PDPASA_FNM_ZONESUMMARY

PDPASA_FNM_ZONESUMMARY shows the summary of PDPASA outcome for each zone.

Column Schema

INTERVAL_DATETIME

End date time of the interval

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

This column is mandatory, and a primary key.

Example: 2026/01/20 00:05:00

RUN_DATETIME

Unique Timestamp Identifier for this 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: 2026/01/20 00:05:00

ZONEID

Zone identifier

SQL Type: VARCHAR2(30) Polars Type: str

This column is mandatory, and a primary key.

DEFICITCONDITION

Deficit Condition (DEFICITCONDITION) > 0 if a supply deficit only exists in a Zone for this Region that does not contain the Regional Reference Node. DEFICITCONDITION indicates the most severe condition: DEFICITCONDITION = 3 if deficit in BASE run; else DEFICITCONDITION = 2 if deficit in RELIABILITY run; else DEFICITCONDITION = 1 if deficit in WARNING run

SQL Type: NUMBER(1,0) Polars Type: pl.Int8

This column is not mandatory.

DEMAND50

50% Probability of Exceedance demand forecast (MW)

SQL Type: NUMBER(12,2) Polars Type: pl.Float64

This column is not mandatory.

DEMAND50_UNSCHED_GEN

50% Probability of Exceedance demand forecast plus Aggregate Generation Forecast of all non-scheduled and exempt generation (MW)

SQL Type: NUMBER(12,2) Polars Type: pl.Float64

This column is not mandatory.

LASTCHANGED

Date time this record was created

For more information, see the dedicated documentation page.

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

This column is not mandatory.

Example: 2026/01/20 00:05:00

LORCONDITION

Lack of Reserve Condition (LORCONDITION) > 0 if a supply deficit exists and this Zone contains the Regional Reference Node LORCONDITION indicates the most severe condition: LORCONDITION = 3 if deficit in BASE run; else LORCONDITION = 2 if deficit in RELIABILITY run; else LORCONDITION = 1 if deficit in WARNING run

SQL Type: NUMBER(1,0) Polars Type: pl.UInt8

This column is not mandatory.

REGIONID

Region identifier of the Region containing this Zone

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 not mandatory.

SCHED_LOAD_CAPACITYAVAIL

Aggregate Bid MaxAvail of all scheduled loads (MW)

SQL Type: NUMBER(12,2) Polars Type: pl.Float64

This column is not mandatory.

SCHED_SS_GEN_CAPACITYAVAIL

Aggregate Bid MaxAvail of all scheduled generating units, scheduled bidirectional units (Gen side) and semi-scheduled generating units, with latter capped at UIGF (MW)

SQL Type: NUMBER(12,2) Polars Type: pl.Float64

This column is not mandatory.

SCHED_SS_GEN_PASAAVAIL

Aggregate Bid PASAAvailability of all scheduled generating units and scheduled bidirectional units (Gen side) with a Bid Recall Period less than (Interval_DateTime minus Run_DateTime) plus UIGF for all semi-scheduled generating units (MW)

SQL Type: NUMBER(12,2) Polars Type: pl.Float64

This column is not mandatory.

SS_SOLAR_UIGF

Aggregate 50% Probability of Exceedance Unconstrained Intermittent Generation Forecast (UIGF) of all solar semi-scheduled generating units (MW)

SQL Type: NUMBER(12,2) Polars Type: pl.Float64

This column is not mandatory.

SS_UIGF

Aggregate 50% Probability of Exceedance Unconstrained Intermittent Generation Forecast (UIGF) of all semi-scheduled generating units (MW)

SQL Type: NUMBER(12,2) Polars Type: pl.Float64

This column is not mandatory.

SS_WIND_UIGF

Aggregate 50% Probability of Exceedance Unconstrained Intermittent Generation Forecast (UIGF) of all wind semi-scheduled generating units (MW)

SQL Type: NUMBER(12,2) Polars Type: pl.Float64

This column is not mandatory.

UNSCHED_GEN_CAPACITYAVAIL

Aggregate Generation Forecast of all non-scheduled and exempt generation (MW)

SQL Type: NUMBER(12,2) Polars Type: pl.Float64

This column is not mandatory.

New Data Timing

Every 30 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", "RUN_DATETIME", "LASTCHANGED").str.strptime(
            pl.Datetime, dt_s_fmt
        ),
    )
    .cast(
        {
            "ZONEID": str,
            "DEFICITCONDITION": int,  # or pl.Int8
            "DEMAND50": pl.Float64,
            "DEMAND50_UNSCHED_GEN": pl.Float64,
            "LORCONDITION": int,  # or pl.UInt8
            "REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
            "SCHED_LOAD_CAPACITYAVAIL": pl.Float64,
            "SCHED_SS_GEN_CAPACITYAVAIL": pl.Float64,
            "SCHED_SS_GEN_PASAAVAIL": pl.Float64,
            "SS_SOLAR_UIGF": pl.Float64,
            "SS_UIGF": pl.Float64,
            "SS_WIND_UIGF": pl.Float64,
            "UNSCHED_GEN_CAPACITYAVAIL": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['INTERVAL_DATETIME', 'RUN_DATETIME', 'ZONEID']
    # Tie-breaker: greatest value of LASTCHANGED
    .sort(LASTCHANGED, descending=False)
    .unique(subset=["INTERVAL_DATETIME", "RUN_DATETIME", "ZONEID"], keep="last")
)

Nemosis

This table is not yet available in Nemosis.

Availability

AEMO's documentation states that this table's visibility is "Public". However, it is not available in the public monthly MMSDM archive. AEMO probably call this public because it is available to all market participants over private FTP.

There is a chance that is is still public to non-participants. It may only be available for some months; or (rarely) it may be available in a non-standard location.

Deduplication and PDR Loader Configuration

The primary keys are:

• INTERVAL_DATETIME

• RUN_DATETIME

• ZONEID

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	FNM_ZONESUMMARY	1

This means that the CSV files start with metadata columns:

I,PDPASA,FNM_ZONESUMMARY,1,...
D,PDPASA,FNM_ZONESUMMARY,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: PDPASA

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