P5MIN_CONSTRAINTSOLUTION

The Five-Minute Pre-Dispatch (P5Min) is a MMS system providing projected dispatch for 12 Dispatch cycles (one hour). The Five-Minute Pre-dispatch 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_CONSTRAINTSOLUTION shows binding and violated constraint results from the capacity evaluation, including the RHS value.

P5MIN_CONSTRAINTSOLUTION is public data, so is available to all participants.

Source

P5MIN_CONSTRAINTSOLUTION updates every five minutes.

Volume

Rows per day: ~2.3 million

Column Schema

CONSTRAINTID

Constraint identifier (synonymous with GenConID)

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

This column is mandatory, and a primary key.

Examples:

DATASNAP_DFS_QSTHE
DATASNAP_DFS_QSTHW
DATASNAP_DFS_Q_BCCP
DATASNAP_DFS_Q_BCRG
DATASNAP_DFS_Q_CLST
DATASNAP_DFS_Q_CLWU
DATASNAP_DFS_Q_LCCP
DATASNAP_DFS_Q_RGLC
DATASNAP_DFS_Q_TAMR
DATASNAP_DFS_Q_TAMR2

INTERVAL_DATETIME

The unique identifier for the interval within this study

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

This column is mandatory, and a primary key.

Example: 2026/09/24 00:25:00

RUN_DATETIME

Unique Timestamp Identifier for this study

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

This column is mandatory, and a primary key.

Example: 2026/09/24 00:25:00

DUID

DUID to which the Constraint is confidential. Null denotes non-confidential

Warning

This column may contain funny characters such as / (slash) or # (hash). This may break hive-style partitioning.

Warning

In the CSV files, this column actually has header CONFIDENTIAL_TO, not DUID. In a SQL database, this should appear as DUID

For more information, see the dedicated documentation page.

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

This column is not mandatory.

Example: W/HOE#2

GENCONID_EFFECTIVEDATE

Effective date of the Generic Constraint (ConstraintID). This field is used to track the version of this generic constraint applied in this dispatch interval

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

This column is not mandatory.

Example: 2013/08/21 00:00:00

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

GENCONID_VERSIONNO

Version number of the Generic Constraint (ConstraintID). This field is used to track the version of this generic constraint applied in this dispatch interval

SQL Type: NUMBER(22,0) Polars Type: pl.Int128

This column is not mandatory.

Examples: 1, 2

INTERVENTION

Flag to indicate if this result set was sourced from the pricing run (INTERVENTION=0) or the physical run(INTERVENTION=1). In the event there is not intervention in the market, both pricing and physical runs correspond to INTERVENTION=0)

Warning

Unlike most tables with an INTERVENTION column, INTERVENTION is not a primary key of P5MIN_CONSTRAINTSOLUTION.

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 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: 2026/09/24 00:20:31

LHS

Aggregation of the constraints LHS term solution values

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

This column is not mandatory.

Examples: -0.45381, 5.21261, 170, 1276.87673

MARGINALVALUE

Marginal cost of constraint (>0 if binding)

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

This column is not mandatory.

Example: 0

RHS

Right Hand Side value in the capacity evaluation

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

This column is not mandatory.

Examples: -0.00032, 20.49098, -630, 10732

VIOLATIONDEGREE

Amount of Violation (>0 if violating)

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

This column is not mandatory.

Example: 0

New Data Timing

Every 5 minutes

Code Examples

Polars

This table is so large that you should explicitly enumerate and cast every column, which will speed things up and prevents some errors. Although some of the datatypes are inferred, so they may be wrong. In particular, UInt16 vs UInt8 vs Int16 etc. If you get errors, you can swap these for just int.

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",
    )
    # These columns have names in the CSV which do not match the official schema
    .rename({"CONFIDENTIAL_TO": "DUID"})
    .with_columns(
        pl.col("INTERVAL_DATETIME", "RUN_DATETIME", "LASTCHANGED").str.strptime(
            pl.Datetime, dt_s_fmt
        ),
        pl.col("GENCONID_EFFECTIVEDATE").str.strptime(pl.Date, d_fmt),
    )
    # Polars can't cast string straight to bool. Need to go via something else.
    .cast({"INTERVENTION": pl.UInt8})
    .cast(
        {
            "CONSTRAINTID": pl.Categorical,
            "DUID": pl.Categorical,
            "GENCONID_EFFECTIVEDATE": pl.Date(),
            "GENCONID_VERSIONNO": pl.Int128,
            "INTERVENTION": pl.Boolean,
            "LHS": pl.Float64,
            "MARGINALVALUE": pl.Float64,
            "RHS": pl.Float64,
            "VIOLATIONDEGREE": pl.Float64,
        }
    )
    # Deduplicate
    # These files are so large that this step may cause your laptop to run out of memory.
    # We want one row per ['CONSTRAINTID', 'INTERVAL_DATETIME', 'RUN_DATETIME']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(subset=["CONSTRAINTID", "INTERVAL_DATETIME", "RUN_DATETIME"], keep="last")
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_P5MIN_CONSTRAINTSOLUTION_FILE03_202609010000.zip

Table without MetadataTable with MetadataRaw Text without Metadata

RUN_DATETIME	INTERVAL_DATETIME	CONSTRAINTID	RHS	MARGINALVALUE	VIOLATIONDEGREE	LASTCHANGED	DUID	GENCONID_EFFECTIVEDATE	GENCONID_VERSIONNO	LHS	INTERVENTION
2026/09/24 00:25:00	2026/09/24 01:05:00	F_MAIN+TTS_TG_R6	-10193.5464	0	0	2026/09/24 00:20:31		2020/12/04 00:00:00	1	633.22248	0
2026/09/24 00:25:00	2026/09/24 01:10:00	F_MAIN+TTS_TG_R6	-10192.8964	0	0	2026/09/24 00:20:31		2020/12/04 00:00:00	1	633.54748	0
2026/09/24 00:25:00	2026/09/24 01:15:00	F_MAIN+TTS_TG_R6	-10192.2364	0	0	2026/09/24 00:20:31		2020/12/04 00:00:00	1	633.87748	0
2026/09/24 00:25:00	2026/09/24 01:20:00	F_MAIN+TTS_TG_R6	-10191.5264	0	0	2026/09/24 00:20:31		2020/12/04 00:00:00	1	634.23248	0
2026/09/24 00:25:00	2026/09/24 00:25:00	F_MAIN+TTS_TG_R60	-10099.5582	0	0	2026/09/24 00:20:31		2020/12/04 00:00:00	1	630.68547	0
2026/09/24 00:25:00	2026/09/24 00:30:00	F_MAIN+TTS_TG_R60	-10099.2182	0	0	2026/09/24 00:20:31		2020/12/04 00:00:00	1	630.77748	0
2026/09/24 00:25:00	2026/09/24 00:40:00	F_MAIN+TTS_TG_R60	-10098.5682	0	0	2026/09/24 00:20:31		2020/12/04 00:00:00	1	631.42748	0
2026/09/24 00:25:00	2026/09/24 00:45:00	F_MAIN+TTS_TG_R60	-10098.2682	0	0	2026/09/24 00:20:31		2020/12/04 00:00:00	1	631.72748	0
2026/09/24 00:25:00	2026/09/24 00:50:00	F_MAIN+TTS_TG_R60	-10097.9232	0	0	2026/09/24 00:20:31		2020/12/04 00:00:00	1	632.07248	0
2026/09/24 00:25:00	2026/09/24 00:55:00	F_MAIN+TTS_TG_R60	-10097.5582	0	0	2026/09/24 00:20:31		2020/12/04 00:00:00	1	632.43748	0
2026/09/24 00:25:00	2026/09/24 01:00:00	F_MAIN+TTS_TG_R60	-10097.1782	0	0	2026/09/24 00:20:31		2020/12/04 00:00:00	1	632.81748	0
...

C	SETP.WORLD	DVD_P5MIN_CONSTRAINTSOLUTION	AEMO	PUBLIC	2026/10/09	17:12:33	001791529953559	MONTHLY_ARCHIVE	001791529953559
I	P5MIN	CONSTRAINTSOLUTION	6	RUN_DATETIME	INTERVAL_DATETIME	CONSTRAINTID	RHS	MARGINALVALUE	VIOLATIONDEGREE	LASTCHANGED	DUID	GENCONID_EFFECTIVEDATE	GENCONID_VERSIONNO	LHS	INTERVENTION
D	P5MIN	CONSTRAINTSOLUTION	6	2026/09/24 00:25:00	2026/09/24 01:05:00	F_MAIN+TTS_TG_R6	-10193.5464	0	0	2026/09/24 00:20:31		2020/12/04 00:00:00	1	633.22248	0
D	P5MIN	CONSTRAINTSOLUTION	6	2026/09/24 00:25:00	2026/09/24 01:10:00	F_MAIN+TTS_TG_R6	-10192.8964	0	0	2026/09/24 00:20:31		2020/12/04 00:00:00	1	633.54748	0
D	P5MIN	CONSTRAINTSOLUTION	6	2026/09/24 00:25:00	2026/09/24 01:15:00	F_MAIN+TTS_TG_R6	-10192.2364	0	0	2026/09/24 00:20:31		2020/12/04 00:00:00	1	633.87748	0
D	P5MIN	CONSTRAINTSOLUTION	6	2026/09/24 00:25:00	2026/09/24 01:20:00	F_MAIN+TTS_TG_R6	-10191.5264	0	0	2026/09/24 00:20:31		2020/12/04 00:00:00	1	634.23248	0
D	P5MIN	CONSTRAINTSOLUTION	6	2026/09/24 00:25:00	2026/09/24 00:25:00	F_MAIN+TTS_TG_R60	-10099.5582	0	0	2026/09/24 00:20:31		2020/12/04 00:00:00	1	630.68547	0
D	P5MIN	CONSTRAINTSOLUTION	6	2026/09/24 00:25:00	2026/09/24 00:30:00	F_MAIN+TTS_TG_R60	-10099.2182	0	0	2026/09/24 00:20:31		2020/12/04 00:00:00	1	630.77748	0
D	P5MIN	CONSTRAINTSOLUTION	6	2026/09/24 00:25:00	2026/09/24 00:40:00	F_MAIN+TTS_TG_R60	-10098.5682	0	0	2026/09/24 00:20:31		2020/12/04 00:00:00	1	631.42748	0
D	P5MIN	CONSTRAINTSOLUTION	6	2026/09/24 00:25:00	2026/09/24 00:45:00	F_MAIN+TTS_TG_R60	-10098.2682	0	0	2026/09/24 00:20:31		2020/12/04 00:00:00	1	631.72748	0
D	P5MIN	CONSTRAINTSOLUTION	6	2026/09/24 00:25:00	2026/09/24 00:50:00	F_MAIN+TTS_TG_R60	-10097.9232	0	0	2026/09/24 00:20:31		2020/12/04 00:00:00	1	632.07248	0
D	P5MIN	CONSTRAINTSOLUTION	6	2026/09/24 00:25:00	2026/09/24 00:55:00	F_MAIN+TTS_TG_R60	-10097.5582	0	0	2026/09/24 00:20:31		2020/12/04 00:00:00	1	632.43748	0
D	P5MIN	CONSTRAINTSOLUTION	6	2026/09/24 00:25:00	2026/09/24 01:00:00	F_MAIN+TTS_TG_R60	-10097.1782	0	0	2026/09/24 00:20:31		2020/12/04 00:00:00	1	632.81748	0
...
C	END OF REPORT	1003

RUN_DATETIME,INTERVAL_DATETIME,CONSTRAINTID,RHS,MARGINALVALUE,VIOLATIONDEGREE,LASTCHANGED,DUID,GENCONID_EFFECTIVEDATE,GENCONID_VERSIONNO,LHS,INTERVENTION
"2026/09/24 00:25:00","2026/09/24 01:05:00",F_MAIN+TTS_TG_R6,-10193.5464,0,0,"2026/09/24 00:20:31",,"2020/12/04 00:00:00",1,633.22248,0
"2026/09/24 00:25:00","2026/09/24 01:10:00",F_MAIN+TTS_TG_R6,-10192.8964,0,0,"2026/09/24 00:20:31",,"2020/12/04 00:00:00",1,633.54748,0
"2026/09/24 00:25:00","2026/09/24 01:15:00",F_MAIN+TTS_TG_R6,-10192.2364,0,0,"2026/09/24 00:20:31",,"2020/12/04 00:00:00",1,633.87748,0
"2026/09/24 00:25:00","2026/09/24 01:20:00",F_MAIN+TTS_TG_R6,-10191.5264,0,0,"2026/09/24 00:20:31",,"2020/12/04 00:00:00",1,634.23248,0
"2026/09/24 00:25:00","2026/09/24 00:25:00",F_MAIN+TTS_TG_R60,-10099.5582,0,0,"2026/09/24 00:20:31",,"2020/12/04 00:00:00",1,630.68547,0
"2026/09/24 00:25:00","2026/09/24 00:30:00",F_MAIN+TTS_TG_R60,-10099.2182,0,0,"2026/09/24 00:20:31",,"2020/12/04 00:00:00",1,630.77748,0
"2026/09/24 00:25:00","2026/09/24 00:40:00",F_MAIN+TTS_TG_R60,-10098.5682,0,0,"2026/09/24 00:20:31",,"2020/12/04 00:00:00",1,631.42748,0
"2026/09/24 00:25:00","2026/09/24 00:45:00",F_MAIN+TTS_TG_R60,-10098.2682,0,0,"2026/09/24 00:20:31",,"2020/12/04 00:00:00",1,631.72748,0
"2026/09/24 00:25:00","2026/09/24 00:50:00",F_MAIN+TTS_TG_R60,-10097.9232,0,0,"2026/09/24 00:20:31",,"2020/12/04 00:00:00",1,632.07248,0
"2026/09/24 00:25:00","2026/09/24 00:55:00",F_MAIN+TTS_TG_R60,-10097.5582,0,0,"2026/09/24 00:20:31",,"2020/12/04 00:00:00",1,632.43748,0
"2026/09/24 00:25:00","2026/09/24 01:00:00",F_MAIN+TTS_TG_R60,-10097.1782,0,0,"2026/09/24 00:20:31",,"2020/12/04 00:00:00",1,632.81748,0
...

Raw Text with Metadata

C,SETP.WORLD,DVD_P5MIN_CONSTRAINTSOLUTION,AEMO,PUBLIC,2026/10/09,17:12:33,001791529953559,MONTHLY_ARCHIVE,001791529953559
I,P5MIN,CONSTRAINTSOLUTION,6,RUN_DATETIME,INTERVAL_DATETIME,CONSTRAINTID,RHS,MARGINALVALUE,VIOLATIONDEGREE,LASTCHANGED,DUID,GENCONID_EFFECTIVEDATE,GENCONID_VERSIONNO,LHS,INTERVENTION
D,P5MIN,CONSTRAINTSOLUTION,6,"2026/09/24 00:25:00","2026/09/24 01:05:00",F_MAIN+TTS_TG_R6,-10193.5464,0,0,"2026/09/24 00:20:31",,"2020/12/04 00:00:00",1,633.22248,0
D,P5MIN,CONSTRAINTSOLUTION,6,"2026/09/24 00:25:00","2026/09/24 01:10:00",F_MAIN+TTS_TG_R6,-10192.8964,0,0,"2026/09/24 00:20:31",,"2020/12/04 00:00:00",1,633.54748,0
D,P5MIN,CONSTRAINTSOLUTION,6,"2026/09/24 00:25:00","2026/09/24 01:15:00",F_MAIN+TTS_TG_R6,-10192.2364,0,0,"2026/09/24 00:20:31",,"2020/12/04 00:00:00",1,633.87748,0
D,P5MIN,CONSTRAINTSOLUTION,6,"2026/09/24 00:25:00","2026/09/24 01:20:00",F_MAIN+TTS_TG_R6,-10191.5264,0,0,"2026/09/24 00:20:31",,"2020/12/04 00:00:00",1,634.23248,0
D,P5MIN,CONSTRAINTSOLUTION,6,"2026/09/24 00:25:00","2026/09/24 00:25:00",F_MAIN+TTS_TG_R60,-10099.5582,0,0,"2026/09/24 00:20:31",,"2020/12/04 00:00:00",1,630.68547,0
D,P5MIN,CONSTRAINTSOLUTION,6,"2026/09/24 00:25:00","2026/09/24 00:30:00",F_MAIN+TTS_TG_R60,-10099.2182,0,0,"2026/09/24 00:20:31",,"2020/12/04 00:00:00",1,630.77748,0
D,P5MIN,CONSTRAINTSOLUTION,6,"2026/09/24 00:25:00","2026/09/24 00:40:00",F_MAIN+TTS_TG_R60,-10098.5682,0,0,"2026/09/24 00:20:31",,"2020/12/04 00:00:00",1,631.42748,0
D,P5MIN,CONSTRAINTSOLUTION,6,"2026/09/24 00:25:00","2026/09/24 00:45:00",F_MAIN+TTS_TG_R60,-10098.2682,0,0,"2026/09/24 00:20:31",,"2020/12/04 00:00:00",1,631.72748,0
D,P5MIN,CONSTRAINTSOLUTION,6,"2026/09/24 00:25:00","2026/09/24 00:50:00",F_MAIN+TTS_TG_R60,-10097.9232,0,0,"2026/09/24 00:20:31",,"2020/12/04 00:00:00",1,632.07248,0
D,P5MIN,CONSTRAINTSOLUTION,6,"2026/09/24 00:25:00","2026/09/24 00:55:00",F_MAIN+TTS_TG_R60,-10097.5582,0,0,"2026/09/24 00:20:31",,"2020/12/04 00:00:00",1,632.43748,0
D,P5MIN,CONSTRAINTSOLUTION,6,"2026/09/24 00:25:00","2026/09/24 01:00:00",F_MAIN+TTS_TG_R60,-10097.1782,0,0,"2026/09/24 00:20:31",,"2020/12/04 00:00:00",1,632.81748,0
...
C,"END OF REPORT",1003

Availability

Visibility: Private & 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).

Tip

There are multiple files per month, because this is such a large table.

The earliest available data is month 1 of 2015. The most recent available data is month 9 of 2026.

Every month in between has at least one file.

Data Size

Each year of data is approximately 15 GB when zipped, 146 GB as unzipped CSV.

Tip

This table is large. Simply calling pd.read_csv() might fill up all the memory on your laptop. (As a rule of thumb, you need double the amount of memory as the size of the CSV.) You will need to think hard about memory optimisation. e.g. consider using Polars instead of Pandas, and selecting only the columns you care about.

Deduplication and PDR Loader Configuration

The primary keys are:

CONSTRAINTID
INTERVAL_DATETIME
RUN_DATETIME

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	CONSTRAINTSOLUTION	6

This means that the CSV files start with metadata columns:

I,P5MIN,CONSTRAINTSOLUTION,6,...
D,P5MIN,CONSTRAINTSOLUTION,6,...

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: P5MIN