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FPP_PERFORMANCE

This report delivers the calculated performance value for each 5 minute trading interval for each FPP unit

Tip

1 second, 6 second, 5 minute, 60 second and regulation refer to types of FCAS markets.

Column Schema

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

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 07:00:22

LOWER_PERFORMANCE

Lower performance value in MWHz units (calculated by FPP for that trading interval taken from FPP database)

SQL Type: NUMBER(18,5) Polars Type: pl.Float64

This column is not mandatory.

Examples: -0.00007, 0.00048, 0, 0.88449

LOWER_REASON_FLAG

The reason flag showing the decision matrix for the lower performance value Supported values are: 0 = Performance is calculated based on good input data 1 = Performance is Null as unit is a Non Primary DUID in the group 2 = Performance against the Primary DUID representing the group 4 = Performance is Null as Input data is bad or unavailable 8 = Performance is Null as FM is unreliable 12 = Performance is Null as Input data is bad or unavailable and FM is unreliable 6 = Performance against the Primary DUID representing the group is Null as Input data is bad or unavailable 10 = Performance against the Primary DUID representing the group is Null as FM is unreliable 14 = Performance against the Primary DUID representing the group is Null as Input data is bad or unavailable and FM is unreliable

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

This column is not mandatory.

Examples: 0, 1, 12

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
  • ALCOAPA
  • ALDOGASF
  • ALNTARES
  • ALNTBHWF
  • ARARATWF
  • B2PS
  • BALBESS
  • BANGOWF2
  • BBPEM

RAISE_PERFORMANCE

Raise performance value in MWHz units (calculated by FPP for that trading interval taken from FPP database)

SQL Type: NUMBER(18,5) Polars Type: pl.Float64

This column is not mandatory.

Examples: -0.00245, 0.00005, 0, 30.18222

RAISE_REASON_FLAG

The reason flag showing the decision matrix for the raise performance value Supported values are: 0 = Performance is calculated based on good input data 1 = Performance is Null as unit is a Non Primary DUID in the group 2 = Performance against the Primary DUID representing the group 4 = Performance is Null as Input data is bad or unavailable 8 = Performance is Null as FM is unreliable 12 = Performance is Null as Input data is bad or unavailable and FM is unreliable 6 = Performance against the Primary DUID representing the group is Null as Input data is bad or unavailable 10 = Performance against the Primary DUID representing the group is Null as FM is unreliable 14 = Performance against the Primary DUID representing the group is Null as Input data is bad or unavailable and FM is unreliable

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

This column is not mandatory.

Examples: 0, 1, 4

New Data Timing

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

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", "LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast(
        {
            "FPP_UNITID": str,
            "VERSIONNO": pl.UInt32,
            "LOWER_PERFORMANCE": pl.Float64,
            "LOWER_REASON_FLAG": pl.Int32,
            "PARTICIPANTID": pl.Categorical,
            "RAISE_PERFORMANCE": pl.Float64,
            "RAISE_REASON_FLAG": pl.Int32,
        }
    )
    # Deduplicate
    # These files are so large that this step may cause your laptop to run out of memory.
    # We want one row per ['FPP_UNITID', 'INTERVAL_DATETIME', 'VERSIONNO']
    # Tie-breaker: greatest value of LASTCHANGED
    .sort(LASTCHANGED, descending=False)
    .unique(subset=["FPP_UNITID", "INTERVAL_DATETIME", "VERSIONNO"], keep="last")
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_FPP_PERFORMANCE_FILE01_202609010000.zip

INTERVAL_DATETIMEFPP_UNITIDVERSIONNORAISE_PERFORMANCERAISE_REASON_FLAGLOWER_PERFORMANCELOWER_REASON_FLAGPARTICIPANTIDLASTCHANGED
2026/09/01 00:05:00ADPBA110.2293308SAWCML2026/09/01 07:00:22
2026/09/01 00:05:00ADPPV11008SAWCML2026/09/01 07:00:22
2026/09/01 00:05:00ER0317.6893808OERARING2026/09/01 07:00:22
2026/09/01 00:05:00AGLHAL1008HALLETT2026/09/01 07:00:22
2026/09/01 00:05:00AGLSOM1008SOUTHHYD2026/09/01 07:00:22
2026/09/01 00:05:00ALDGASF11008ALDOGASF2026/09/01 07:00:22
2026/09/01 00:05:00ANGAST11-0.3454408SNOWY2026/09/01 07:00:22
2026/09/01 00:05:00APD_LOAD1-0.9341108ALCOAPA2026/09/01 07:00:22
2026/09/01 00:05:00ARWF1112.9191708ARARATWF2026/09/01 07:00:22
2026/09/01 00:05:00AVLSF11008BBPEM2026/09/01 07:00:22
2026/09/01 00:05:00BALB11008BALBESS2026/09/01 07:00:22
...

CSETP.WORLDDVD_FPP_PERFORMANCEAEMOPUBLIC2026/10/0917:38:36001791531516767MONTHLY_ARCHIVE001791531516767
IFPPFPP_PERFORMANCE1INTERVAL_DATETIMEFPP_UNITIDVERSIONNORAISE_PERFORMANCERAISE_REASON_FLAGLOWER_PERFORMANCELOWER_REASON_FLAGPARTICIPANTIDLASTCHANGED
DFPPFPP_PERFORMANCE12026/09/01 00:05:00ADPBA110.2293308SAWCML2026/09/01 07:00:22
DFPPFPP_PERFORMANCE12026/09/01 00:05:00ADPPV11008SAWCML2026/09/01 07:00:22
DFPPFPP_PERFORMANCE12026/09/01 00:05:00ER0317.6893808OERARING2026/09/01 07:00:22
DFPPFPP_PERFORMANCE12026/09/01 00:05:00AGLHAL1008HALLETT2026/09/01 07:00:22
DFPPFPP_PERFORMANCE12026/09/01 00:05:00AGLSOM1008SOUTHHYD2026/09/01 07:00:22
DFPPFPP_PERFORMANCE12026/09/01 00:05:00ALDGASF11008ALDOGASF2026/09/01 07:00:22
DFPPFPP_PERFORMANCE12026/09/01 00:05:00ANGAST11-0.3454408SNOWY2026/09/01 07:00:22
DFPPFPP_PERFORMANCE12026/09/01 00:05:00APD_LOAD1-0.9341108ALCOAPA2026/09/01 07:00:22
DFPPFPP_PERFORMANCE12026/09/01 00:05:00ARWF1112.9191708ARARATWF2026/09/01 07:00:22
DFPPFPP_PERFORMANCE12026/09/01 00:05:00AVLSF11008BBPEM2026/09/01 07:00:22
DFPPFPP_PERFORMANCE12026/09/01 00:05:00BALB11008BALBESS2026/09/01 07:00:22
...
CEND OF REPORT1003

INTERVAL_DATETIME,FPP_UNITID,VERSIONNO,RAISE_PERFORMANCE,RAISE_REASON_FLAG,LOWER_PERFORMANCE,LOWER_REASON_FLAG,PARTICIPANTID,LASTCHANGED
"2026/09/01 00:05:00",ADPBA1,1,0.22933,0,,8,SAWCML,"2026/09/01 07:00:22"
"2026/09/01 00:05:00",ADPPV1,1,0,0,,8,SAWCML,"2026/09/01 07:00:22"
"2026/09/01 00:05:00",ER03,1,7.68938,0,,8,OERARING,"2026/09/01 07:00:22"
"2026/09/01 00:05:00",AGLHAL,1,0,0,,8,HALLETT,"2026/09/01 07:00:22"
"2026/09/01 00:05:00",AGLSOM,1,0,0,,8,SOUTHHYD,"2026/09/01 07:00:22"
"2026/09/01 00:05:00",ALDGASF1,1,0,0,,8,ALDOGASF,"2026/09/01 07:00:22"
"2026/09/01 00:05:00",ANGAST1,1,-0.34544,0,,8,SNOWY,"2026/09/01 07:00:22"
"2026/09/01 00:05:00",APD_LOAD,1,-0.93411,0,,8,ALCOAPA,"2026/09/01 07:00:22"
"2026/09/01 00:05:00",ARWF1,1,12.91917,0,,8,ARARATWF,"2026/09/01 07:00:22"
"2026/09/01 00:05:00",AVLSF1,1,0,0,,8,BBPEM,"2026/09/01 07:00:22"
"2026/09/01 00:05:00",BALB1,1,0,0,,8,BALBESS,"2026/09/01 07:00:22"
...

C,SETP.WORLD,DVD_FPP_PERFORMANCE,AEMO,PUBLIC,2026/10/09,17:38:36,001791531516767,MONTHLY_ARCHIVE,001791531516767
I,FPP,FPP_PERFORMANCE,1,INTERVAL_DATETIME,FPP_UNITID,VERSIONNO,RAISE_PERFORMANCE,RAISE_REASON_FLAG,LOWER_PERFORMANCE,LOWER_REASON_FLAG,PARTICIPANTID,LASTCHANGED
D,FPP,FPP_PERFORMANCE,1,"2026/09/01 00:05:00",ADPBA1,1,0.22933,0,,8,SAWCML,"2026/09/01 07:00:22"
D,FPP,FPP_PERFORMANCE,1,"2026/09/01 00:05:00",ADPPV1,1,0,0,,8,SAWCML,"2026/09/01 07:00:22"
D,FPP,FPP_PERFORMANCE,1,"2026/09/01 00:05:00",ER03,1,7.68938,0,,8,OERARING,"2026/09/01 07:00:22"
D,FPP,FPP_PERFORMANCE,1,"2026/09/01 00:05:00",AGLHAL,1,0,0,,8,HALLETT,"2026/09/01 07:00:22"
D,FPP,FPP_PERFORMANCE,1,"2026/09/01 00:05:00",AGLSOM,1,0,0,,8,SOUTHHYD,"2026/09/01 07:00:22"
D,FPP,FPP_PERFORMANCE,1,"2026/09/01 00:05:00",ALDGASF1,1,0,0,,8,ALDOGASF,"2026/09/01 07:00:22"
D,FPP,FPP_PERFORMANCE,1,"2026/09/01 00:05:00",ANGAST1,1,-0.34544,0,,8,SNOWY,"2026/09/01 07:00:22"
D,FPP,FPP_PERFORMANCE,1,"2026/09/01 00:05:00",APD_LOAD,1,-0.93411,0,,8,ALCOAPA,"2026/09/01 07:00:22"
D,FPP,FPP_PERFORMANCE,1,"2026/09/01 00:05:00",ARWF1,1,12.91917,0,,8,ARARATWF,"2026/09/01 07:00:22"
D,FPP,FPP_PERFORMANCE,1,"2026/09/01 00:05:00",AVLSF1,1,0,0,,8,BBPEM,"2026/09/01 07:00:22"
D,FPP,FPP_PERFORMANCE,1,"2026/09/01 00:05:00",BALB1,1,0,0,,8,BALBESS,"2026/09/01 07:00:22"
...
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).

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

Every month in between has a file. (No gaps.)

Data Size

Each year of data is approximately 577 MB when zipped, 6 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:

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 FPP_PERFORMANCE 1

This means that the CSV files start with metadata columns:

I,FPP,FPP_PERFORMANCE,1,...
D,FPP,FPP_PERFORMANCE,1,...

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