STPASA_FNM_ZONESOLUTION

STPASA_FNM_ZONESOLUTION shows zone demand, cleared value of resources, spare capacity, losses for each run type and interval.

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

RUNTYPE

Run Type (BASE, RELIABILITY, WARNING)

The SQL type is VARCHAR2(20). This is actually a factor/enum, with values:

• BASE

• RELIABILITY

• WARNING

This column is mandatory, and a primary key.

ZONEID

Zone identifier

SQL Type: VARCHAR2(30) Polars Type: pl.Categorical

This column is mandatory, and a primary key.

BDU_CLEARED

Cleared Generation (positive) or Consumption (negative) from bidirectional units (MW)

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

This column is not mandatory.

CLEAREDDEMAND

Cleared Demand (MW)

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

This column is not mandatory.

CLEAREDLOSSES

Cleared Grid Losses (MW)

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

This column is not mandatory.

CLEAREDNETINTERCHANGE

Cleared Net Export (positive) or Net Import (negative) (MW)

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

This column is not mandatory.

CLEAREDSUPPLY

Cleared Generation (MW)

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

This column is not mandatory.

DEFICITCONDITION

Deficit Condition (DEFICITCONDITION) > 0 if a supply deficit exists and this Zone does not contain the Regional Reference Node. DEFICITCONDITION = 3 if deficit in BASE run DEFICITCONDITION = 2 if deficit in RELIABILITY run DEFICITCONDITION = 1 if deficit in WARNING run

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

This column is not mandatory.

DEMAND_UNCERTAINTY_MARGIN

Aggregate Uncertainty Margin adjustment (increase) to most probable Demand Forecast (MW)

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

This column is not mandatory.

ENERGYCONSTRAINED_CLEARED

Cleared Generation from energy-constrained resources - that is, from bidirectional units and generating units subject to daily energy limits (MW)

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

This column is not mandatory.

ENERGYUNCONSTRAINED_CLEARED

Cleared Generation from non energy-constrained resources - that is, excluding bidirectional units and generating units subject to daily energy limits (MW)

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

This column is not mandatory.

INITIALDEMAND

Most probable Demand Forecast adjusted by Demand Uncertainty Margin (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 = 3 if deficit in BASE run LORCONDITION = 2 if deficit in RELIABILITY run 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_GEN_AUX_LOAD

Aggregate Auxiliary Load adjustment to uncertainty-adjusted Bid MaxAvail of all scheduled generating units (MW)

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

This column is not mandatory.

SCHED_GEN_UNCERTAINTY_MARGIN

Aggregate Uncertainty Margin adjustment (decrease) to Scheduled Generation Bid Max Avail (MW)

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

This column is not mandatory.

SPARECAPACITY

Spare generation capacity = max(0, Available Generation minus [Cleared Generation minus Cleared Net Interchange]) (MW)

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

This column is not mandatory.

SS_CLEARED

Cleared Generation from semi-scheduled generating units (MW)

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

This column is not mandatory.

SS_SOLAR_CLEARED

Cleared Generation from semi-scheduled solar generating units (MW)

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

This column is not mandatory.

SS_WIND_CLEARED

Cleared Generation from semi-scheduled wind generating units (MW)

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

This column is not mandatory.

SUPPLYDEFICIT

Supply Deficit at loads = Max(0, Initial Demand minus Cleared Demand) where Cleared Demand = (Cleared Generation minus Cleared Losses minus Cleared Net Interchange) (MW)

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

This column is not mandatory.

VRE_GEN_UNCERTAINTY_MARGIN

Aggregate Uncertainty Margin adjustment (decrease) to most probable VRE Forecast (MW)

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

This column is not mandatory.

New Data Timing

Every 60 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(
        {
            "RUNTYPE": pl.Enum(["BASE", "RELIABILITY", "WARNING"]),
            "ZONEID": pl.Categorical,
            "BDU_CLEARED": pl.Float64,
            "CLEAREDDEMAND": pl.Float64,
            "CLEAREDLOSSES": pl.Float64,
            "CLEAREDNETINTERCHANGE": pl.Float64,
            "CLEAREDSUPPLY": pl.Float64,
            "DEFICITCONDITION": int,  # or pl.UInt8
            "DEMAND_UNCERTAINTY_MARGIN": pl.Float64,
            "ENERGYCONSTRAINED_CLEARED": pl.Float64,
            "ENERGYUNCONSTRAINED_CLEARED": pl.Float64,
            "INITIALDEMAND": pl.Float64,
            "LORCONDITION": int,  # or pl.UInt8
            "REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
            "SCHED_GEN_AUX_LOAD": pl.Float64,
            "SCHED_GEN_UNCERTAINTY_MARGIN": pl.Float64,
            "SPARECAPACITY": pl.Float64,
            "SS_CLEARED": pl.Float64,
            "SS_SOLAR_CLEARED": pl.Float64,
            "SS_WIND_CLEARED": pl.Float64,
            "SUPPLYDEFICIT": pl.Float64,
            "VRE_GEN_UNCERTAINTY_MARGIN": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['INTERVAL_DATETIME', 'RUN_DATETIME', 'RUNTYPE', 'ZONEID']
    # Tie-breaker: greatest value of LASTCHANGED
    .sort(LASTCHANGED, descending=False)
    .unique(
        subset=["INTERVAL_DATETIME", "RUN_DATETIME", "RUNTYPE", "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

• RUNTYPE

• 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
STPASA	FNM_ZONESOLUTION	1

This means that the CSV files start with metadata columns:

I,STPASA,FNM_ZONESOLUTION,1,...
D,STPASA,FNM_ZONESOLUTION,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: STPASA_SOLUTION

Copy as Markdown