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PDPASA_DUIDAVAILABILITY

This report delivers available capacity, PASA availability and given recall period for all scheduled resources. Note that for an MNSP, DUID = LINKID in the MNSP_INTERCONNECTOR table.

Column Schema

DUID

NEM Dispatchable Unit Identifier

Warning

This column may contain funny characters such as / (slash) or # (hash). This may break hive-style partitioning.

For more information, see the dedicated documentation page.

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

This column is mandatory, and a primary key.

Examples:

  • ADPPV1
  • AGLHAL
  • AGLSOM
  • BOWWPV1
  • BRAEMAR1
  • BRAEMAR2
  • CHYTWF1
  • CLARESF1
  • CLEMGPWF
  • CLERMSF1

INTERVAL_DATETIME

Half hour ended interval

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

This column is mandatory, and a primary key.

Example: 2026/09/09 18:30:00

RUN_DATETIME

PDPASA run, identified by the first half hour ended interval of the run.

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

This column is mandatory, and a primary key.

Example: 2026/09/09 18:30:00

GENERATION_MAX_AVAILABILITY

Available Capacity for a scheduled generating unit, semi-scheduled generating unit, BDU (Gen side), WDR or MNSP.

SQL Type: NUMBER(12,6) Polars Type: pl.Float64

This column is not mandatory.

Examples: 0.033, 0, 1, 255

GENERATION_PASA_AVAILABILITY

PASA Availability for a scheduled generating unit, BDU (Gen side), WDR or MNSP. Null for a semi-scheduled generating unit.

SQL Type: NUMBER(12,0) Polars Type: pl.Int64

This column is not mandatory.

Examples: 0, 8, 255

GENERATION_RECALL_PERIOD

Recall Period associated with the PASA Availability for a scheduled generating unit, BDU (Gen side), WDR or MNSP. Null for a semi-scheduled generating unit.

SQL Type: NUMBER(8,3) Polars Type: pl.Float64

This column is not mandatory.

Examples: 0, 1, 24000

LASTCHANGED

Report Creation Date Time.

For more information, see the dedicated documentation page.

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

This column is not mandatory.

Example: 2026/09/09 18:02:09

LOAD_MAX_AVAILABILITY

Available Capacity for a scheduled load or BDU(Load side).

SQL Type: NUMBER(12,6) Polars Type: pl.Float64

This column is not mandatory.

Examples: 0, 89, 386

LOAD_PASA_AVAILABILITY

PASA Availability for a scheduled load or BDU(Load side).

SQL Type: NUMBER(12,0) Polars Type: pl.Int64

This column is not mandatory.

Examples: 8, 600

LOAD_RECALL_PERIOD

Recall Period associated with the PASA Availability for a scheduled load or BDU(Load side).

SQL Type: NUMBER(8,3) Polars Type: pl.Float64

This column is not mandatory.

Examples: 4, 24000

New Data Timing

The start of PDPASA run or near to

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", "LASTCHANGED").str.strptime(
            pl.Datetime, dt_s_fmt
        ),
    )
    .cast(
        {
            "DUID": pl.Categorical,
            "GENERATION_MAX_AVAILABILITY": pl.Float64,
            "GENERATION_PASA_AVAILABILITY": pl.Int64,
            "GENERATION_RECALL_PERIOD": pl.Float64,
            "LOAD_MAX_AVAILABILITY": pl.Float64,
            "LOAD_PASA_AVAILABILITY": pl.Int64,
            "LOAD_RECALL_PERIOD": 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 ['DUID', 'INTERVAL_DATETIME', 'RUN_DATETIME']
    # Tie-breaker: greatest value of LASTCHANGED
    .sort(LASTCHANGED, descending=False)
    .unique(subset=["DUID", "INTERVAL_DATETIME", "RUN_DATETIME"], keep="last")
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_PDPASA_DUIDAVAILABILITY_FILE01_202609010000.zip

RUN_DATETIMEINTERVAL_DATETIMEDUIDGENERATION_MAX_AVAILABILITYGENERATION_PASA_AVAILABILITYGENERATION_RECALL_PERIODLOAD_MAX_AVAILABILITYLOAD_PASA_AVAILABILITYLOAD_RECALL_PERIODLASTCHANGED
2026/09/09 18:30:002026/09/10 19:30:00PUMP20245162026/09/09 18:02:09
2026/09/09 18:30:002026/09/10 20:00:00PUMP20245162026/09/09 18:02:09
2026/09/09 18:30:002026/09/10 20:30:00PUMP20245162026/09/09 18:02:09
2026/09/09 18:30:002026/09/10 21:00:00PUMP20245162026/09/09 18:02:09
2026/09/09 18:30:002026/09/10 21:30:00PUMP20245162026/09/09 18:02:09
2026/09/09 18:30:002026/09/10 22:00:00PUMP20245162026/09/09 18:02:09
2026/09/09 18:30:002026/09/10 22:30:00PUMP20245162026/09/09 18:02:09
2026/09/09 18:30:002026/09/10 23:00:00PUMP20245162026/09/09 18:02:09
2026/09/09 18:30:002026/09/10 23:30:00PUMP20245162026/09/09 18:02:09
2026/09/09 18:30:002026/09/11 00:00:00PUMP20245162026/09/09 18:02:09
2026/09/09 18:30:002026/09/11 00:30:00PUMP20245162026/09/09 18:02:09
...

CSETP.WORLDDVD_PDPASA_DUIDAVAILABILITYAEMOPUBLIC2026/10/0918:52:57001791535977290MONTHLY_ARCHIVE001791535977290
IPDPASADUIDAVAILABILITY1RUN_DATETIMEINTERVAL_DATETIMEDUIDGENERATION_MAX_AVAILABILITYGENERATION_PASA_AVAILABILITYGENERATION_RECALL_PERIODLOAD_MAX_AVAILABILITYLOAD_PASA_AVAILABILITYLOAD_RECALL_PERIODLASTCHANGED
DPDPASADUIDAVAILABILITY12026/09/09 18:30:002026/09/10 19:30:00PUMP20245162026/09/09 18:02:09
DPDPASADUIDAVAILABILITY12026/09/09 18:30:002026/09/10 20:00:00PUMP20245162026/09/09 18:02:09
DPDPASADUIDAVAILABILITY12026/09/09 18:30:002026/09/10 20:30:00PUMP20245162026/09/09 18:02:09
DPDPASADUIDAVAILABILITY12026/09/09 18:30:002026/09/10 21:00:00PUMP20245162026/09/09 18:02:09
DPDPASADUIDAVAILABILITY12026/09/09 18:30:002026/09/10 21:30:00PUMP20245162026/09/09 18:02:09
DPDPASADUIDAVAILABILITY12026/09/09 18:30:002026/09/10 22:00:00PUMP20245162026/09/09 18:02:09
DPDPASADUIDAVAILABILITY12026/09/09 18:30:002026/09/10 22:30:00PUMP20245162026/09/09 18:02:09
DPDPASADUIDAVAILABILITY12026/09/09 18:30:002026/09/10 23:00:00PUMP20245162026/09/09 18:02:09
DPDPASADUIDAVAILABILITY12026/09/09 18:30:002026/09/10 23:30:00PUMP20245162026/09/09 18:02:09
DPDPASADUIDAVAILABILITY12026/09/09 18:30:002026/09/11 00:00:00PUMP20245162026/09/09 18:02:09
DPDPASADUIDAVAILABILITY12026/09/09 18:30:002026/09/11 00:30:00PUMP20245162026/09/09 18:02:09
...
CEND OF REPORT1003

RUN_DATETIME,INTERVAL_DATETIME,DUID,GENERATION_MAX_AVAILABILITY,GENERATION_PASA_AVAILABILITY,GENERATION_RECALL_PERIOD,LOAD_MAX_AVAILABILITY,LOAD_PASA_AVAILABILITY,LOAD_RECALL_PERIOD,LASTCHANGED
"2026/09/09 18:30:00","2026/09/10 19:30:00",PUMP2,,,,0,245,16,"2026/09/09 18:02:09"
"2026/09/09 18:30:00","2026/09/10 20:00:00",PUMP2,,,,0,245,16,"2026/09/09 18:02:09"
"2026/09/09 18:30:00","2026/09/10 20:30:00",PUMP2,,,,0,245,16,"2026/09/09 18:02:09"
"2026/09/09 18:30:00","2026/09/10 21:00:00",PUMP2,,,,0,245,16,"2026/09/09 18:02:09"
"2026/09/09 18:30:00","2026/09/10 21:30:00",PUMP2,,,,0,245,16,"2026/09/09 18:02:09"
"2026/09/09 18:30:00","2026/09/10 22:00:00",PUMP2,,,,0,245,16,"2026/09/09 18:02:09"
"2026/09/09 18:30:00","2026/09/10 22:30:00",PUMP2,,,,0,245,16,"2026/09/09 18:02:09"
"2026/09/09 18:30:00","2026/09/10 23:00:00",PUMP2,,,,0,245,16,"2026/09/09 18:02:09"
"2026/09/09 18:30:00","2026/09/10 23:30:00",PUMP2,,,,0,245,16,"2026/09/09 18:02:09"
"2026/09/09 18:30:00","2026/09/11 00:00:00",PUMP2,,,,0,245,16,"2026/09/09 18:02:09"
"2026/09/09 18:30:00","2026/09/11 00:30:00",PUMP2,,,,0,245,16,"2026/09/09 18:02:09"
...

C,SETP.WORLD,DVD_PDPASA_DUIDAVAILABILITY,AEMO,PUBLIC,2026/10/09,18:52:57,001791535977290,MONTHLY_ARCHIVE,001791535977290
I,PDPASA,DUIDAVAILABILITY,1,RUN_DATETIME,INTERVAL_DATETIME,DUID,GENERATION_MAX_AVAILABILITY,GENERATION_PASA_AVAILABILITY,GENERATION_RECALL_PERIOD,LOAD_MAX_AVAILABILITY,LOAD_PASA_AVAILABILITY,LOAD_RECALL_PERIOD,LASTCHANGED
D,PDPASA,DUIDAVAILABILITY,1,"2026/09/09 18:30:00","2026/09/10 19:30:00",PUMP2,,,,0,245,16,"2026/09/09 18:02:09"
D,PDPASA,DUIDAVAILABILITY,1,"2026/09/09 18:30:00","2026/09/10 20:00:00",PUMP2,,,,0,245,16,"2026/09/09 18:02:09"
D,PDPASA,DUIDAVAILABILITY,1,"2026/09/09 18:30:00","2026/09/10 20:30:00",PUMP2,,,,0,245,16,"2026/09/09 18:02:09"
D,PDPASA,DUIDAVAILABILITY,1,"2026/09/09 18:30:00","2026/09/10 21:00:00",PUMP2,,,,0,245,16,"2026/09/09 18:02:09"
D,PDPASA,DUIDAVAILABILITY,1,"2026/09/09 18:30:00","2026/09/10 21:30:00",PUMP2,,,,0,245,16,"2026/09/09 18:02:09"
D,PDPASA,DUIDAVAILABILITY,1,"2026/09/09 18:30:00","2026/09/10 22:00:00",PUMP2,,,,0,245,16,"2026/09/09 18:02:09"
D,PDPASA,DUIDAVAILABILITY,1,"2026/09/09 18:30:00","2026/09/10 22:30:00",PUMP2,,,,0,245,16,"2026/09/09 18:02:09"
D,PDPASA,DUIDAVAILABILITY,1,"2026/09/09 18:30:00","2026/09/10 23:00:00",PUMP2,,,,0,245,16,"2026/09/09 18:02:09"
D,PDPASA,DUIDAVAILABILITY,1,"2026/09/09 18:30:00","2026/09/10 23:30:00",PUMP2,,,,0,245,16,"2026/09/09 18:02:09"
D,PDPASA,DUIDAVAILABILITY,1,"2026/09/09 18:30:00","2026/09/11 00:00:00",PUMP2,,,,0,245,16,"2026/09/09 18:02:09"
D,PDPASA,DUIDAVAILABILITY,1,"2026/09/09 18:30:00","2026/09/11 00:30:00",PUMP2,,,,0,245,16,"2026/09/09 18:02:09"
...
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 7 of 2025. 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 2 GB when zipped, 21 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
PDPASA DUIDAVAILABILITY 1

This means that the CSV files start with metadata columns:

I,PDPASA,DUIDAVAILABILITY,1,...
D,PDPASA,DUIDAVAILABILITY,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: PDPASA