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MTPASA_LOLPRESULT

Results for Loss of Load Probability (LOLP) run per day

MTPASA_LOLPRESULT is public data.

Column Schema

DAY

Day this result is for

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

This column is mandatory, and a primary key.

Example: 2026/09/13 00:00:00

REGIONID

The unique region identifier

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 mandatory, and a primary key.

RUN_DATETIME

Date processing of the run begins.

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

This column is mandatory, and a primary key.

Example: 2026/09/07 01:00:00

RUN_NO

Unique run id.

SQL Type: NUMBER(4) Polars Type: pl.Int16

This column is mandatory, and a primary key.

Example: 1177

RUNTYPE

Type of run. Always LOLP

SQL Type: VARCHAR2(20) Polars Type: pl.Categorical

This column is mandatory, and a primary key.

Example: LOLP

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: 2026/09/08 12:29:02

LOSSOFLOADMAGNITUDE

Loss of Load Magnitude for the worst interval in this region. Values are LOW, MEDIUM, HIGH

SQL Type: VARCHAR2(20) Polars Type: str

This column is not mandatory.

Examples:

  • High
  • Low
  • Medium

LOSSOFLOADPROBABILITY

Loss of Load Probability for the worst interval in this region

SQL Type: NUMBER(8,5) Polars Type: pl.Float64

This column is not mandatory.

Examples: 0.2, 0, 1, 45

WORST_INTERVAL_DEMAND

The Abstract Operational Demand for the worst interval in this region (MW)

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

This column is not mandatory.

Examples: 1053.72, 1344, 14507.24, 11324

WORST_INTERVAL_DSP

The half hourly aggregate demand side participation for the worst interval period in this region (MW)

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

This column is not mandatory.

Examples: 0.01, 0, 1, 157.85

WORST_INTERVAL_INTGEN

The half hourly aggregate intermittent generation for the worst interval in this region (MW)

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

This column is not mandatory.

Examples: 2.94, 48, 5235.37, 3906

WORST_INTERVAL_PERIODID

The half hourly interval period with the highest LOLP, or highest region demand if LOLP = 0 for all intervals (1..48)

SQL Type: NUMBER(3,0) Polars Type: pl.Int16

This column is not mandatory.

Examples: 1, 48

New Data Timing

On MTPASA solution published to the market (usually weekly)

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("DAY", "RUN_DATETIME", "LASTCHANGED").str.strptime(
            pl.Datetime, dt_s_fmt
        ),
    )
    .cast(
        {
            "REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
            "RUN_NO": int,  # or pl.Int16
            "RUNTYPE": pl.Categorical,
            "LOSSOFLOADMAGNITUDE": str,
            "LOSSOFLOADPROBABILITY": pl.Float64,
            "WORST_INTERVAL_DEMAND": pl.Float64,
            "WORST_INTERVAL_DSP": pl.Float64,
            "WORST_INTERVAL_INTGEN": pl.Float64,
            "WORST_INTERVAL_PERIODID": int,  # or pl.Int16
        }
    )
    # Deduplicate
    # We want one row per ['DAY', 'REGIONID', 'RUN_DATETIME', 'RUN_NO', 'RUNTYPE']
    # Tie-breaker: greatest value of LASTCHANGED
    .sort(LASTCHANGED, descending=False)
    .unique(
        subset=["DAY", "REGIONID", "RUN_DATETIME", "RUN_NO", "RUNTYPE"], keep="last"
    )
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_MTPASA_LOLPRESULT_FILE01_202609010000.zip

RUN_DATETIMERUN_NORUNTYPEDAYREGIONIDWORST_INTERVAL_PERIODIDWORST_INTERVAL_DEMANDWORST_INTERVAL_INTGENWORST_INTERVAL_DSPLOSSOFLOADPROBABILITYLOSSOFLOADMAGNITUDELASTCHANGED
2026/09/07 01:00:001177LOLP2026/09/13 00:00:00NSW13710949.82322.160.390Low2026/09/08 12:29:02
2026/09/07 01:00:001177LOLP2026/09/13 00:00:00QLD1378158.311067.8100Low2026/09/08 12:29:02
2026/09/07 01:00:001177LOLP2026/09/13 00:00:00SA1392076.89704.7300Low2026/09/08 12:29:02
2026/09/07 01:00:001177LOLP2026/09/13 00:00:00TAS1381532.848.6400Low2026/09/08 12:29:02
2026/09/07 01:00:001177LOLP2026/09/13 00:00:00VIC1377737.981065.6800Low2026/09/08 12:29:02
2026/09/07 01:00:001177LOLP2026/09/14 00:00:00NSW13710930.67536.714.450Low2026/09/08 12:29:02
2026/09/07 01:00:001177LOLP2026/09/14 00:00:00QLD1378673.641158.610.610Low2026/09/08 12:29:02
2026/09/07 01:00:001177LOLP2026/09/14 00:00:00SA1392147.21995.4800Low2026/09/08 12:29:02
2026/09/07 01:00:001177LOLP2026/09/14 00:00:00TAS1151522.9242.9400Low2026/09/08 12:29:02
2026/09/07 01:00:001177LOLP2026/09/14 00:00:00VIC1377718.981961.190.180Low2026/09/08 12:29:02
2026/09/07 01:00:001177LOLP2026/09/15 00:00:00NSW13710833.53357.684.090Low2026/09/08 12:29:02
...

CSETP.WORLDDVD_MTPASA_LOLPRESULTAEMOPUBLIC2026/10/0915:55:14001791525314348MONTHLY_ARCHIVE001791525314348
IMTPASALOLPRESULT1RUN_DATETIMERUN_NORUNTYPEDAYREGIONIDWORST_INTERVAL_PERIODIDWORST_INTERVAL_DEMANDWORST_INTERVAL_INTGENWORST_INTERVAL_DSPLOSSOFLOADPROBABILITYLOSSOFLOADMAGNITUDELASTCHANGED
DMTPASALOLPRESULT12026/09/07 01:00:001177LOLP2026/09/13 00:00:00NSW13710949.82322.160.390Low2026/09/08 12:29:02
DMTPASALOLPRESULT12026/09/07 01:00:001177LOLP2026/09/13 00:00:00QLD1378158.311067.8100Low2026/09/08 12:29:02
DMTPASALOLPRESULT12026/09/07 01:00:001177LOLP2026/09/13 00:00:00SA1392076.89704.7300Low2026/09/08 12:29:02
DMTPASALOLPRESULT12026/09/07 01:00:001177LOLP2026/09/13 00:00:00TAS1381532.848.6400Low2026/09/08 12:29:02
DMTPASALOLPRESULT12026/09/07 01:00:001177LOLP2026/09/13 00:00:00VIC1377737.981065.6800Low2026/09/08 12:29:02
DMTPASALOLPRESULT12026/09/07 01:00:001177LOLP2026/09/14 00:00:00NSW13710930.67536.714.450Low2026/09/08 12:29:02
DMTPASALOLPRESULT12026/09/07 01:00:001177LOLP2026/09/14 00:00:00QLD1378673.641158.610.610Low2026/09/08 12:29:02
DMTPASALOLPRESULT12026/09/07 01:00:001177LOLP2026/09/14 00:00:00SA1392147.21995.4800Low2026/09/08 12:29:02
DMTPASALOLPRESULT12026/09/07 01:00:001177LOLP2026/09/14 00:00:00TAS1151522.9242.9400Low2026/09/08 12:29:02
DMTPASALOLPRESULT12026/09/07 01:00:001177LOLP2026/09/14 00:00:00VIC1377718.981961.190.180Low2026/09/08 12:29:02
DMTPASALOLPRESULT12026/09/07 01:00:001177LOLP2026/09/15 00:00:00NSW13710833.53357.684.090Low2026/09/08 12:29:02
...
CEND OF REPORT1003

RUN_DATETIME,RUN_NO,RUNTYPE,DAY,REGIONID,WORST_INTERVAL_PERIODID,WORST_INTERVAL_DEMAND,WORST_INTERVAL_INTGEN,WORST_INTERVAL_DSP,LOSSOFLOADPROBABILITY,LOSSOFLOADMAGNITUDE,LASTCHANGED
"2026/09/07 01:00:00",1177,LOLP,"2026/09/13 00:00:00",NSW1,37,10949.82,322.16,0.39,0,Low,"2026/09/08 12:29:02"
"2026/09/07 01:00:00",1177,LOLP,"2026/09/13 00:00:00",QLD1,37,8158.31,1067.81,0,0,Low,"2026/09/08 12:29:02"
"2026/09/07 01:00:00",1177,LOLP,"2026/09/13 00:00:00",SA1,39,2076.89,704.73,0,0,Low,"2026/09/08 12:29:02"
"2026/09/07 01:00:00",1177,LOLP,"2026/09/13 00:00:00",TAS1,38,1532.84,8.64,0,0,Low,"2026/09/08 12:29:02"
"2026/09/07 01:00:00",1177,LOLP,"2026/09/13 00:00:00",VIC1,37,7737.98,1065.68,0,0,Low,"2026/09/08 12:29:02"
"2026/09/07 01:00:00",1177,LOLP,"2026/09/14 00:00:00",NSW1,37,10930.67,536.71,4.45,0,Low,"2026/09/08 12:29:02"
"2026/09/07 01:00:00",1177,LOLP,"2026/09/14 00:00:00",QLD1,37,8673.64,1158.61,0.61,0,Low,"2026/09/08 12:29:02"
"2026/09/07 01:00:00",1177,LOLP,"2026/09/14 00:00:00",SA1,39,2147.2,1995.48,0,0,Low,"2026/09/08 12:29:02"
"2026/09/07 01:00:00",1177,LOLP,"2026/09/14 00:00:00",TAS1,15,1522.9,242.94,0,0,Low,"2026/09/08 12:29:02"
"2026/09/07 01:00:00",1177,LOLP,"2026/09/14 00:00:00",VIC1,37,7718.98,1961.19,0.18,0,Low,"2026/09/08 12:29:02"
"2026/09/07 01:00:00",1177,LOLP,"2026/09/15 00:00:00",NSW1,37,10833.53,357.68,4.09,0,Low,"2026/09/08 12:29:02"
...

C,SETP.WORLD,DVD_MTPASA_LOLPRESULT,AEMO,PUBLIC,2026/10/09,15:55:14,001791525314348,MONTHLY_ARCHIVE,001791525314348
I,MTPASA,LOLPRESULT,1,RUN_DATETIME,RUN_NO,RUNTYPE,DAY,REGIONID,WORST_INTERVAL_PERIODID,WORST_INTERVAL_DEMAND,WORST_INTERVAL_INTGEN,WORST_INTERVAL_DSP,LOSSOFLOADPROBABILITY,LOSSOFLOADMAGNITUDE,LASTCHANGED
D,MTPASA,LOLPRESULT,1,"2026/09/07 01:00:00",1177,LOLP,"2026/09/13 00:00:00",NSW1,37,10949.82,322.16,0.39,0,Low,"2026/09/08 12:29:02"
D,MTPASA,LOLPRESULT,1,"2026/09/07 01:00:00",1177,LOLP,"2026/09/13 00:00:00",QLD1,37,8158.31,1067.81,0,0,Low,"2026/09/08 12:29:02"
D,MTPASA,LOLPRESULT,1,"2026/09/07 01:00:00",1177,LOLP,"2026/09/13 00:00:00",SA1,39,2076.89,704.73,0,0,Low,"2026/09/08 12:29:02"
D,MTPASA,LOLPRESULT,1,"2026/09/07 01:00:00",1177,LOLP,"2026/09/13 00:00:00",TAS1,38,1532.84,8.64,0,0,Low,"2026/09/08 12:29:02"
D,MTPASA,LOLPRESULT,1,"2026/09/07 01:00:00",1177,LOLP,"2026/09/13 00:00:00",VIC1,37,7737.98,1065.68,0,0,Low,"2026/09/08 12:29:02"
D,MTPASA,LOLPRESULT,1,"2026/09/07 01:00:00",1177,LOLP,"2026/09/14 00:00:00",NSW1,37,10930.67,536.71,4.45,0,Low,"2026/09/08 12:29:02"
D,MTPASA,LOLPRESULT,1,"2026/09/07 01:00:00",1177,LOLP,"2026/09/14 00:00:00",QLD1,37,8673.64,1158.61,0.61,0,Low,"2026/09/08 12:29:02"
D,MTPASA,LOLPRESULT,1,"2026/09/07 01:00:00",1177,LOLP,"2026/09/14 00:00:00",SA1,39,2147.2,1995.48,0,0,Low,"2026/09/08 12:29:02"
D,MTPASA,LOLPRESULT,1,"2026/09/07 01:00:00",1177,LOLP,"2026/09/14 00:00:00",TAS1,15,1522.9,242.94,0,0,Low,"2026/09/08 12:29:02"
D,MTPASA,LOLPRESULT,1,"2026/09/07 01:00:00",1177,LOLP,"2026/09/14 00:00:00",VIC1,37,7718.98,1961.19,0.18,0,Low,"2026/09/08 12:29:02"
D,MTPASA,LOLPRESULT,1,"2026/09/07 01:00:00",1177,LOLP,"2026/09/15 00:00:00",NSW1,37,10833.53,357.68,4.09,0,Low,"2026/09/08 12:29:02"
...
C,"END OF REPORT",1003

Availability

Visibility: Public

This table is in the public monthly MMSDM archive.

I have collated the list of all those files into one place, in this CSV metadata file. (Columns year, month, url, compressed_size in bytes).

The earliest available data is month 5 of 2018. The most recent available data is month 9 of 2026.

Every month in between has a file. (No gaps.)

Data Size

Each year of data is approximately 2 MB when zipped, 22 MB as unzipped CSV.

Deduplication and PDR Loader Configuration

The primary keys are:

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
MTPASA LOLPRESULT 1

This means that the CSV files start with metadata columns:

I,MTPASA,LOLPRESULT,1,...
D,MTPASA,LOLPRESULT,1,...

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: MTPASA