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FPP_RESIDUAL_PERFORMANCE

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

Tip

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

Column Schema

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

REGIONID

Region ID of the residual performance

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.

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 00:10:20

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: -0.00075, 0.00659, -648.26534, 128.80915

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 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

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

This column is not mandatory.

Examples: 0, 8

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: -0.01546, 0.00285, -1021.75449, 299.7415

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 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

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

This column is not mandatory.

Examples: 0, 8

New Data Timing

The public report delivers at the completion of an FPP calculation run for a closed trading interval (i.e. every 5 minutes)

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", "LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast(
        {
            "REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
            "VERSIONNO": int,  # or pl.UInt32
            "LOWER_PERFORMANCE": pl.Float64,
            "LOWER_REASON_FLAG": int,  # or pl.Int32
            "RAISE_PERFORMANCE": pl.Float64,
            "RAISE_REASON_FLAG": int,  # or pl.Int32
        }
    )
    # Deduplicate
    # We want one row per ['INTERVAL_DATETIME', 'REGIONID', 'VERSIONNO']
    # Tie-breaker: greatest value of LASTCHANGED
    .sort(LASTCHANGED, descending=False)
    .unique(subset=["INTERVAL_DATETIME", "REGIONID", "VERSIONNO"], keep="last")
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_FPP_RESIDUAL_PERFORMANCE_FILE01_202609010000.zip

INTERVAL_DATETIMEREGIONIDVERSIONNORAISE_PERFORMANCERAISE_REASON_FLAGLOWER_PERFORMANCELOWER_REASON_FLAGLASTCHANGED
2026/09/01 00:05:00NSW11-183.01425082026/09/01 00:10:20
2026/09/01 00:05:00QLD11-29.09258082026/09/01 00:10:20
2026/09/01 00:05:00SA11-29.81061082026/09/01 00:10:20
2026/09/01 00:05:00TAS119.33064082026/09/01 00:10:20
2026/09/01 00:05:00VIC11-196.30961082026/09/01 00:10:20
2026/09/01 00:10:00NSW11-26.36536082026/09/01 00:14:20
2026/09/01 00:10:00QLD11-12.9082403.030502026/09/01 00:14:20
2026/09/01 00:10:00SA11-9.5773201.9318102026/09/01 00:14:20
2026/09/01 00:10:00TAS112.804970-1.2150302026/09/01 00:14:20
2026/09/01 00:10:00VIC11-11.289700.9590702026/09/01 00:14:20
2026/09/01 00:15:00NSW11-25.411140-3.4306302026/09/01 00:20:20
...

CSETP.WORLDDVD_FPP_RESIDUAL_PERFORMANCEAEMOPUBLIC2026/10/0917:41:51001791531711851MONTHLY_ARCHIVE001791531711851
IFPPRESIDUAL_PERFORMANCE1INTERVAL_DATETIMEREGIONIDVERSIONNORAISE_PERFORMANCERAISE_REASON_FLAGLOWER_PERFORMANCELOWER_REASON_FLAGLASTCHANGED
DFPPRESIDUAL_PERFORMANCE12026/09/01 00:05:00NSW11-183.01425082026/09/01 00:10:20
DFPPRESIDUAL_PERFORMANCE12026/09/01 00:05:00QLD11-29.09258082026/09/01 00:10:20
DFPPRESIDUAL_PERFORMANCE12026/09/01 00:05:00SA11-29.81061082026/09/01 00:10:20
DFPPRESIDUAL_PERFORMANCE12026/09/01 00:05:00TAS119.33064082026/09/01 00:10:20
DFPPRESIDUAL_PERFORMANCE12026/09/01 00:05:00VIC11-196.30961082026/09/01 00:10:20
DFPPRESIDUAL_PERFORMANCE12026/09/01 00:10:00NSW11-26.36536082026/09/01 00:14:20
DFPPRESIDUAL_PERFORMANCE12026/09/01 00:10:00QLD11-12.9082403.030502026/09/01 00:14:20
DFPPRESIDUAL_PERFORMANCE12026/09/01 00:10:00SA11-9.5773201.9318102026/09/01 00:14:20
DFPPRESIDUAL_PERFORMANCE12026/09/01 00:10:00TAS112.804970-1.2150302026/09/01 00:14:20
DFPPRESIDUAL_PERFORMANCE12026/09/01 00:10:00VIC11-11.289700.9590702026/09/01 00:14:20
DFPPRESIDUAL_PERFORMANCE12026/09/01 00:15:00NSW11-25.411140-3.4306302026/09/01 00:20:20
...
CEND OF REPORT1003

INTERVAL_DATETIME,REGIONID,VERSIONNO,RAISE_PERFORMANCE,RAISE_REASON_FLAG,LOWER_PERFORMANCE,LOWER_REASON_FLAG,LASTCHANGED
"2026/09/01 00:05:00",NSW1,1,-183.01425,0,,8,"2026/09/01 00:10:20"
"2026/09/01 00:05:00",QLD1,1,-29.09258,0,,8,"2026/09/01 00:10:20"
"2026/09/01 00:05:00",SA1,1,-29.81061,0,,8,"2026/09/01 00:10:20"
"2026/09/01 00:05:00",TAS1,1,9.33064,0,,8,"2026/09/01 00:10:20"
"2026/09/01 00:05:00",VIC1,1,-196.30961,0,,8,"2026/09/01 00:10:20"
"2026/09/01 00:10:00",NSW1,1,-26.36536,0,,8,"2026/09/01 00:14:20"
"2026/09/01 00:10:00",QLD1,1,-12.90824,0,3.0305,0,"2026/09/01 00:14:20"
"2026/09/01 00:10:00",SA1,1,-9.57732,0,1.93181,0,"2026/09/01 00:14:20"
"2026/09/01 00:10:00",TAS1,1,2.80497,0,-1.21503,0,"2026/09/01 00:14:20"
"2026/09/01 00:10:00",VIC1,1,-11.2897,0,0.95907,0,"2026/09/01 00:14:20"
"2026/09/01 00:15:00",NSW1,1,-25.41114,0,-3.43063,0,"2026/09/01 00:20:20"
...

C,SETP.WORLD,DVD_FPP_RESIDUAL_PERFORMANCE,AEMO,PUBLIC,2026/10/09,17:41:51,001791531711851,MONTHLY_ARCHIVE,001791531711851
I,FPP,RESIDUAL_PERFORMANCE,1,INTERVAL_DATETIME,REGIONID,VERSIONNO,RAISE_PERFORMANCE,RAISE_REASON_FLAG,LOWER_PERFORMANCE,LOWER_REASON_FLAG,LASTCHANGED
D,FPP,RESIDUAL_PERFORMANCE,1,"2026/09/01 00:05:00",NSW1,1,-183.01425,0,,8,"2026/09/01 00:10:20"
D,FPP,RESIDUAL_PERFORMANCE,1,"2026/09/01 00:05:00",QLD1,1,-29.09258,0,,8,"2026/09/01 00:10:20"
D,FPP,RESIDUAL_PERFORMANCE,1,"2026/09/01 00:05:00",SA1,1,-29.81061,0,,8,"2026/09/01 00:10:20"
D,FPP,RESIDUAL_PERFORMANCE,1,"2026/09/01 00:05:00",TAS1,1,9.33064,0,,8,"2026/09/01 00:10:20"
D,FPP,RESIDUAL_PERFORMANCE,1,"2026/09/01 00:05:00",VIC1,1,-196.30961,0,,8,"2026/09/01 00:10:20"
D,FPP,RESIDUAL_PERFORMANCE,1,"2026/09/01 00:10:00",NSW1,1,-26.36536,0,,8,"2026/09/01 00:14:20"
D,FPP,RESIDUAL_PERFORMANCE,1,"2026/09/01 00:10:00",QLD1,1,-12.90824,0,3.0305,0,"2026/09/01 00:14:20"
D,FPP,RESIDUAL_PERFORMANCE,1,"2026/09/01 00:10:00",SA1,1,-9.57732,0,1.93181,0,"2026/09/01 00:14:20"
D,FPP,RESIDUAL_PERFORMANCE,1,"2026/09/01 00:10:00",TAS1,1,2.80497,0,-1.21503,0,"2026/09/01 00:14:20"
D,FPP,RESIDUAL_PERFORMANCE,1,"2026/09/01 00:10:00",VIC1,1,-11.2897,0,0.95907,0,"2026/09/01 00:14:20"
D,FPP,RESIDUAL_PERFORMANCE,1,"2026/09/01 00:15:00",NSW1,1,-25.41114,0,-3.43063,0,"2026/09/01 00:20:20"
...
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).

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 7 MB when zipped, 68 MB as unzipped CSV.

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 RESIDUAL_PERFORMANCE 1

This means that the CSV files start with metadata columns:

I,FPP,RESIDUAL_PERFORMANCE,1,...
D,FPP,RESIDUAL_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