SET_CSP_SUPPORTDATA_SUBPRICE

A settlements table for the publication of support data for the Snowy CSP derogation amounts. This table publishes substitution price information for each five minute interval in the settlement run

Deprecation Warning

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

Source

Settlements data process is populated at the posting of a billing run in which it is included.

Volume

Estimated number of rows is an average of 1000 per week based on the 35 settlement days posted per week.

Tip

1 second, 6 second, 5 minute, 60 second and regulation refer to types of FCAS markets.

Column Schema

INTERVAL_DATETIME

Dispatch interval identifier

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

This column is mandatory, and a primary key.

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

SETTLEMENTDATE

Settlement Date

Warning

Despite what the name suggests, this is a datetime, not a date.

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

This column is mandatory, and a primary key.

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

VERSIONNO

Settlement run number

SQL Type: NUMBER(3) Polars Type: pl.UInt16

This column is mandatory, and a primary key.

INTERVAL_ABS_X

Sum of all ABS_X values for binding CSP constraints in the dispatch interval

SQL Type: NUMBER(18,8) Polars Type: pl.Float64

This column is not mandatory.

INTERVAL_ABS_Y

Sum of all ABS_Y values for binding CSP constraints in the dispatch interval

SQL Type: NUMBER(18,8) Polars Type: pl.Float64

This column is not mandatory.

IS_CSP_INTERVAL

A flag to indicate whether a binding CSP constraint was present in the dispatch interval. A value of 1 indicates that CSP processing occurred due to a binding CSP constraint, while a value of 0 indicates that no binding CSP constraints were present in this interval

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

This column is not mandatory.

LASTCHANGED

Last changed date for the 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

LOWERTUMUT_CSPA_COEFF

Sum of CSPAxCOEFF for all constraints and Lower Tumut left-hand terms

SQL Type: NUMBER(18,8) Polars Type: pl.Float64

This column is not mandatory.

LOWERTUMUT_PRICE

The dispatch price at the Lower Tumut node

SQL Type: NUMBER(18,8) Polars Type: pl.Float64

This column is not mandatory.

LOWERTUMUT_SPDP

Substitute Price for Lower Tumut

SQL Type: NUMBER(18,8) Polars Type: pl.Float64

This column is not mandatory.

LOWERTUMUT_SPDP_UNCAPPED

LOWERTUMUT_SPDP before VOLL or MPF capping is applied

SQL Type: NUMBER(18,8) Polars Type: pl.Float64

This column is not mandatory.

LOWERTUMUT_TLF

Transmission loss factor of the Lower Tumut unit

SQL Type: NUMBER(18,8) Polars Type: pl.Float64

This column is not mandatory.

PERIODID

Period identifier

SQL Type: NUMBER(3) Polars Type: pl.Int16

This column is not mandatory.

RRP

SNOWY1 RRP for the dispatch interval

For more information, see the dedicated documentation page.

SQL Type: NUMBER(18,8) Polars Type: pl.Float64

This column is not mandatory.

UPPERTUMUT_CSPA_COEFF

Sum of CSPAxCOEFF for all constraints and Upper Tumut left-hand terms

SQL Type: NUMBER(18,8) Polars Type: pl.Float64

This column is not mandatory.

UPPERTUMUT_PRICE

The dispatch price at the Upper Tumut node

SQL Type: NUMBER(18,8) Polars Type: pl.Float64

This column is not mandatory.

UPPERTUMUT_SPDP

Substitute Price for Upper Tumut

SQL Type: NUMBER(18,8) Polars Type: pl.Float64

This column is not mandatory.

UPPERTUMUT_SPDP_UNCAPPED

UPPERTUMUT_SPDP before VOLL or MPF capping is applied

SQL Type: NUMBER(18,8) Polars Type: pl.Float64

This column is not mandatory.

UPPERTUMUT_TLF

Transmission Loss factor of the Upper Tumut unit

SQL Type: NUMBER(18,8) Polars Type: pl.Float64

This column is not mandatory.

New Data Timing

At the posting of a billing 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", "SETTLEMENTDATE", "LASTCHANGED").str.strptime(
            pl.Datetime, dt_s_fmt
        ),
    )
    # Polars can't cast string straight to bool. Need to go via something else.
    .cast({"IS_CSP_INTERVAL": pl.UInt8})
    .cast(
        {
            "VERSIONNO": int,  # or pl.UInt16
            "INTERVAL_ABS_X": pl.Float64,
            "INTERVAL_ABS_Y": pl.Float64,
            "IS_CSP_INTERVAL": pl.Boolean,
            "LOWERTUMUT_CSPA_COEFF": pl.Float64,
            "LOWERTUMUT_PRICE": pl.Float64,
            "LOWERTUMUT_SPDP": pl.Float64,
            "LOWERTUMUT_SPDP_UNCAPPED": pl.Float64,
            "LOWERTUMUT_TLF": pl.Float64,
            "PERIODID": int,  # or pl.Int16
            "RRP": pl.Float64,
            "UPPERTUMUT_CSPA_COEFF": pl.Float64,
            "UPPERTUMUT_PRICE": pl.Float64,
            "UPPERTUMUT_SPDP": pl.Float64,
            "UPPERTUMUT_SPDP_UNCAPPED": pl.Float64,
            "UPPERTUMUT_TLF": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['INTERVAL_DATETIME', 'SETTLEMENTDATE', 'VERSIONNO']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(subset=["INTERVAL_DATETIME", "SETTLEMENTDATE", "VERSIONNO"], 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

• SETTLEMENTDATE

• VERSIONNO

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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