SET_CSP_SUPPORTDATA_ENERGYDIFF

A settlements table for the publication of support data for the Snowy CSP derogation amounts. This table publishes energy differential information for each half-hour interval in the settlement run

Deprecation Warning

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

THIS TABLE WILL BE DISCONTINUED AS PART OF THE END OF YEAR 2009 MMS RELEASESource

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

PERIODID

Period identifier

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

This column is mandatory, and a primary key.

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.

EVA

Energy value adjustment for northward flows in the half-hour interval

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

This column is not mandatory.

FLOW_DIRECTION

Indicates the determined direction of flow in the half hour. Will be either “”NORTH” or “SOUTH”

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

• NORTH

• SOUTH

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_AGE

Energy output of the Lower Tumut unit in the half hour interval

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

This column is not mandatory.

LOWERTUMUT_EVDP

Lower Tumut Energy Value Differential for the half hour interval

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

This column is not mandatory.

LOWERTUMUT_SPDP

Lower Tumut Substitute Price for the half hour interval

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

This column is not mandatory.

TOTAL_X

Sum of all "ABS_X" values in the half hour

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

This column is not mandatory.

TOTAL_Y

Sum of all "ABS_Y" values in the half hour

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

This column is not mandatory.

UPPERTUMUT_AGE

Energy output of the Upper Tumut unit in the half hour interval

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

This column is not mandatory.

UPPERTUMUT_EVDP

Upper Tumut Energy Value Differential for the half hour interval

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

This column is not mandatory.

UPPERTUMUT_SPDP

Upper Tumut Substitute Price for the half hour interval

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

This column is not mandatory.

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("SETTLEMENTDATE", "LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast(
        {
            "PERIODID": int,  # or pl.Int16
            "VERSIONNO": int,  # or pl.UInt16
            "EVA": pl.Float64,
            "FLOW_DIRECTION": pl.Enum(["NORTH", "SOUTH"]),
            "LOWERTUMUT_AGE": pl.Float64,
            "LOWERTUMUT_EVDP": pl.Float64,
            "LOWERTUMUT_SPDP": pl.Float64,
            "TOTAL_X": pl.Float64,
            "TOTAL_Y": pl.Float64,
            "UPPERTUMUT_AGE": pl.Float64,
            "UPPERTUMUT_EVDP": pl.Float64,
            "UPPERTUMUT_SPDP": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['PERIODID', 'SETTLEMENTDATE', 'VERSIONNO']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(subset=["PERIODID", "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:

• PERIODID

• 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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