FPP_CONTRIBUTION_FACTOR

This report delivers the calculated contribution factor value for each 5 minute trading interval for each constraint and FPP unit

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.

Examples:

F_T+LREG_0050
F_T+RREG_0050
F_TASCAP_LREG_0210
F_TASCAP_RREG_0220

FPP_UNITID

FPP Unit ID (registered DUID/ TNI)

SQL Type: VARCHAR2(20) Polars Type: str

This column is mandatory, and a primary key.

Examples:

ADPBA1
ADPPV1
AGLHAL
AGLSOM
ALDGASF1
ANGAST1
APD_LOAD
ARWF1
AVLSF1
BALB1

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: 2026/09/01 00:05:00

VERSIONNO

Version (FPP run number from the FPP database)

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

This column is mandatory, and a primary key.

Example: 1

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.

CF_ABS_NEGATIVE_PERF_TOTAL

The sum of absolute negative performance in MWHz for the combination of constraint / bid type (raise or lower). This is used as the denominator in normalising contribution factors (CF) where a units performance is negative. For further details please see the FPP procedure document. >0 = Performance was calculated for the units NULL = Performance for the units was unavailable

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

This column is not mandatory.

Examples: 2.76377, 0, 597.15514

CF_ABS_POSITIVE_PERF_TOTAL

The sum of absolute positive performance in MWHz for the combination of constraint / bid type (raise or lower). This is used as the denominator in normalising contribution factors (CF) where a units performance is positive. For further details please see the FPP procedure document. >0 = Performance was calculated for the units NULL = Performance for the units was unavailable

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

This column is not mandatory.

Examples: 1.41659, 0, 590.56978

CF_REASON_FLAG

The reason flag showing the decision matrix for the contribution factor (CF) Supported values are: 0 = CF is calculated based on good input data 1 = CF is 0 because it is not primary in the group 2 = CF is not for the DUID but for the whole group 4 = CF is calculated based on substitute performance 8 = CF is 0 because FM is unreliable. 16 = CF is 0 because more than 50 percent input is bad or not available.

SQL Type: NUMBER(5) Polars Type: pl.Int32

This column is not mandatory.

Examples: 0, 1, 32

CONTRIBUTION_FACTOR

Contribution Factor (the calculated contribution factor for the FPP unit and constraint ID for that trading interval) - for further details please see the FPP procedure document

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

This column is not mandatory.

Examples: -0.00000677, 0.00000102, 0, 0.62437967

DEFAULT_CONTRIBUTION_FACTOR

The Default Contribution Factor (the calculated default contribution factor based on historical performance for the FPP unit and constraint ID for that trading interval) that is effective for this trading interval, which joins back to FPP_FORECAST_DEFAULT_CF - for further details please see the FPP procedure document

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

This column is not mandatory.

Examples: -0.00000003, 0, -0.12903896

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: 2026/09/01 07:00:00

NCF_ABS_NEGATIVE_PERF_TOTAL

The sum of absolute negative performance in MWHz for the combination of constraint / bid type (raise or lower). This is used as the denominator in normalising negative contribution factors (NCF). For further details please see the FPP procedure document. >0 = Performance was calculated for the units NULL = Performance for the units was unavailable 0 = When NCF is zero (i.e. CF is positive), then this total will be represented as zero

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

This column is not mandatory.

Examples: 2.76377, 0, 597.16219

NEGATIVE_CONTRIBUTION_FACTOR

Negative Contribution Factor (the calculated negative contribution factor for the FPP unit and constraint ID for that trading interval) - for further details please see the FPP procedure document

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

This column is not mandatory.

Examples: -0.00000677, 0, -0.44490562

PARTICIPANTID

Participant ID

For more information, see the dedicated documentation page.

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

This column is not mandatory.

Examples:

AGLLBESS
AGLWBESS
ALCOAPA
ALDOGASF
ALNTARES
ALNTBHWF
ARARATWF
B2PS
BALBESS
BANGOWF2

SETTLEMENTS_UNITID

The Settlements Unit ID (registered DUID / TNI) Note that this SETTLEMENTS_UNITID is what is sent and used by the Settlements system, and may be different from the FPP_UNITID for non-scheduled loads where the FPP_UNITID may be a descriptive key, whereas what will be sent to Settlements as the SETTLEMENTS_UNITID will be the TNI code.

SQL Type: VARCHAR2(50) Polars Type: str

This column is not mandatory.

Examples:

ADPBA1
ADPPV1
AGLHAL
AGLSOM
ALDGASF1
ANGAST1
ARWF1
AVLSF1
BALB1
BALDHWF1

New Data Timing

The private report will be delivered at the completion of an FPP calculation run for a closed trading interval (i.e. every 5 minutes)The public report is delivered the next market day (at 4 am)

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",
    )
    .rename(
        {
            # These columns have names that conflict with metadata columns
            "CONTRIBUTION_FACTOR_duplicated_0": "CONTRIBUTION_FACTOR"
        }
    )
    .with_columns(
        pl.col("INTERVAL_DATETIME", "LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast(
        {
            "CONSTRAINTID": pl.Categorical,
            "FPP_UNITID": str,
            "VERSIONNO": pl.UInt32,
            "BIDTYPE": pl.Enum(
                [
                    "ENERGY",
                    "LOWERREG",
                    "LOWER60SEC",
                    "RAISE5MIN",
                    "LOWER5MIN",
                    "LOWER6SEC",
                    "RAISE60SEC",
                    "RAISE6SEC",
                    "RAISEREG",
                    "RAISE1SEC",
                    "LOWER1SEC",
                ]
            ),
            "CF_ABS_NEGATIVE_PERF_TOTAL": pl.Float64,
            "CF_ABS_POSITIVE_PERF_TOTAL": pl.Float64,
            "CF_REASON_FLAG": pl.Int32,
            "CONTRIBUTION_FACTOR": pl.Float64,
            "DEFAULT_CONTRIBUTION_FACTOR": pl.Float64,
            "NCF_ABS_NEGATIVE_PERF_TOTAL": pl.Float64,
            "NEGATIVE_CONTRIBUTION_FACTOR": pl.Float64,
            "PARTICIPANTID": pl.Categorical,
            "SETTLEMENTS_UNITID": str,
        }
    )
    # Deduplicate
    # These files are so large that this step may cause your laptop to run out of memory.
    # We want one row per ['CONSTRAINTID', 'FPP_UNITID', 'INTERVAL_DATETIME', 'VERSIONNO']
    # Tie-breaker: greatest value of LASTCHANGED
    .sort(LASTCHANGED, descending=False)
    .unique(
        subset=["CONSTRAINTID", "FPP_UNITID", "INTERVAL_DATETIME", "VERSIONNO"],
        keep="last",
    )
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_FPP_CONTRIBUTION_FACTOR_FILE01_202609010000.zip

Table without MetadataTable with MetadataRaw Text without Metadata

INTERVAL_DATETIME	CONSTRAINTID	FPP_UNITID	VERSIONNO	BIDTYPE	CONTRIBUTION_FACTOR	NEGATIVE_CONTRIBUTION_FACTOR	DEFAULT_CONTRIBUTION_FACTOR	CF_REASON_FLAG	CF_ABS_POSITIVE_PERF_TOTAL	CF_ABS_NEGATIVE_PERF_TOTAL	NCF_ABS_NEGATIVE_PERF_TOTAL	PARTICIPANTID	SETTLEMENTS_UNITID	LASTCHANGED
2026/09/01 00:05:00	F_T+LREG_0050	BASTYAN	1	LOWERREG	0	0	-0.00032556	8	0	0	0	HYDROTAS	BASTYAN	2026/09/01 07:00:00
2026/09/01 00:05:00	F_T+LREG_0050	BBTHREE1	1	LOWERREG	0	0	0	8	0	0	0	HYDROTAS	BBTHREE1	2026/09/01 07:00:00
2026/09/01 00:05:00	F_T+LREG_0050	BBTHREE2	1	LOWERREG	0	0	-0.00134223	8	0	0	0	HYDROTAS	BBTHREE2	2026/09/01 07:00:00
2026/09/01 00:05:00	F_T+LREG_0050	BBTHREE3	1	LOWERREG	0	0	0	8	0	0	0	HYDROTAS	BBTHREE3	2026/09/01 07:00:00
2026/09/01 00:05:00	F_T+LREG_0050	BUTLERSG	1	LOWERREG	0	0	-0.00014832	8	0	0	0	HYDROTAS	BUTLERSG	2026/09/01 07:00:00
2026/09/01 00:05:00	F_T+LREG_0050	CETHANA	1	LOWERREG	0	0	-0.0071641	8	0	0	0	HYDROTAS	CETHANA	2026/09/01 07:00:00
2026/09/01 00:05:00	F_T+LREG_0050	CLUNY	1	LOWERREG	0	0	-0.00403346	8	0	0	0	HYDROTAS	CLUNY	2026/09/01 07:00:00
2026/09/01 00:05:00	F_T+LREG_0050	CTHLWF1	1	LOWERREG	0	0	-0.11823754	8	0	0	0	WILDCATL	CTHLWF1	2026/09/01 07:00:00
2026/09/01 00:05:00	F_T+LREG_0050	DEVILS_G	1	LOWERREG	0	0	-0.00093051	8	0	0	0	HYDROTAS	DEVILS_G	2026/09/01 07:00:00
2026/09/01 00:05:00	F_T+LREG_0050	FISHER	1	LOWERREG	0	0	-0.00051141	8	0	0	0	HYDROTAS	FISHER	2026/09/01 07:00:00
2026/09/01 00:05:00	F_T+LREG_0050	GORDON	1	LOWERREG	0	0	-0.02208434	8	0	0	0	HYDROTAS	GORDON	2026/09/01 07:00:00
...

C	SETP.WORLD	DVD_FPP_CONTRIBUTION_FACTOR	AEMO	PUBLIC	2026/10/09	17:41:53	001791531713942	MONTHLY_ARCHIVE	001791531713942
I	FPP	CONTRIBUTION_FACTOR	1	INTERVAL_DATETIME	CONSTRAINTID	FPP_UNITID	VERSIONNO	BIDTYPE	CONTRIBUTION_FACTOR	NEGATIVE_CONTRIBUTION_FACTOR	DEFAULT_CONTRIBUTION_FACTOR	CF_REASON_FLAG	CF_ABS_POSITIVE_PERF_TOTAL	CF_ABS_NEGATIVE_PERF_TOTAL	NCF_ABS_NEGATIVE_PERF_TOTAL	PARTICIPANTID	SETTLEMENTS_UNITID	LASTCHANGED
D	FPP	CONTRIBUTION_FACTOR	1	2026/09/01 00:05:00	F_T+LREG_0050	BASTYAN	1	LOWERREG	0	0	-0.00032556	8	0	0	0	HYDROTAS	BASTYAN	2026/09/01 07:00:00
D	FPP	CONTRIBUTION_FACTOR	1	2026/09/01 00:05:00	F_T+LREG_0050	BBTHREE1	1	LOWERREG	0	0	0	8	0	0	0	HYDROTAS	BBTHREE1	2026/09/01 07:00:00
D	FPP	CONTRIBUTION_FACTOR	1	2026/09/01 00:05:00	F_T+LREG_0050	BBTHREE2	1	LOWERREG	0	0	-0.00134223	8	0	0	0	HYDROTAS	BBTHREE2	2026/09/01 07:00:00
D	FPP	CONTRIBUTION_FACTOR	1	2026/09/01 00:05:00	F_T+LREG_0050	BBTHREE3	1	LOWERREG	0	0	0	8	0	0	0	HYDROTAS	BBTHREE3	2026/09/01 07:00:00
D	FPP	CONTRIBUTION_FACTOR	1	2026/09/01 00:05:00	F_T+LREG_0050	BUTLERSG	1	LOWERREG	0	0	-0.00014832	8	0	0	0	HYDROTAS	BUTLERSG	2026/09/01 07:00:00
D	FPP	CONTRIBUTION_FACTOR	1	2026/09/01 00:05:00	F_T+LREG_0050	CETHANA	1	LOWERREG	0	0	-0.0071641	8	0	0	0	HYDROTAS	CETHANA	2026/09/01 07:00:00
D	FPP	CONTRIBUTION_FACTOR	1	2026/09/01 00:05:00	F_T+LREG_0050	CLUNY	1	LOWERREG	0	0	-0.00403346	8	0	0	0	HYDROTAS	CLUNY	2026/09/01 07:00:00
D	FPP	CONTRIBUTION_FACTOR	1	2026/09/01 00:05:00	F_T+LREG_0050	CTHLWF1	1	LOWERREG	0	0	-0.11823754	8	0	0	0	WILDCATL	CTHLWF1	2026/09/01 07:00:00
D	FPP	CONTRIBUTION_FACTOR	1	2026/09/01 00:05:00	F_T+LREG_0050	DEVILS_G	1	LOWERREG	0	0	-0.00093051	8	0	0	0	HYDROTAS	DEVILS_G	2026/09/01 07:00:00
D	FPP	CONTRIBUTION_FACTOR	1	2026/09/01 00:05:00	F_T+LREG_0050	FISHER	1	LOWERREG	0	0	-0.00051141	8	0	0	0	HYDROTAS	FISHER	2026/09/01 07:00:00
D	FPP	CONTRIBUTION_FACTOR	1	2026/09/01 00:05:00	F_T+LREG_0050	GORDON	1	LOWERREG	0	0	-0.02208434	8	0	0	0	HYDROTAS	GORDON	2026/09/01 07:00:00
...
C	END OF REPORT	1003

INTERVAL_DATETIME,CONSTRAINTID,FPP_UNITID,VERSIONNO,BIDTYPE,CONTRIBUTION_FACTOR,NEGATIVE_CONTRIBUTION_FACTOR,DEFAULT_CONTRIBUTION_FACTOR,CF_REASON_FLAG,CF_ABS_POSITIVE_PERF_TOTAL,CF_ABS_NEGATIVE_PERF_TOTAL,NCF_ABS_NEGATIVE_PERF_TOTAL,PARTICIPANTID,SETTLEMENTS_UNITID,LASTCHANGED
"2026/09/01 00:05:00",F_T+LREG_0050,BASTYAN,1,LOWERREG,0,0,-0.00032556,8,0,0,0,HYDROTAS,BASTYAN,"2026/09/01 07:00:00"
"2026/09/01 00:05:00",F_T+LREG_0050,BBTHREE1,1,LOWERREG,0,0,0,8,0,0,0,HYDROTAS,BBTHREE1,"2026/09/01 07:00:00"
"2026/09/01 00:05:00",F_T+LREG_0050,BBTHREE2,1,LOWERREG,0,0,-0.00134223,8,0,0,0,HYDROTAS,BBTHREE2,"2026/09/01 07:00:00"
"2026/09/01 00:05:00",F_T+LREG_0050,BBTHREE3,1,LOWERREG,0,0,0,8,0,0,0,HYDROTAS,BBTHREE3,"2026/09/01 07:00:00"
"2026/09/01 00:05:00",F_T+LREG_0050,BUTLERSG,1,LOWERREG,0,0,-0.00014832,8,0,0,0,HYDROTAS,BUTLERSG,"2026/09/01 07:00:00"
"2026/09/01 00:05:00",F_T+LREG_0050,CETHANA,1,LOWERREG,0,0,-0.0071641,8,0,0,0,HYDROTAS,CETHANA,"2026/09/01 07:00:00"
"2026/09/01 00:05:00",F_T+LREG_0050,CLUNY,1,LOWERREG,0,0,-0.00403346,8,0,0,0,HYDROTAS,CLUNY,"2026/09/01 07:00:00"
"2026/09/01 00:05:00",F_T+LREG_0050,CTHLWF1,1,LOWERREG,0,0,-0.11823754,8,0,0,0,WILDCATL,CTHLWF1,"2026/09/01 07:00:00"
"2026/09/01 00:05:00",F_T+LREG_0050,DEVILS_G,1,LOWERREG,0,0,-0.00093051,8,0,0,0,HYDROTAS,DEVILS_G,"2026/09/01 07:00:00"
"2026/09/01 00:05:00",F_T+LREG_0050,FISHER,1,LOWERREG,0,0,-0.00051141,8,0,0,0,HYDROTAS,FISHER,"2026/09/01 07:00:00"
"2026/09/01 00:05:00",F_T+LREG_0050,GORDON,1,LOWERREG,0,0,-0.02208434,8,0,0,0,HYDROTAS,GORDON,"2026/09/01 07:00:00"
...

Raw Text with Metadata

C,SETP.WORLD,DVD_FPP_CONTRIBUTION_FACTOR,AEMO,PUBLIC,2026/10/09,17:41:53,001791531713942,MONTHLY_ARCHIVE,001791531713942
I,FPP,CONTRIBUTION_FACTOR,1,INTERVAL_DATETIME,CONSTRAINTID,FPP_UNITID,VERSIONNO,BIDTYPE,CONTRIBUTION_FACTOR,NEGATIVE_CONTRIBUTION_FACTOR,DEFAULT_CONTRIBUTION_FACTOR,CF_REASON_FLAG,CF_ABS_POSITIVE_PERF_TOTAL,CF_ABS_NEGATIVE_PERF_TOTAL,NCF_ABS_NEGATIVE_PERF_TOTAL,PARTICIPANTID,SETTLEMENTS_UNITID,LASTCHANGED
D,FPP,CONTRIBUTION_FACTOR,1,"2026/09/01 00:05:00",F_T+LREG_0050,BASTYAN,1,LOWERREG,0,0,-0.00032556,8,0,0,0,HYDROTAS,BASTYAN,"2026/09/01 07:00:00"
D,FPP,CONTRIBUTION_FACTOR,1,"2026/09/01 00:05:00",F_T+LREG_0050,BBTHREE1,1,LOWERREG,0,0,0,8,0,0,0,HYDROTAS,BBTHREE1,"2026/09/01 07:00:00"
D,FPP,CONTRIBUTION_FACTOR,1,"2026/09/01 00:05:00",F_T+LREG_0050,BBTHREE2,1,LOWERREG,0,0,-0.00134223,8,0,0,0,HYDROTAS,BBTHREE2,"2026/09/01 07:00:00"
D,FPP,CONTRIBUTION_FACTOR,1,"2026/09/01 00:05:00",F_T+LREG_0050,BBTHREE3,1,LOWERREG,0,0,0,8,0,0,0,HYDROTAS,BBTHREE3,"2026/09/01 07:00:00"
D,FPP,CONTRIBUTION_FACTOR,1,"2026/09/01 00:05:00",F_T+LREG_0050,BUTLERSG,1,LOWERREG,0,0,-0.00014832,8,0,0,0,HYDROTAS,BUTLERSG,"2026/09/01 07:00:00"
D,FPP,CONTRIBUTION_FACTOR,1,"2026/09/01 00:05:00",F_T+LREG_0050,CETHANA,1,LOWERREG,0,0,-0.0071641,8,0,0,0,HYDROTAS,CETHANA,"2026/09/01 07:00:00"
D,FPP,CONTRIBUTION_FACTOR,1,"2026/09/01 00:05:00",F_T+LREG_0050,CLUNY,1,LOWERREG,0,0,-0.00403346,8,0,0,0,HYDROTAS,CLUNY,"2026/09/01 07:00:00"
D,FPP,CONTRIBUTION_FACTOR,1,"2026/09/01 00:05:00",F_T+LREG_0050,CTHLWF1,1,LOWERREG,0,0,-0.11823754,8,0,0,0,WILDCATL,CTHLWF1,"2026/09/01 07:00:00"
D,FPP,CONTRIBUTION_FACTOR,1,"2026/09/01 00:05:00",F_T+LREG_0050,DEVILS_G,1,LOWERREG,0,0,-0.00093051,8,0,0,0,HYDROTAS,DEVILS_G,"2026/09/01 07:00:00"
D,FPP,CONTRIBUTION_FACTOR,1,"2026/09/01 00:05:00",F_T+LREG_0050,FISHER,1,LOWERREG,0,0,-0.00051141,8,0,0,0,HYDROTAS,FISHER,"2026/09/01 07:00:00"
D,FPP,CONTRIBUTION_FACTOR,1,"2026/09/01 00:05:00",F_T+LREG_0050,GORDON,1,LOWERREG,0,0,-0.02208434,8,0,0,0,HYDROTAS,GORDON,"2026/09/01 07:00:00"
...
C,"END OF REPORT",1003

Availability

Visibility: Private & Public Next-Day

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.

Warning

The REPORT_SUB_TYPE metadata column has the same column name as an actual columns CONTRIBUTION_FACTOR. So the raw CSVs have duplicate column names. You will need to be careful when reading this CSV.

Data Size

Each year of data is approximately 2 GB when zipped, 18 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:

CONSTRAINTID
FPP_UNITID
INTERVAL_DATETIME
VERSIONNO

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
FPP	CONTRIBUTION_FACTOR	1

This means that the CSV files start with metadata columns:

I,FPP,CONTRIBUTION_FACTOR,1,...
D,FPP,CONTRIBUTION_FACTOR,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: FPP