STPASA_UNITSOLUTION

STPASA_UNITSOLUTION shows the unit results from the capacity evaluations for each period of the study.

Deprecation Warning

This table is in the HISTORICAL TABLES package, so is likely deprecated.

STPASA_UNITSOLUTION was discontinued in the End Year 2005 MMS Release. See Change Notice 512c for further details.

STPASA_UNITSOLUTION is confidential data.

Source

STPASA_UNITSOLUTION is updated each STPASA run (i.e. every 2 hours).

Column Schema

DUID

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(10) Polars Type: pl.Categorical

This column is mandatory, and a primary key.

Example: W/HOE#2

INTERVAL_DATETIME

The unique identifier for the interval within this study

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 study

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

This column is mandatory, and a primary key.

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

RUNTYPE

Type of run. Values are RELIABILITY_LRC and OUTAGE_LRC

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

• RELIABILITY_LRC

• OUTAGE_LRC

This column is mandatory, and a primary key.

CAPACITYAVAILABLE

The available MW capacity for the period

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

This column is not mandatory.

CAPACITYMARGINALVALUE

Capacity adequacy assessment marginal value, 0 if not binding

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

This column is not mandatory.

CAPACITYVIOLATIONDEGREE

Capacity adequacy assessment violation degree for unit capacity; 0 if not violating

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

This column is not mandatory.

CONNECTIONPOINTID

Connection point identifier

SQL Type: VARCHAR2(10) Polars Type: pl.Categorical

This column is not mandatory.

ENERGYAVAILABLE

The energy limit (MWH) over this energy block for the energy constrained unit

SQL Type: NUMBER(10,0) Polars Type: pl.Int64

This column is not mandatory.

ENERGYCONSTRAINED

0 if not energy constrained, 1 if energy constrained for this energy block

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

This column is not mandatory.

EXPECTEDMAXCAPACITY

Max MW capacity that can be obtained in case of capacity scarcity from units subject to network and energy constraints.

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

This column is not mandatory.

LASTCHANGED

Last changed date of this record

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

PASAAVAILABILITY

The physical plant capability including any capability that can be made available within 24 hrs

SQL Type: NUMBER(12,0) Polars Type: pl.UInt64

This column is not mandatory.

New Data Timing

Each ST PASA 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("INTERVAL_DATETIME", "RUN_DATETIME", "LASTCHANGED").str.strptime(
            pl.Datetime, dt_s_fmt
        ),
    )
    # Polars can't cast string straight to bool. Need to go via something else.
    .cast({"ENERGYCONSTRAINED": pl.UInt8})
    .cast(
        {
            "DUID": pl.Categorical,
            "RUNTYPE": pl.Enum(["RELIABILITY_LRC", "OUTAGE_LRC"]),
            "CAPACITYAVAILABLE": pl.Float64,
            "CAPACITYMARGINALVALUE": pl.Float64,
            "CAPACITYVIOLATIONDEGREE": pl.Float64,
            "CONNECTIONPOINTID": pl.Categorical,
            "ENERGYAVAILABLE": int,  # or pl.Int64
            "ENERGYCONSTRAINED": pl.Boolean,
            "EXPECTEDMAXCAPACITY": pl.Float64,
            "PASAAVAILABILITY": int,  # or pl.UInt64
        }
    )
    # Deduplicate
    # We want one row per ['DUID', 'INTERVAL_DATETIME', 'RUN_DATETIME', 'RUNTYPE']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(
        subset=["DUID", "INTERVAL_DATETIME", "RUN_DATETIME", "RUNTYPE"], keep="last"
    )
)

Nemosis

This table is not yet available in Nemosis.

Availability

Visibility: Private

This table is not available in the public monthly MMSDM archive. Is is only available to registered market participants over FTP inside a VPN.

Deduplication and PDR Loader Configuration

The primary keys are:

• DUID

• INTERVAL_DATETIME

• RUN_DATETIME

• RUNTYPE

PDR Loader configuration does not mention this table, so it is unclear how to choose which row to keep when there are multiple rows for a given set of primary keys.

You should probably take the row with the maximum LASTCHANGED.

Indexes

• LASTCHANGED

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: HISTORICAL TABLES

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