

The dispatched MW for the bid type inside the region from the physical run of the PREDISPATCHREGIONSUM table

SQL Type: NUMBER(15,5) Polars Type: pl.Float64

This column is not mandatory.

Examples: 0.27, 0, 1, 281

RHS

Constraints RHS value used in the solution - may be either dynamic (calculated) or static from the physical run from the PREDISPATCHCONSTRAINT table

SQL Type: NUMBER(15,5) Polars Type: pl.Float64

This column is not mandatory.

Examples: -0.0001, 8.47886, 0, 1390

RRP

Bid type prices for the region coming from the pricing run of the PREDISPATCHREGIONSUM table

For more information, see the dedicated documentation page.

SQL Type: NUMBER(15,5) Polars Type: pl.Float64

This column is not mandatory.

Examples: 0.01, 0, 1, 3

New Data Timing

Public at the completion of a constraint FCAS processor run for a 30 minute predispatch run

Code Examples

Polars

This table is so large that you should explicitly enumerate and cast every column, which will speed things up and prevents some errors. Although some of the datatypes are inferred, so they may be wrong. In particular, UInt16 vs UInt8 vs Int16 etc. If you get errors, you can swap these for just int.

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").str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast(
        {
            "BIDTYPE": pl.Enum(
                [
                    "ENERGY",
                    "LOWERREG",
                    "LOWER60SEC",
                    "RAISE5MIN",
                    "LOWER5MIN",
                    "LOWER6SEC",
                    "RAISE60SEC",
                    "RAISE6SEC",
                    "RAISEREG",
                    "RAISE1SEC",
                    "LOWER1SEC",
                ]
            ),
            "CONSTRAINTID": pl.Categorical,
            "PREDISPATCHSEQNO": str,
            "REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
            "RUNNO": pl.UInt32,
            "ADJUSTED_COST": pl.Float64,
            "BASE_COST": pl.Float64,
            "CONSTRAINT_ENABLEMENT": pl.Float64,
            "LHS": pl.Float64,
            "MARGINALVALUE": pl.Float64,
            "P_REGULATION": pl.Float64,
            "REGION_BASE_COST": pl.Float64,
            "REGIONAL_ENABLEMENT": pl.Float64,
            "RHS": pl.Float64,
            "RRP": pl.Float64,
        }
    )
    # Deduplicate
    # These files are so large that this step may cause your laptop to run out of memory.
    # We want one row per ['BIDTYPE', 'CONSTRAINTID', 'INTERVAL_DATETIME', 'PREDISPATCHSEQNO', 'REGIONID', 'RUN_DATETIME', 'RUNNO']
    .unique(
        subset=[
            "BIDTYPE",
            "CONSTRAINTID",
            "INTERVAL_DATETIME",
            "PREDISPATCHSEQNO",
            "REGIONID",
            "RUN_DATETIME",
            "RUNNO",
        ],
        keep="any",
    )
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_PD_FCAS_REQ_CONSTRAINT_FILE02_202609010000.zip

Table without MetadataTable with MetadataRaw Text without Metadata

PREDISPATCHSEQNO	RUN_DATETIME	RUNNO	INTERVAL_DATETIME	CONSTRAINTID	REGIONID	BIDTYPE	LHS	RHS	MARGINALVALUE	RRP	REGIONAL_ENABLEMENT	CONSTRAINT_ENABLEMENT	REGION_BASE_COST	BASE_COST	ADJUSTED_COST	P_REGULATION
2026092417	2026/09/24 12:30:00	1	2026/09/25 00:00:00	F_MAIN+MREH_TL_L1	SA1	LOWER1SEC	22.47572	-10000	0	0	0	22.48	0	0	0	0
2026092417	2026/09/24 12:30:00	1	2026/09/25 00:00:00	F_MAIN+MREH_TL_L1	VIC1	LOWER1SEC	22.47572	-10000	0	0	1	22.48	0	0	0	0
2026092417	2026/09/24 12:30:00	1	2026/09/25 00:00:00	F_MAIN+MREH_TL_L5	NSW1	LOWER5MIN	642.07987	-10029.91984	0	0	88	275.08	0	0	0	0
2026092417	2026/09/24 12:30:00	1	2026/09/25 00:00:00	F_MAIN+MREH_TL_L5	NSW1	LOWERREG	642.07987	-10029.91984	0	1	11	160	0	0	0	0
2026092417	2026/09/24 12:30:00	1	2026/09/25 00:00:00	F_MAIN+MREH_TL_L5	QLD1	LOWER5MIN	642.07987	-10029.91984	0	0	36	275.08	0	0	0	0
2026092417	2026/09/24 12:30:00	1	2026/09/25 00:00:00	F_MAIN+MREH_TL_L5	QLD1	LOWERREG	642.07987	-10029.91984	0	1	104	160	0	0	0	0
2026092417	2026/09/24 12:30:00	1	2026/09/25 00:00:00	F_MAIN+MREH_TL_L5	SA1	LOWER5MIN	642.07987	-10029.91984	0	0	97	275.08	0	0	0	0
2026092417	2026/09/24 12:30:00	1	2026/09/25 00:00:00	F_MAIN+MREH_TL_L5	SA1	LOWERREG	642.07987	-10029.91984	0	1	20	160	0	0	0	0
2026092417	2026/09/24 12:30:00	1	2026/09/25 00:00:00	F_MAIN+MREH_TL_L5	VIC1	LOWER5MIN	642.07987	-10029.91984	0	0	54.08	275.08	0	0	0	0
2026092417	2026/09/24 12:30:00	1	2026/09/25 01:00:00	F_I+NIL_MG_R60	SA1	RAISE60SEC	634.92576	634.92576	0.09	0.09	50	634.93	2.25	28.57185	28.57185	0
2026092417	2026/09/24 12:30:00	1	2026/09/25 01:00:00	F_I+NIL_MG_R60	TAS1	RAISE60SEC	634.92576	634.92576	0.09	0.09	0	634.93	0	28.57185	28.57185	0
...

C	SETP.WORLD	DVD_PD_FCAS_REQ_CONSTRAINT	AEMO	PUBLIC	2026/10/09	18:43:52	001791535432585	MONTHLY_ARCHIVE	001791535432585
I	PREDISPATCH	FCAS_REQ_CONSTRAINT	1	PREDISPATCHSEQNO	RUN_DATETIME	RUNNO	INTERVAL_DATETIME	CONSTRAINTID	REGIONID	BIDTYPE	LHS	RHS	MARGINALVALUE	RRP	REGIONAL_ENABLEMENT	CONSTRAINT_ENABLEMENT	REGION_BASE_COST	BASE_COST	ADJUSTED_COST	P_REGULATION
D	PREDISPATCH	FCAS_REQ_CONSTRAINT	1	2026092417	2026/09/24 12:30:00	1	2026/09/25 00:00:00	F_MAIN+MREH_TL_L1	SA1	LOWER1SEC	22.47572	-10000	0	0	0	22.48	0	0	0	0
D	PREDISPATCH	FCAS_REQ_CONSTRAINT	1	2026092417	2026/09/24 12:30:00	1	2026/09/25 00:00:00	F_MAIN+MREH_TL_L1	VIC1	LOWER1SEC	22.47572	-10000	0	0	1	22.48	0	0	0	0
D	PREDISPATCH	FCAS_REQ_CONSTRAINT	1	2026092417	2026/09/24 12:30:00	1	2026/09/25 00:00:00	F_MAIN+MREH_TL_L5	NSW1	LOWER5MIN	642.07987	-10029.91984	0	0	88	275.08	0	0	0	0
D	PREDISPATCH	FCAS_REQ_CONSTRAINT	1	2026092417	2026/09/24 12:30:00	1	2026/09/25 00:00:00	F_MAIN+MREH_TL_L5	NSW1	LOWERREG	642.07987	-10029.91984	0	1	11	160	0	0	0	0
D	PREDISPATCH	FCAS_REQ_CONSTRAINT	1	2026092417	2026/09/24 12:30:00	1	2026/09/25 00:00:00	F_MAIN+MREH_TL_L5	QLD1	LOWER5MIN	642.07987	-10029.91984	0	0	36	275.08	0	0	0	0
D	PREDISPATCH	FCAS_REQ_CONSTRAINT	1	2026092417	2026/09/24 12:30:00	1	2026/09/25 00:00:00	F_MAIN+MREH_TL_L5	QLD1	LOWERREG	642.07987	-10029.91984	0	1	104	160	0	0	0	0
D	PREDISPATCH	FCAS_REQ_CONSTRAINT	1	2026092417	2026/09/24 12:30:00	1	2026/09/25 00:00:00	F_MAIN+MREH_TL_L5	SA1	LOWER5MIN	642.07987	-10029.91984	0	0	97	275.08	0	0	0	0
D	PREDISPATCH	FCAS_REQ_CONSTRAINT	1	2026092417	2026/09/24 12:30:00	1	2026/09/25 00:00:00	F_MAIN+MREH_TL_L5	SA1	LOWERREG	642.07987	-10029.91984	0	1	20	160	0	0	0	0
D	PREDISPATCH	FCAS_REQ_CONSTRAINT	1	2026092417	2026/09/24 12:30:00	1	2026/09/25 00:00:00	F_MAIN+MREH_TL_L5	VIC1	LOWER5MIN	642.07987	-10029.91984	0	0	54.08	275.08	0	0	0	0
D	PREDISPATCH	FCAS_REQ_CONSTRAINT	1	2026092417	2026/09/24 12:30:00	1	2026/09/25 01:00:00	F_I+NIL_MG_R60	SA1	RAISE60SEC	634.92576	634.92576	0.09	0.09	50	634.93	2.25	28.57185	28.57185	0
D	PREDISPATCH	FCAS_REQ_CONSTRAINT	1	2026092417	2026/09/24 12:30:00	1	2026/09/25 01:00:00	F_I+NIL_MG_R60	TAS1	RAISE60SEC	634.92576	634.92576	0.09	0.09	0	634.93	0	28.57185	28.57185	0
...
C	END OF REPORT	1003

PREDISPATCHSEQNO,RUN_DATETIME,RUNNO,INTERVAL_DATETIME,CONSTRAINTID,REGIONID,BIDTYPE,LHS,RHS,MARGINALVALUE,RRP,REGIONAL_ENABLEMENT,CONSTRAINT_ENABLEMENT,REGION_BASE_COST,BASE_COST,ADJUSTED_COST,P_REGULATION
2026092417,"2026/09/24 12:30:00",1,"2026/09/25 00:00:00",F_MAIN+MREH_TL_L1,SA1,LOWER1SEC,22.47572,-10000,0,0,0,22.48,0,0,0,0
2026092417,"2026/09/24 12:30:00",1,"2026/09/25 00:00:00",F_MAIN+MREH_TL_L1,VIC1,LOWER1SEC,22.47572,-10000,0,0,1,22.48,0,0,0,0
2026092417,"2026/09/24 12:30:00",1,"2026/09/25 00:00:00",F_MAIN+MREH_TL_L5,NSW1,LOWER5MIN,642.07987,-10029.91984,0,0,88,275.08,0,0,0,0
2026092417,"2026/09/24 12:30:00",1,"2026/09/25 00:00:00",F_MAIN+MREH_TL_L5,NSW1,LOWERREG,642.07987,-10029.91984,0,1,11,160,0,0,0,0
2026092417,"2026/09/24 12:30:00",1,"2026/09/25 00:00:00",F_MAIN+MREH_TL_L5,QLD1,LOWER5MIN,642.07987,-10029.91984,0,0,36,275.08,0,0,0,0
2026092417,"2026/09/24 12:30:00",1,"2026/09/25 00:00:00",F_MAIN+MREH_TL_L5,QLD1,LOWERREG,642.07987,-10029.91984,0,1,104,160,0,0,0,0
2026092417,"2026/09/24 12:30:00",1,"2026/09/25 00:00:00",F_MAIN+MREH_TL_L5,SA1,LOWER5MIN,642.07987,-10029.91984,0,0,97,275.08,0,0,0,0
2026092417,"2026/09/24 12:30:00",1,"2026/09/25 00:00:00",F_MAIN+MREH_TL_L5,SA1,LOWERREG,642.07987,-10029.91984,0,1,20,160,0,0,0,0
2026092417,"2026/09/24 12:30:00",1,"2026/09/25 00:00:00",F_MAIN+MREH_TL_L5,VIC1,LOWER5MIN,642.07987,-10029.91984,0,0,54.08,275.08,0,0,0,0
2026092417,"2026/09/24 12:30:00",1,"2026/09/25 01:00:00",F_I+NIL_MG_R60,SA1,RAISE60SEC,634.92576,634.92576,0.09,0.09,50,634.93,2.25,28.57185,28.57185,0
2026092417,"2026/09/24 12:30:00",1,"2026/09/25 01:00:00",F_I+NIL_MG_R60,TAS1,RAISE60SEC,634.92576,634.92576,0.09,0.09,0,634.93,0,28.57185,28.57185,0
...

Raw Text with Metadata

C,SETP.WORLD,DVD_PD_FCAS_REQ_CONSTRAINT,AEMO,PUBLIC,2026/10/09,18:43:52,001791535432585,MONTHLY_ARCHIVE,001791535432585
I,PREDISPATCH,FCAS_REQ_CONSTRAINT,1,PREDISPATCHSEQNO,RUN_DATETIME,RUNNO,INTERVAL_DATETIME,CONSTRAINTID,REGIONID,BIDTYPE,LHS,RHS,MARGINALVALUE,RRP,REGIONAL_ENABLEMENT,CONSTRAINT_ENABLEMENT,REGION_BASE_COST,BASE_COST,ADJUSTED_COST,P_REGULATION
D,PREDISPATCH,FCAS_REQ_CONSTRAINT,1,2026092417,"2026/09/24 12:30:00",1,"2026/09/25 00:00:00",F_MAIN+MREH_TL_L1,SA1,LOWER1SEC,22.47572,-10000,0,0,0,22.48,0,0,0,0
D,PREDISPATCH,FCAS_REQ_CONSTRAINT,1,2026092417,"2026/09/24 12:30:00",1,"2026/09/25 00:00:00",F_MAIN+MREH_TL_L1,VIC1,LOWER1SEC,22.47572,-10000,0,0,1,22.48,0,0,0,0
D,PREDISPATCH,FCAS_REQ_CONSTRAINT,1,2026092417,"2026/09/24 12:30:00",1,"2026/09/25 00:00:00",F_MAIN+MREH_TL_L5,NSW1,LOWER5MIN,642.07987,-10029.91984,0,0,88,275.08,0,0,0,0
D,PREDISPATCH,FCAS_REQ_CONSTRAINT,1,2026092417,"2026/09/24 12:30:00",1,"2026/09/25 00:00:00",F_MAIN+MREH_TL_L5,NSW1,LOWERREG,642.07987,-10029.91984,0,1,11,160,0,0,0,0
D,PREDISPATCH,FCAS_REQ_CONSTRAINT,1,2026092417,"2026/09/24 12:30:00",1,"2026/09/25 00:00:00",F_MAIN+MREH_TL_L5,QLD1,LOWER5MIN,642.07987,-10029.91984,0,0,36,275.08,0,0,0,0
D,PREDISPATCH,FCAS_REQ_CONSTRAINT,1,2026092417,"2026/09/24 12:30:00",1,"2026/09/25 00:00:00",F_MAIN+MREH_TL_L5,QLD1,LOWERREG,642.07987,-10029.91984,0,1,104,160,0,0,0,0
D,PREDISPATCH,FCAS_REQ_CONSTRAINT,1,2026092417,"2026/09/24 12:30:00",1,"2026/09/25 00:00:00",F_MAIN+MREH_TL_L5,SA1,LOWER5MIN,642.07987,-10029.91984,0,0,97,275.08,0,0,0,0
D,PREDISPATCH,FCAS_REQ_CONSTRAINT,1,2026092417,"2026/09/24 12:30:00",1,"2026/09/25 00:00:00",F_MAIN+MREH_TL_L5,SA1,LOWERREG,642.07987,-10029.91984,0,1,20,160,0,0,0,0
D,PREDISPATCH,FCAS_REQ_CONSTRAINT,1,2026092417,"2026/09/24 12:30:00",1,"2026/09/25 00:00:00",F_MAIN+MREH_TL_L5,VIC1,LOWER5MIN,642.07987,-10029.91984,0,0,54.08,275.08,0,0,0,0
D,PREDISPATCH,FCAS_REQ_CONSTRAINT,1,2026092417,"2026/09/24 12:30:00",1,"2026/09/25 01:00:00",F_I+NIL_MG_R60,SA1,RAISE60SEC,634.92576,634.92576,0.09,0.09,50,634.93,2.25,28.57185,28.57185,0
D,PREDISPATCH,FCAS_REQ_CONSTRAINT,1,2026092417,"2026/09/24 12:30:00",1,"2026/09/25 01:00:00",F_I+NIL_MG_R60,TAS1,RAISE60SEC,634.92576,634.92576,0.09,0.09,0,634.93,0,28.57185,28.57185,0
...
C,"END OF REPORT",1003

Availability

Visibility: Public

This table is in the public monthly MMSDM archive.

I have collated the list of all those files into one place, in this CSV metadata file. (Columns year, month, url, compressed_size in bytes).

Tip

There are multiple files per month, because this is such a large table.

The earliest available data is month 12 of 2024. The most recent available data is month 9 of 2026.

Every month in between has at least one file.

Data Size

Each year of data is approximately 8 GB when zipped, 76 GB as unzipped CSV.

Tip

This table is large. Simply calling pd.read_csv() might fill up all the memory on your laptop. (As a rule of thumb, you need double the amount of memory as the size of the CSV.) You will need to think hard about memory optimisation. e.g. consider using Polars instead of Pandas, and selecting only the columns you care about.

Deduplication and PDR Loader Configuration

The primary keys are:

BIDTYPE
CONSTRAINTID
INTERVAL_DATETIME
PREDISPATCHSEQNO
REGIONID
RUN_DATETIME
RUNNO

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.

PDR Loader configuration is:

Report Name	Report Subtype	Version
PREDISPATCH	FCAS_REQ_CONSTRAINT	1

This means that the CSV files start with metadata columns:

I,PREDISPATCH,FCAS_REQ_CONSTRAINT,1,...
D,PREDISPATCH,FCAS_REQ_CONSTRAINT,1,...

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: PRE_DISPATCH