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MARKET_SUSPEND_SCHEDULE

Trading prices that will apply in the event of a market suspension event updated weekly.

MARKET_SUSPEND_SCHEDULE is public data, so is available to all participants.

Column Schema

DAY_TYPE

Distinguishes which record set to apply - at time of writing this was Business or Non-business day but may change in the future depending on outcome of consultation

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

This column is mandatory, and a primary key.

Examples:

  • BUS_DAY
  • NON_BUS_DAY

EFFECTIVEDATE

Calendar date from when this record set is effective

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

This column is mandatory, and a primary key.

Example: 2017/12/25 00:00:00

Note that AEMO represents dates as datetimes with a midnight time component.

PERIODID

48 intervals for a day, midnight base (equates to 00:30 - 00:00)

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

This column is mandatory, and a primary key.

Examples: 1, 48

REGIONID

Region affected.

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.

ENERGY_RRP

Energy Price applied for this period for this Day Type

SQL Type: NUMBER(15,5) Polars Type: pl.Float64

This column is not mandatory.

Examples: 27.2, 60, 1184.65, 106

L1_RRP

Lower 1Sec contingency Price applied for this period for this Day Type

SQL Type: NUMBER(15,5) Polars Type: pl.Float64

This column is not mandatory.

L5_RRP

Lower 5Min contingency Price applied for this period for this Day Type

SQL Type: NUMBER(15,5) Polars Type: pl.Float64

This column is not mandatory.

Examples: 0.04, 1, 1.9

L6_RRP

Lower 6Sec contingency Price applied for this period for this Day Type

SQL Type: NUMBER(15,5) Polars Type: pl.Float64

This column is not mandatory.

Examples: 0.01, 0, 10.7

L60_RRP

Lower 60Sec contingency Price applied for this period for this Day Type

SQL Type: NUMBER(15,5) Polars Type: pl.Float64

This column is not mandatory.

Examples: 0.01, 0, 2, 17.75

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: 2017/12/18 11:49:38

LREG_RRP

Lower Regulation Price applied for this period for this Day Type

SQL Type: NUMBER(15,5) Polars Type: pl.Float64

This column is not mandatory.

Examples: 6.77, 15, 58.52, 22

R1_RRP

Raise 1Sec contingency Price applied for this period for this Day Type

SQL Type: NUMBER(15,5) Polars Type: pl.Float64

This column is not mandatory.

R5_RRP

Raise 5Min contingency Price applied for this period for this Day Type

SQL Type: NUMBER(15,5) Polars Type: pl.Float64

This column is not mandatory.

Examples: 1.3, 2, 16.96, 12

R6_RRP

Raise 6Sec contingency Price applied for this period for this Day Type

SQL Type: NUMBER(15,5) Polars Type: pl.Float64

This column is not mandatory.

Examples: 0.99, 7, 390.08, 51

R60_RRP

Raise 60Sec contingency Price applied for this period for this Day Type

SQL Type: NUMBER(15,5) Polars Type: pl.Float64

This column is not mandatory.

Examples: 0.67, 1, 268.04, 7

RREG_RRP

Raise Regulation contingency Price applied for this period for this Day Type

SQL Type: NUMBER(15,5) Polars Type: pl.Float64

This column is not mandatory.

Examples: 10.71, 17, 132.85, 28

New Data Timing

Weekly and ad hoc

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"

# Dates are represented as datetimes at midnight

d_fmt = "%Y/%m/%d 00:00:00"

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("LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
        pl.col("EFFECTIVEDATE").str.strptime(pl.Date, d_fmt),
    )
    .cast(
        {
            "DAY_TYPE": pl.Categorical,
            "EFFECTIVEDATE": pl.Date(),
            "PERIODID": int,  # or pl.Int16
            "REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
            "ENERGY_RRP": pl.Float64,
            "L1_RRP": pl.Float64,
            "L5_RRP": pl.Float64,
            "L6_RRP": pl.Float64,
            "L60_RRP": pl.Float64,
            "LREG_RRP": pl.Float64,
            "R1_RRP": pl.Float64,
            "R5_RRP": pl.Float64,
            "R6_RRP": pl.Float64,
            "R60_RRP": pl.Float64,
            "RREG_RRP": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['DAY_TYPE', 'EFFECTIVEDATE', 'PERIODID', 'REGIONID']
    # Tie-breaker: greatest value of LASTCHANGED
    .sort(LASTCHANGED, descending=False)
    .unique(subset=["DAY_TYPE", "EFFECTIVEDATE", "PERIODID", "REGIONID"], keep="last")
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_MARKET_SUSPEND_SCHEDULE_FILE01_202609010000.zip

EFFECTIVEDATEDAY_TYPEREGIONIDPERIODIDENERGY_RRPR6_RRPR60_RRPR5_RRPRREG_RRPL6_RRPL60_RRPL5_RRPLREG_RRPLASTCHANGEDL1_RRPR1_RRP
2018/01/22 00:00:00BUS_DAYQLD12173.798.12.735.9823.9200.020.1617.262018/01/07 09:48:32
2018/01/22 00:00:00BUS_DAYQLD12277.511.824.566.6427.9500.020.1716.982018/01/07 09:48:32
2018/01/22 00:00:00BUS_DAYQLD12373.898.332.925.5226.6200.020.1818.12018/01/07 09:48:32
2018/01/22 00:00:00BUS_DAYQLD12478.499.243.397.7328.2700.020.1517.22018/01/07 09:48:32
2018/01/22 00:00:00BUS_DAYQLD12579.59.943.657.5228.8100.020.1717.72018/01/07 09:48:32
2018/01/22 00:00:00BUS_DAYQLD12681.8310.364.896.4831.3200.020.1516.942018/01/07 09:48:32
2018/01/22 00:00:00BUS_DAYQLD12787.1310.294.658.2834.4500.020.1517.252018/01/07 09:48:32
2018/01/22 00:00:00BUS_DAYQLD12886.412.185.099.9134.6500.020.1517.482018/01/07 09:48:32
2018/01/22 00:00:00BUS_DAYQLD1298511.025.451238.4700.020.1417.022018/01/07 09:48:32
2018/01/22 00:00:00BUS_DAYQLD13086.2710.033.9712.8542.3700.020.1417.342018/01/07 09:48:32
2018/01/22 00:00:00BUS_DAYQLD13195.5110.363.6810.3444.9200.010.1316.522018/01/07 09:48:32
...

CSETP.WORLDDVD_MARKET_SUSPEND_SCHEDULEAEMOPUBLIC2026/10/0915:53:07001791525187848MONTHLY_ARCHIVE001791525187848
IFORCE_MAJEUREMARKET_SUSPEND_SCHEDULE2EFFECTIVEDATEDAY_TYPEREGIONIDPERIODIDENERGY_RRPR6_RRPR60_RRPR5_RRPRREG_RRPL6_RRPL60_RRPL5_RRPLREG_RRPLASTCHANGEDL1_RRPR1_RRP
DFORCE_MAJEUREMARKET_SUSPEND_SCHEDULE22018/01/22 00:00:00BUS_DAYQLD12173.798.12.735.9823.9200.020.1617.262018/01/07 09:48:32
DFORCE_MAJEUREMARKET_SUSPEND_SCHEDULE22018/01/22 00:00:00BUS_DAYQLD12277.511.824.566.6427.9500.020.1716.982018/01/07 09:48:32
DFORCE_MAJEUREMARKET_SUSPEND_SCHEDULE22018/01/22 00:00:00BUS_DAYQLD12373.898.332.925.5226.6200.020.1818.12018/01/07 09:48:32
DFORCE_MAJEUREMARKET_SUSPEND_SCHEDULE22018/01/22 00:00:00BUS_DAYQLD12478.499.243.397.7328.2700.020.1517.22018/01/07 09:48:32
DFORCE_MAJEUREMARKET_SUSPEND_SCHEDULE22018/01/22 00:00:00BUS_DAYQLD12579.59.943.657.5228.8100.020.1717.72018/01/07 09:48:32
DFORCE_MAJEUREMARKET_SUSPEND_SCHEDULE22018/01/22 00:00:00BUS_DAYQLD12681.8310.364.896.4831.3200.020.1516.942018/01/07 09:48:32
DFORCE_MAJEUREMARKET_SUSPEND_SCHEDULE22018/01/22 00:00:00BUS_DAYQLD12787.1310.294.658.2834.4500.020.1517.252018/01/07 09:48:32
DFORCE_MAJEUREMARKET_SUSPEND_SCHEDULE22018/01/22 00:00:00BUS_DAYQLD12886.412.185.099.9134.6500.020.1517.482018/01/07 09:48:32
DFORCE_MAJEUREMARKET_SUSPEND_SCHEDULE22018/01/22 00:00:00BUS_DAYQLD1298511.025.451238.4700.020.1417.022018/01/07 09:48:32
DFORCE_MAJEUREMARKET_SUSPEND_SCHEDULE22018/01/22 00:00:00BUS_DAYQLD13086.2710.033.9712.8542.3700.020.1417.342018/01/07 09:48:32
DFORCE_MAJEUREMARKET_SUSPEND_SCHEDULE22018/01/22 00:00:00BUS_DAYQLD13195.5110.363.6810.3444.9200.010.1316.522018/01/07 09:48:32
...
CEND OF REPORT1003

EFFECTIVEDATE,DAY_TYPE,REGIONID,PERIODID,ENERGY_RRP,R6_RRP,R60_RRP,R5_RRP,RREG_RRP,L6_RRP,L60_RRP,L5_RRP,LREG_RRP,LASTCHANGED,L1_RRP,R1_RRP
"2018/01/22 00:00:00",BUS_DAY,QLD1,21,73.79,8.1,2.73,5.98,23.92,0,0.02,0.16,17.26,"2018/01/07 09:48:32",,
"2018/01/22 00:00:00",BUS_DAY,QLD1,22,77.5,11.82,4.56,6.64,27.95,0,0.02,0.17,16.98,"2018/01/07 09:48:32",,
"2018/01/22 00:00:00",BUS_DAY,QLD1,23,73.89,8.33,2.92,5.52,26.62,0,0.02,0.18,18.1,"2018/01/07 09:48:32",,
"2018/01/22 00:00:00",BUS_DAY,QLD1,24,78.49,9.24,3.39,7.73,28.27,0,0.02,0.15,17.2,"2018/01/07 09:48:32",,
"2018/01/22 00:00:00",BUS_DAY,QLD1,25,79.5,9.94,3.65,7.52,28.81,0,0.02,0.17,17.7,"2018/01/07 09:48:32",,
"2018/01/22 00:00:00",BUS_DAY,QLD1,26,81.83,10.36,4.89,6.48,31.32,0,0.02,0.15,16.94,"2018/01/07 09:48:32",,
"2018/01/22 00:00:00",BUS_DAY,QLD1,27,87.13,10.29,4.65,8.28,34.45,0,0.02,0.15,17.25,"2018/01/07 09:48:32",,
"2018/01/22 00:00:00",BUS_DAY,QLD1,28,86.4,12.18,5.09,9.91,34.65,0,0.02,0.15,17.48,"2018/01/07 09:48:32",,
"2018/01/22 00:00:00",BUS_DAY,QLD1,29,85,11.02,5.45,12,38.47,0,0.02,0.14,17.02,"2018/01/07 09:48:32",,
"2018/01/22 00:00:00",BUS_DAY,QLD1,30,86.27,10.03,3.97,12.85,42.37,0,0.02,0.14,17.34,"2018/01/07 09:48:32",,
"2018/01/22 00:00:00",BUS_DAY,QLD1,31,95.51,10.36,3.68,10.34,44.92,0,0.01,0.13,16.52,"2018/01/07 09:48:32",,
...

C,SETP.WORLD,DVD_MARKET_SUSPEND_SCHEDULE,AEMO,PUBLIC,2026/10/09,15:53:07,001791525187848,MONTHLY_ARCHIVE,001791525187848
I,FORCE_MAJEURE,MARKET_SUSPEND_SCHEDULE,2,EFFECTIVEDATE,DAY_TYPE,REGIONID,PERIODID,ENERGY_RRP,R6_RRP,R60_RRP,R5_RRP,RREG_RRP,L6_RRP,L60_RRP,L5_RRP,LREG_RRP,LASTCHANGED,L1_RRP,R1_RRP
D,FORCE_MAJEURE,MARKET_SUSPEND_SCHEDULE,2,"2018/01/22 00:00:00",BUS_DAY,QLD1,21,73.79,8.1,2.73,5.98,23.92,0,0.02,0.16,17.26,"2018/01/07 09:48:32",,
D,FORCE_MAJEURE,MARKET_SUSPEND_SCHEDULE,2,"2018/01/22 00:00:00",BUS_DAY,QLD1,22,77.5,11.82,4.56,6.64,27.95,0,0.02,0.17,16.98,"2018/01/07 09:48:32",,
D,FORCE_MAJEURE,MARKET_SUSPEND_SCHEDULE,2,"2018/01/22 00:00:00",BUS_DAY,QLD1,23,73.89,8.33,2.92,5.52,26.62,0,0.02,0.18,18.1,"2018/01/07 09:48:32",,
D,FORCE_MAJEURE,MARKET_SUSPEND_SCHEDULE,2,"2018/01/22 00:00:00",BUS_DAY,QLD1,24,78.49,9.24,3.39,7.73,28.27,0,0.02,0.15,17.2,"2018/01/07 09:48:32",,
D,FORCE_MAJEURE,MARKET_SUSPEND_SCHEDULE,2,"2018/01/22 00:00:00",BUS_DAY,QLD1,25,79.5,9.94,3.65,7.52,28.81,0,0.02,0.17,17.7,"2018/01/07 09:48:32",,
D,FORCE_MAJEURE,MARKET_SUSPEND_SCHEDULE,2,"2018/01/22 00:00:00",BUS_DAY,QLD1,26,81.83,10.36,4.89,6.48,31.32,0,0.02,0.15,16.94,"2018/01/07 09:48:32",,
D,FORCE_MAJEURE,MARKET_SUSPEND_SCHEDULE,2,"2018/01/22 00:00:00",BUS_DAY,QLD1,27,87.13,10.29,4.65,8.28,34.45,0,0.02,0.15,17.25,"2018/01/07 09:48:32",,
D,FORCE_MAJEURE,MARKET_SUSPEND_SCHEDULE,2,"2018/01/22 00:00:00",BUS_DAY,QLD1,28,86.4,12.18,5.09,9.91,34.65,0,0.02,0.15,17.48,"2018/01/07 09:48:32",,
D,FORCE_MAJEURE,MARKET_SUSPEND_SCHEDULE,2,"2018/01/22 00:00:00",BUS_DAY,QLD1,29,85,11.02,5.45,12,38.47,0,0.02,0.14,17.02,"2018/01/07 09:48:32",,
D,FORCE_MAJEURE,MARKET_SUSPEND_SCHEDULE,2,"2018/01/22 00:00:00",BUS_DAY,QLD1,30,86.27,10.03,3.97,12.85,42.37,0,0.02,0.14,17.34,"2018/01/07 09:48:32",,
D,FORCE_MAJEURE,MARKET_SUSPEND_SCHEDULE,2,"2018/01/22 00:00:00",BUS_DAY,QLD1,31,95.51,10.36,3.68,10.34,44.92,0,0.01,0.13,16.52,"2018/01/07 09:48:32",,
...
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 8 of 2024. 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 54 MB when zipped, 541 MB as unzipped CSV.

Deduplication and PDR Loader Configuration

The primary keys are:

The transaction type is "INSERT-UPDATE", which means that you will likely see duplicate data with different values.

You should deduplicate by sorting based on LASTCHANGED, within each group of primary keys.

PDR Loader configuration is:

Report Name Report Subtype Version
FORCE_MAJEURE MARKET_SUSPEND_SCHEDULE 1
FORCE_MAJEURE MARKET_SUSPEND_SCHEDULE 2

This means that the CSV files start with metadata columns:

I,FORCE_MAJEURE,MARKET_SUSPEND_SCHEDULE,1,...
D,FORCE_MAJEURE,MARKET_SUSPEND_SCHEDULE,1,...

or

I,FORCE_MAJEURE,MARKET_SUSPEND_SCHEDULE,2,...
D,FORCE_MAJEURE,MARKET_SUSPEND_SCHEDULE,2,...

ROW_FILTER_TYPE is LASTCHANGED

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: FORCE_MAJEURE