SETGENDATA

SETGENDATA shows meter settlement data for each generation meter point. A regional summary is also provided. The table is populated for participants using Data Model 5.2. For participants using Data Model 5.3 or higher, SET_ENERGY_TRANSACTIONS and SET_ENERGY_GENSET_DETAIL replace SETGENDATA.

Deprecation Warning

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

SETGENDATA shows generator meter details, and SETGENDATA data is confidential to the participant.

By comparison, the regional summary (SETGENDATAREGION) is public data.

Source

SETGENDATA updates with each Settlement run.

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

GENSETID

Physical unit identifier

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

This column is mandatory, and a primary key.

PERIODID

Settlements Trading Interval.

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

This column is mandatory, and a primary key.

REGIONID

Region Identifier

For more information, see the dedicated documentation page.

The SQL type is VARCHAR2(10). This is actually a factor/enum, with values:

QLD1
VIC1
TAS1
NSW1
SA1
SNOWY1

This column is mandatory, and a primary key.

SETTLEMENTDATE

Calendar 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

STATIONID

Station Identifier

For more information, see the dedicated documentation page.

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

This column is mandatory, and a primary key.

VERSIONNO

Settlement run no

SQL Type: NUMBER(10,0) Polars Type: pl.UInt64

This column is mandatory, and a primary key.

AENERGY

Purchased Energy

SQL Type: NUMBER(16,6) Polars Type: pl.Float64

This column is not mandatory.

APC

Administered Price Compensation

SQL Type: NUMBER(16,6) Polars Type: pl.Float64

This column is not mandatory.

CPEEP

Connection Point Excess Energy Price = EEP * TLF

SQL Type: NUMBER(16,6) Polars Type: pl.Float64

This column is not mandatory.

CPRRP

Connection Point Price = RRP * TLF

SQL Type: NUMBER(16,6) Polars Type: pl.Float64

This column is not mandatory.

EEP

Excess Energy Price

SQL Type: NUMBER(16,6) Polars Type: pl.Float64

This column is not mandatory.

ENERGYCOST

Cost of net energy $

SQL Type: NUMBER(16,6) Polars Type: pl.Float64

This column is not mandatory.

EXCESSENERGYCOST

Cost of excess energy $

SQL Type: NUMBER(16,6) Polars Type: pl.Float64

This column is not mandatory.

EXPENERGY

Export Energy (Generator Purchases) (MWh)

SQL Type: NUMBER(15,6) Polars Type: pl.Float64

This column is not mandatory.

EXPENERGYCOST

Export Energy Cost ($)

SQL Type: NUMBER(15,6) Polars Type: pl.Float64

This column is not mandatory.

GENERGY

Generated energy

SQL Type: NUMBER(16,6) Polars Type: pl.Float64

This column is not mandatory.

LASTCHANGED

Last date and time record changed

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

MDA

Metering Data Agent

SQL Type: VARCHAR2(10) Polars Type: str

This column is not mandatory.

METERRUNNO

Identifier of the meter run used in this settlement calculation

SQL Type: NUMBER(6,0) Polars Type: pl.UInt32

This column is not mandatory.

NETENERGY

Generated energy

SQL Type: NUMBER(16,6) Polars Type: pl.Float64

This column is not mandatory.

PARTICIPANTID

Unique participant identifier

For more information, see the dedicated documentation page.

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

This column is not mandatory.

RRP

Regional Reference Price

For more information, see the dedicated documentation page.

SQL Type: NUMBER(20,5) Polars Type: pl.Float64

This column is not mandatory.

SECONDARY_TLF

Secondary Transmission Loss Factor

SQL Type: NUMBER(7,5) Polars Type: pl.Float64

This column is not mandatory.

TLF

Transmission Loss Factor

SQL Type: NUMBER(7,5) Polars Type: pl.Float64

This column is not mandatory.

APOWER

Not used

SQL Type: NUMBER(16,6) Polars Type: pl.Float64

This column is unused.

GPOWER

Not used

SQL Type: NUMBER(16,6) Polars Type: pl.Float64

This column is unused.

RESC

Not used

SQL Type: NUMBER(16,6) Polars Type: pl.Float64

This column is unused.

RESP

Not used

SQL Type: NUMBER(16,6) Polars Type: pl.Float64

This column is unused.

New Data Timing

After each settlement 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("SETTLEMENTDATE", "LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast(
        {
            "DUID": pl.Categorical,
            "GENSETID": pl.Categorical,
            "PERIODID": int,  # or pl.Int64
            "REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
            "STATIONID": pl.Categorical,
            "VERSIONNO": int,  # or pl.UInt64
            "AENERGY": pl.Float64,
            "APC": pl.Float64,
            "CPEEP": pl.Float64,
            "CPRRP": pl.Float64,
            "EEP": pl.Float64,
            "ENERGYCOST": pl.Float64,
            "EXCESSENERGYCOST": pl.Float64,
            "EXPENERGY": pl.Float64,
            "EXPENERGYCOST": pl.Float64,
            "GENERGY": pl.Float64,
            "MDA": str,
            "METERRUNNO": int,  # or pl.UInt32
            "NETENERGY": pl.Float64,
            "PARTICIPANTID": pl.Categorical,
            "RRP": pl.Float64,
            "SECONDARY_TLF": pl.Float64,
            "TLF": pl.Float64,
            "APOWER": pl.Float64,
            "GPOWER": pl.Float64,
            "RESC": pl.Float64,
            "RESP": pl.Float64,
        }
    )
    # Drop deprecated/unused columns
    .drop("APOWER", "GPOWER", "RESC", "RESP")
    # Deduplicate
    # We want one row per ['DUID', 'GENSETID', 'PERIODID', 'REGIONID', 'SETTLEMENTDATE', 'STATIONID', 'VERSIONNO']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(
        subset=[
            "DUID",
            "GENSETID",
            "PERIODID",
            "REGIONID",
            "SETTLEMENTDATE",
            "STATIONID",
            "VERSIONNO",
        ],
        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
GENSETID
PERIODID
REGIONID
SETTLEMENTDATE
STATIONID
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.