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P5MIN_CASESOLUTION

The five-minute predispatch (P5Min) is a MMS system providing projected dispatch for 12 Dispatch cycles (one hour). The 5-minute Predispatch cycle runs every 5-minutes to produce a dispatch and pricing schedule to a 5-minute resolution covering the next hour, a total of twelve periods. P5MIN_CASESOLUTION shows one record containing results pertaining to the entire solution.

P5MIN_CASESOLUTION data is public, so is available to all participants.

Source

P5MIN_CASESOLUTION updates every 5 minutes.

Volume

Rows per day: 288

Column Schema

RUN_DATETIME

Unique Timestamp Identifier for this study

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

This column is mandatory, and a primary key.

Example: 2026/09/01 00:05:00

INTERVENTION

Flag to indicate if this Predispatch case includes an intervention pricing run: 0 = case does not include an intervention pricing run, 1 = case does include an intervention pricing run. This field has a default value of 0 and is not nullable

Warning

Unlike most tables with an INTERVENTION column, INTERVENTION is not a primary key of P5MIN_CASESOLUTION.

For more information, see the dedicated documentation page.

SQL Type: NUMBER(2,0) Polars Type: pl.Boolean

This column is not mandatory.

Example: 0

LASTCHANGED

Last changed date and time of this record

For more information, see the dedicated documentation page.

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

This column is not mandatory.

Example: 2026/09/01 00:00:33

NONPHYSICALLOSSES

Flag to indicate non-physical losses occurred in this study

SQL Type: NUMBER(1,0) Polars Type: pl.Boolean

This column is not mandatory.

Example: 0

STARTINTERVAL_DATETIME

Date and Time of first interval in study

SQL Type: VARCHAR2(20) Polars Type: str

This column is not mandatory.

Example: 2026/09/01 00:05:00

TOTAL5MINVIOLATION

Sum of regional 5 min FCAS violations

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

This column is not mandatory.

TOTAL6SECVIOLATION

Sum of regional 6 sec FCAS violations

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

This column is not mandatory.

TOTAL60SECVIOLATION

Sum of regional 60 sec FCAS violations

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

This column is not mandatory.

TOTALAREAGENVIOLATION

Sum of Regional Energy balance violations

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

This column is not mandatory.

Example: 0

TOTALASPROFILEVIOLATION

Sum of unit FCAS profile offer violations

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

This column is not mandatory.

Example: 0

TOTALENERGYCONSTRVIOLATION

Sum of unit energy constrained violations

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

This column is not mandatory.

Example: 0

TOTALENERGYOFFERVIOLATION

Sum of unit offer violations

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

This column is not mandatory.

Example: 0

TOTALFASTSTARTVIOLATION

Sum of unit Fast start profile violations

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

This column is not mandatory.

Example: 0

TOTALGENERICVIOLATION

Sum of Generic Constraint violations

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

This column is not mandatory.

Examples: 0.001, 0, 2, 360

TOTALINTERCONNECTORVIOLATION

Sum of Interconnector violations of standing data limits

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

This column is not mandatory.

Example: 0

TOTALOBJECTIVE

The Objective function from the LP

SQL Type: NUMBER(27,10) Polars Type: pl.Float64

This column is not mandatory.

Examples: -6205670.672, 1855962.533, -904098896.997, 97640236986.66

TOTALRAMPRATEVIOLATION

Sum of Unit Ramp Rate violations

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

This column is not mandatory.

Examples: 0.025, 0, 0.79

TOTALREGVIOLATION

Sum of regional regulation FCAS violations

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

This column is not mandatory.

TOTALUNITMWCAPACITYVIOLATION

Sum of unit capacity violations

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

This column is not mandatory.

Examples: 0.36, 0, 2, 580

New Data Timing

Every 5 minutes

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("RUN_DATETIME", "LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
    )
    # Polars can't cast string straight to bool. Need to go via something else.
    .cast({"INTERVENTION": pl.UInt8, "NONPHYSICALLOSSES": pl.UInt8})
    .cast(
        {
            "INTERVENTION": pl.Boolean,
            "NONPHYSICALLOSSES": pl.Boolean,
            "STARTINTERVAL_DATETIME": str,
            "TOTAL5MINVIOLATION": pl.Float64,
            "TOTAL6SECVIOLATION": pl.Float64,
            "TOTAL60SECVIOLATION": pl.Float64,
            "TOTALAREAGENVIOLATION": pl.Float64,
            "TOTALASPROFILEVIOLATION": pl.Float64,
            "TOTALENERGYCONSTRVIOLATION": pl.Float64,
            "TOTALENERGYOFFERVIOLATION": pl.Float64,
            "TOTALFASTSTARTVIOLATION": pl.Float64,
            "TOTALGENERICVIOLATION": pl.Float64,
            "TOTALINTERCONNECTORVIOLATION": pl.Float64,
            "TOTALOBJECTIVE": pl.Float64,
            "TOTALRAMPRATEVIOLATION": pl.Float64,
            "TOTALREGVIOLATION": pl.Float64,
            "TOTALUNITMWCAPACITYVIOLATION": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['RUN_DATETIME']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(subset=["RUN_DATETIME"], keep="last")
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_P5MIN_CASESOLUTION_FILE01_202609010000.zip

RUN_DATETIMESTARTINTERVAL_DATETIMETOTALOBJECTIVENONPHYSICALLOSSESTOTALAREAGENVIOLATIONTOTALINTERCONNECTORVIOLATIONTOTALGENERICVIOLATIONTOTALRAMPRATEVIOLATIONTOTALUNITMWCAPACITYVIOLATIONTOTAL5MINVIOLATIONTOTALREGVIOLATIONTOTAL6SECVIOLATIONTOTAL60SECVIOLATIONTOTALENERGYCONSTRVIOLATIONTOTALENERGYOFFERVIOLATIONTOTALASPROFILEVIOLATIONTOTALFASTSTARTVIOLATIONLASTCHANGEDINTERVENTION
2026/09/01 00:05:002026/09/01 00:05:001940333302.288000004500002026/09/01 00:00:330
2026/09/01 00:10:002026/09/01 00:10:001979362552.6030000048.600002026/09/01 00:05:320
2026/09/01 00:15:002026/09/01 00:15:002697956509.2840000047.400002026/09/01 00:10:330
2026/09/01 00:20:002026/09/01 00:20:002293084192.0790000046.200002026/09/01 00:15:320
2026/09/01 00:25:002026/09/01 00:25:002094035405.1010000048.600002026/09/01 00:20:320
2026/09/01 00:30:002026/09/01 00:30:002165072658.338000004500002026/09/01 00:25:320
2026/09/01 00:35:002026/09/01 00:35:001849669839.726000003500002026/09/01 00:30:330
2026/09/01 00:40:002026/09/01 00:40:001742590001.128000001400002026/09/01 00:35:320
2026/09/01 00:45:002026/09/01 00:45:00697719971.67800000000002026/09/01 00:40:330
2026/09/01 00:50:002026/09/01 00:50:00740197569.194000003.600002026/09/01 00:45:320
2026/09/01 00:55:002026/09/01 00:55:00571869643.08300000000002026/09/01 00:50:320
...

CSETP.WORLDDVD_P5MIN_CASESOLUTIONAEMOPUBLIC2026/10/0915:57:20001791525440952MONTHLY_ARCHIVE001791525440952
IP5MINCASESOLUTION2RUN_DATETIMESTARTINTERVAL_DATETIMETOTALOBJECTIVENONPHYSICALLOSSESTOTALAREAGENVIOLATIONTOTALINTERCONNECTORVIOLATIONTOTALGENERICVIOLATIONTOTALRAMPRATEVIOLATIONTOTALUNITMWCAPACITYVIOLATIONTOTAL5MINVIOLATIONTOTALREGVIOLATIONTOTAL6SECVIOLATIONTOTAL60SECVIOLATIONTOTALENERGYCONSTRVIOLATIONTOTALENERGYOFFERVIOLATIONTOTALASPROFILEVIOLATIONTOTALFASTSTARTVIOLATIONLASTCHANGEDINTERVENTION
DP5MINCASESOLUTION22026/09/01 00:05:002026/09/01 00:05:001940333302.288000004500002026/09/01 00:00:330
DP5MINCASESOLUTION22026/09/01 00:10:002026/09/01 00:10:001979362552.6030000048.600002026/09/01 00:05:320
DP5MINCASESOLUTION22026/09/01 00:15:002026/09/01 00:15:002697956509.2840000047.400002026/09/01 00:10:330
DP5MINCASESOLUTION22026/09/01 00:20:002026/09/01 00:20:002293084192.0790000046.200002026/09/01 00:15:320
DP5MINCASESOLUTION22026/09/01 00:25:002026/09/01 00:25:002094035405.1010000048.600002026/09/01 00:20:320
DP5MINCASESOLUTION22026/09/01 00:30:002026/09/01 00:30:002165072658.338000004500002026/09/01 00:25:320
DP5MINCASESOLUTION22026/09/01 00:35:002026/09/01 00:35:001849669839.726000003500002026/09/01 00:30:330
DP5MINCASESOLUTION22026/09/01 00:40:002026/09/01 00:40:001742590001.128000001400002026/09/01 00:35:320
DP5MINCASESOLUTION22026/09/01 00:45:002026/09/01 00:45:00697719971.67800000000002026/09/01 00:40:330
DP5MINCASESOLUTION22026/09/01 00:50:002026/09/01 00:50:00740197569.194000003.600002026/09/01 00:45:320
DP5MINCASESOLUTION22026/09/01 00:55:002026/09/01 00:55:00571869643.08300000000002026/09/01 00:50:320
...
CEND OF REPORT1003

RUN_DATETIME,STARTINTERVAL_DATETIME,TOTALOBJECTIVE,NONPHYSICALLOSSES,TOTALAREAGENVIOLATION,TOTALINTERCONNECTORVIOLATION,TOTALGENERICVIOLATION,TOTALRAMPRATEVIOLATION,TOTALUNITMWCAPACITYVIOLATION,TOTAL5MINVIOLATION,TOTALREGVIOLATION,TOTAL6SECVIOLATION,TOTAL60SECVIOLATION,TOTALENERGYCONSTRVIOLATION,TOTALENERGYOFFERVIOLATION,TOTALASPROFILEVIOLATION,TOTALFASTSTARTVIOLATION,LASTCHANGED,INTERVENTION
"2026/09/01 00:05:00","2026/09/01 00:05:00",1940333302.288,0,0,0,0,0,45,,,,,0,0,0,0,"2026/09/01 00:00:33",0
"2026/09/01 00:10:00","2026/09/01 00:10:00",1979362552.603,0,0,0,0,0,48.6,,,,,0,0,0,0,"2026/09/01 00:05:32",0
"2026/09/01 00:15:00","2026/09/01 00:15:00",2697956509.284,0,0,0,0,0,47.4,,,,,0,0,0,0,"2026/09/01 00:10:33",0
"2026/09/01 00:20:00","2026/09/01 00:20:00",2293084192.079,0,0,0,0,0,46.2,,,,,0,0,0,0,"2026/09/01 00:15:32",0
"2026/09/01 00:25:00","2026/09/01 00:25:00",2094035405.101,0,0,0,0,0,48.6,,,,,0,0,0,0,"2026/09/01 00:20:32",0
"2026/09/01 00:30:00","2026/09/01 00:30:00",2165072658.338,0,0,0,0,0,45,,,,,0,0,0,0,"2026/09/01 00:25:32",0
"2026/09/01 00:35:00","2026/09/01 00:35:00",1849669839.726,0,0,0,0,0,35,,,,,0,0,0,0,"2026/09/01 00:30:33",0
"2026/09/01 00:40:00","2026/09/01 00:40:00",1742590001.128,0,0,0,0,0,14,,,,,0,0,0,0,"2026/09/01 00:35:32",0
"2026/09/01 00:45:00","2026/09/01 00:45:00",697719971.678,0,0,0,0,0,0,,,,,0,0,0,0,"2026/09/01 00:40:33",0
"2026/09/01 00:50:00","2026/09/01 00:50:00",740197569.194,0,0,0,0,0,3.6,,,,,0,0,0,0,"2026/09/01 00:45:32",0
"2026/09/01 00:55:00","2026/09/01 00:55:00",571869643.083,0,0,0,0,0,0,,,,,0,0,0,0,"2026/09/01 00:50:32",0
...

C,SETP.WORLD,DVD_P5MIN_CASESOLUTION,AEMO,PUBLIC,2026/10/09,15:57:20,001791525440952,MONTHLY_ARCHIVE,001791525440952
I,P5MIN,CASESOLUTION,2,RUN_DATETIME,STARTINTERVAL_DATETIME,TOTALOBJECTIVE,NONPHYSICALLOSSES,TOTALAREAGENVIOLATION,TOTALINTERCONNECTORVIOLATION,TOTALGENERICVIOLATION,TOTALRAMPRATEVIOLATION,TOTALUNITMWCAPACITYVIOLATION,TOTAL5MINVIOLATION,TOTALREGVIOLATION,TOTAL6SECVIOLATION,TOTAL60SECVIOLATION,TOTALENERGYCONSTRVIOLATION,TOTALENERGYOFFERVIOLATION,TOTALASPROFILEVIOLATION,TOTALFASTSTARTVIOLATION,LASTCHANGED,INTERVENTION
D,P5MIN,CASESOLUTION,2,"2026/09/01 00:05:00","2026/09/01 00:05:00",1940333302.288,0,0,0,0,0,45,,,,,0,0,0,0,"2026/09/01 00:00:33",0
D,P5MIN,CASESOLUTION,2,"2026/09/01 00:10:00","2026/09/01 00:10:00",1979362552.603,0,0,0,0,0,48.6,,,,,0,0,0,0,"2026/09/01 00:05:32",0
D,P5MIN,CASESOLUTION,2,"2026/09/01 00:15:00","2026/09/01 00:15:00",2697956509.284,0,0,0,0,0,47.4,,,,,0,0,0,0,"2026/09/01 00:10:33",0
D,P5MIN,CASESOLUTION,2,"2026/09/01 00:20:00","2026/09/01 00:20:00",2293084192.079,0,0,0,0,0,46.2,,,,,0,0,0,0,"2026/09/01 00:15:32",0
D,P5MIN,CASESOLUTION,2,"2026/09/01 00:25:00","2026/09/01 00:25:00",2094035405.101,0,0,0,0,0,48.6,,,,,0,0,0,0,"2026/09/01 00:20:32",0
D,P5MIN,CASESOLUTION,2,"2026/09/01 00:30:00","2026/09/01 00:30:00",2165072658.338,0,0,0,0,0,45,,,,,0,0,0,0,"2026/09/01 00:25:32",0
D,P5MIN,CASESOLUTION,2,"2026/09/01 00:35:00","2026/09/01 00:35:00",1849669839.726,0,0,0,0,0,35,,,,,0,0,0,0,"2026/09/01 00:30:33",0
D,P5MIN,CASESOLUTION,2,"2026/09/01 00:40:00","2026/09/01 00:40:00",1742590001.128,0,0,0,0,0,14,,,,,0,0,0,0,"2026/09/01 00:35:32",0
D,P5MIN,CASESOLUTION,2,"2026/09/01 00:45:00","2026/09/01 00:45:00",697719971.678,0,0,0,0,0,0,,,,,0,0,0,0,"2026/09/01 00:40:33",0
D,P5MIN,CASESOLUTION,2,"2026/09/01 00:50:00","2026/09/01 00:50:00",740197569.194,0,0,0,0,0,3.6,,,,,0,0,0,0,"2026/09/01 00:45:32",0
D,P5MIN,CASESOLUTION,2,"2026/09/01 00:55:00","2026/09/01 00:55:00",571869643.083,0,0,0,0,0,0,,,,,0,0,0,0,"2026/09/01 00:50:32",0
...
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 1 of 2015. 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, 23 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
P5MIN CASESOLUTION 2

This means that the CSV files start with metadata columns:

I,P5MIN,CASESOLUTION,2,...
D,P5MIN,CASESOLUTION,2,...

Indexes

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: P5MIN