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DISPATCH_FCAS_REQ_RUN

The constraint FCAS processor run details. 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

RUN_DATETIME

The run date and time of the dispatch case that triggers the constraint FCAS processor run

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

This column is mandatory, and a primary key.

Example: 2026/09/01 00:05:00

RUNNO

The dispatch 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.

Examples: 1, 2

LASTCHANGED

The last time the constraint FCAS processor was executed for this case run time.

For more information, see the dedicated documentation page.

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

This column is not mandatory.

Example: 2026/09/01 00:00:15

New Data Timing

Public at the completion of a constraint FCAS processor run for a dispatch run

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("RUN_DATETIME", "LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast({"RUNNO": int})  # or pl.UInt32
    # Deduplicate
    # We want one row per ['RUN_DATETIME', 'RUNNO']
    # Tie-breaker: greatest value of LASTCHANGED
    .sort(LASTCHANGED, descending=False)
    .unique(subset=["RUN_DATETIME", "RUNNO"], keep="last")
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_DISPATCH_FCAS_REQ_RUN_FILE01_202609010000.zip

RUN_DATETIMERUNNOLASTCHANGED
2026/09/01 00:05:0012026/09/01 00:00:15
2026/09/01 00:10:0012026/09/01 00:05:15
2026/09/01 00:15:0012026/09/01 00:10:15
2026/09/01 00:20:0012026/09/01 00:15:15
2026/09/01 00:25:0012026/09/01 00:20:15
2026/09/01 00:30:0012026/09/01 00:25:15
2026/09/01 00:40:0012026/09/01 00:35:15
2026/09/01 00:45:0012026/09/01 00:40:15
2026/09/01 01:00:0012026/09/01 00:55:15
2026/09/01 00:35:0012026/09/01 00:30:15
2026/09/01 00:50:0012026/09/01 00:45:15
...

CSETP.WORLDDVD_DISPATCH_FCAS_REQ_RUNAEMOPUBLIC2026/10/0917:33:59001791531239822MONTHLY_ARCHIVE001791531239822
IDISPATCHFCAS_REQ_RUN1RUN_DATETIMERUNNOLASTCHANGED
DDISPATCHFCAS_REQ_RUN12026/09/01 00:05:0012026/09/01 00:00:15
DDISPATCHFCAS_REQ_RUN12026/09/01 00:10:0012026/09/01 00:05:15
DDISPATCHFCAS_REQ_RUN12026/09/01 00:15:0012026/09/01 00:10:15
DDISPATCHFCAS_REQ_RUN12026/09/01 00:20:0012026/09/01 00:15:15
DDISPATCHFCAS_REQ_RUN12026/09/01 00:25:0012026/09/01 00:20:15
DDISPATCHFCAS_REQ_RUN12026/09/01 00:30:0012026/09/01 00:25:15
DDISPATCHFCAS_REQ_RUN12026/09/01 00:40:0012026/09/01 00:35:15
DDISPATCHFCAS_REQ_RUN12026/09/01 00:45:0012026/09/01 00:40:15
DDISPATCHFCAS_REQ_RUN12026/09/01 01:00:0012026/09/01 00:55:15
DDISPATCHFCAS_REQ_RUN12026/09/01 00:35:0012026/09/01 00:30:15
DDISPATCHFCAS_REQ_RUN12026/09/01 00:50:0012026/09/01 00:45:15
...
CEND OF REPORT1003

RUN_DATETIME,RUNNO,LASTCHANGED
"2026/09/01 00:05:00",1,"2026/09/01 00:00:15"
"2026/09/01 00:10:00",1,"2026/09/01 00:05:15"
"2026/09/01 00:15:00",1,"2026/09/01 00:10:15"
"2026/09/01 00:20:00",1,"2026/09/01 00:15:15"
"2026/09/01 00:25:00",1,"2026/09/01 00:20:15"
"2026/09/01 00:30:00",1,"2026/09/01 00:25:15"
"2026/09/01 00:40:00",1,"2026/09/01 00:35:15"
"2026/09/01 00:45:00",1,"2026/09/01 00:40:15"
"2026/09/01 01:00:00",1,"2026/09/01 00:55:15"
"2026/09/01 00:35:00",1,"2026/09/01 00:30:15"
"2026/09/01 00:50:00",1,"2026/09/01 00:45:15"
...

C,SETP.WORLD,DVD_DISPATCH_FCAS_REQ_RUN,AEMO,PUBLIC,2026/10/09,17:33:59,001791531239822,MONTHLY_ARCHIVE,001791531239822
I,DISPATCH,FCAS_REQ_RUN,1,RUN_DATETIME,RUNNO,LASTCHANGED
D,DISPATCH,FCAS_REQ_RUN,1,"2026/09/01 00:05:00",1,"2026/09/01 00:00:15"
D,DISPATCH,FCAS_REQ_RUN,1,"2026/09/01 00:10:00",1,"2026/09/01 00:05:15"
D,DISPATCH,FCAS_REQ_RUN,1,"2026/09/01 00:15:00",1,"2026/09/01 00:10:15"
D,DISPATCH,FCAS_REQ_RUN,1,"2026/09/01 00:20:00",1,"2026/09/01 00:15:15"
D,DISPATCH,FCAS_REQ_RUN,1,"2026/09/01 00:25:00",1,"2026/09/01 00:20:15"
D,DISPATCH,FCAS_REQ_RUN,1,"2026/09/01 00:30:00",1,"2026/09/01 00:25:15"
D,DISPATCH,FCAS_REQ_RUN,1,"2026/09/01 00:40:00",1,"2026/09/01 00:35:15"
D,DISPATCH,FCAS_REQ_RUN,1,"2026/09/01 00:45:00",1,"2026/09/01 00:40:15"
D,DISPATCH,FCAS_REQ_RUN,1,"2026/09/01 01:00:00",1,"2026/09/01 00:55:15"
D,DISPATCH,FCAS_REQ_RUN,1,"2026/09/01 00:35:00",1,"2026/09/01 00:30:15"
D,DISPATCH,FCAS_REQ_RUN,1,"2026/09/01 00:50:00",1,"2026/09/01 00:45:15"
...
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 584 KB when zipped, 6 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
DISPATCH FCAS_REQ_RUN 1

This means that the CSV files start with metadata columns:

I,DISPATCH,FCAS_REQ_RUN,1,...
D,DISPATCH,FCAS_REQ_RUN,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: DISPATCH