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DISPATCHCONSTRAINT

DISPATCHCONSTRAINT sets out details of all binding and interregion constraints in each dispatch run. Note: invoked constraints can be established from GENCONSETINVOKE. Binding constraints show as marginal value >$0. Interconnector constraints are listed so RHS (SCADA calculated limits) can be reported.

DISPATCHCONSTRAINT is public data, and is available to all participants.

Source

DISPATCHCONSTRAINT updates every five minutes.

Column Schema

CONSTRAINTID

Generic Constraint identifier (synonymous with GenConID)

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

This column is mandatory, and a primary key.

Examples:

  • #BBATTERY1_E1
  • #BBATTERY1_E2
  • #BRAEMAR1_E
  • #CLRKCWF1_E1
  • #CLRKCWF2_E1
  • #COHUNSF1_E1
  • #ERB02_E1
  • #ERB02_E2
  • #GREENB1_E1
  • #GREENB1_E2

DISPATCHINTERVAL

Dispatch period identifier, from 001 to 288 in format YYYYMMDDPPP.

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

This column is mandatory, and a primary key.

Example: 20260831241

INTERVENTION

Manual Intervention flag, which, if set (1), causes predispatch to solve twice.

For more information, see the dedicated documentation page.

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

This column is mandatory, and a primary key.

Example: 0

SETTLEMENTDATE

Market date starting at 04:05

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: 2026/09/01 00:05:00

RUNNO

Dispatch run no; always 1

This column only ever takes one value: 1

(The SQL type is NUMBER(3,0).)

AEMO marked this column as mandatory, and a primary key. However, since it only ever takes one value, you can ignore it.

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.

Examples:

  • BBATTERY1
  • BRAEMAR1
  • BRDDBES1
  • BRDDSF01
  • BUSF1
  • CLRKCWF1
  • CLRKCWF2
  • COHUNSF1
  • ERB02
  • GREENB1

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: 2012/07/04 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

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: 2026/09/01 00:00:07

LHS

Aggregation of the constraints LHS term solution values

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

This column is not mandatory.

Examples: -0.14696, 3.08403, 0, 380

MARGINALVALUE

$ Value of binding constraint

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

This column is not mandatory.

Examples: -46.38658, 0.01, 0, 5.38

RHS

Right hand Side value as used in dispatch.

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

This column is not mandatory.

Examples: -0.0001, 0.0001, 0, 20000

VIOLATIONDEGREE

Degree of violation in MW

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("SETTLEMENTDATE", "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,
            "DISPATCHINTERVAL": pl.Int128,
            "INTERVENTION": pl.Boolean,
            "RUNNO": pl.Enum(["1"]),
            "DUID": pl.Categorical,
            "GENCONID_EFFECTIVEDATE": pl.Date(),
            "GENCONID_VERSIONNO": pl.Int128,
            "LHS": pl.Float64,
            "MARGINALVALUE": pl.Float64,
            "RHS": pl.Float64,
            "VIOLATIONDEGREE": pl.Float64,
        }
    )
    # Drop deprecated/unused columns
    .drop("RUNNO")
    # Deduplicate
    # These files are so large that this step may cause your laptop to run out of memory.
    # We want one row per ['CONSTRAINTID', 'DISPATCHINTERVAL', 'INTERVENTION', 'RUNNO', 'SETTLEMENTDATE']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(
        subset=[
            "CONSTRAINTID",
            "DISPATCHINTERVAL",
            "INTERVENTION",
            "RUNNO",
            "SETTLEMENTDATE",
        ],
        keep="last",
    )
)

Nemosis

from nemosis import dynamic_data_compiler

df = dynamic_data_compiler(
    '2024/01/01 00:00:00',
    '2024/01/31 00:00:00',
    "DISPATCHCONSTRAINT",
    "data/"
)

Nemosis only includes a subset of columns by default, as an optimisation. (i.e. the commonly used columns.) To include others:

from nemosis import defaults

defaults.table_columns["DISPATCHCONSTRAINT"] += ["DUID"]
df = dynamic_data_compiler(...)

Example File

From PUBLIC_ARCHIVE_DISPATCHCONSTRAINT_FILE01_202609010000.zip

SETTLEMENTDATERUNNOCONSTRAINTIDDISPATCHINTERVALINTERVENTIONRHSMARGINALVALUEVIOLATIONDEGREELASTCHANGEDDUIDGENCONID_EFFECTIVEDATEGENCONID_VERSIONNOLHS
2026/09/01 00:05:001C_N_NESBESS_150_G202608312410150002026/09/01 00:00:072026/02/18 00:00:0020
2026/09/01 00:05:001C_N_NESBESS_150_L202608312410-150002026/09/01 00:00:072026/02/18 00:00:0020
2026/09/01 00:05:001C_N_QUORNP_019_L_H202608312410-19.8002026/09/01 00:00:072026/01/29 00:00:0010
2026/09/01 00:05:001C_N_QUORNP_080_G_H20260831241080002026/09/01 00:00:072026/01/29 00:00:0010
2026/09/01 00:05:001C_V_GPWFWST_462202608312410462002026/09/01 00:00:072026/02/02 00:00:001124.95
2026/09/01 00:05:001DATASNAP_DFS_LS20260831241010056.73454002026/09/01 00:00:072018/04/13 00:00:001-41
2026/09/01 00:05:001DATASNAP_DFS_NCAN20260831241010395.73554002026/09/01 00:00:072024/07/11 00:00:001-41
2026/09/01 00:05:001DATASNAP_DFS_NCWEST20260831241010535.33612002026/09/01 00:00:072024/07/11 00:00:001-41
2026/09/01 00:05:001DATASNAP_DFS_NNTH20260831241010619.16943002026/09/01 00:00:072024/07/11 00:00:001-41
2026/09/01 00:05:001DATASNAP_DFS_NSYD20260831241014543.72852002026/09/01 00:00:072024/07/11 00:00:001-41
2026/09/01 00:05:001DATASNAP_DFS_QCEN20260831241010853.03595002026/09/01 00:00:072024/07/11 00:00:001-41
...

CSETP.WORLDDVD_DISPATCHCONSTRAINTAEMOPUBLIC2026/10/0915:25:34001791523534318MONTHLY_ARCHIVE001791523534318
IDISPATCHCONSTRAINT5SETTLEMENTDATERUNNOCONSTRAINTIDDISPATCHINTERVALINTERVENTIONRHSMARGINALVALUEVIOLATIONDEGREELASTCHANGEDDUIDGENCONID_EFFECTIVEDATEGENCONID_VERSIONNOLHS
DDISPATCHCONSTRAINT52026/09/01 00:05:001C_N_NESBESS_150_G202608312410150002026/09/01 00:00:072026/02/18 00:00:0020
DDISPATCHCONSTRAINT52026/09/01 00:05:001C_N_NESBESS_150_L202608312410-150002026/09/01 00:00:072026/02/18 00:00:0020
DDISPATCHCONSTRAINT52026/09/01 00:05:001C_N_QUORNP_019_L_H202608312410-19.8002026/09/01 00:00:072026/01/29 00:00:0010
DDISPATCHCONSTRAINT52026/09/01 00:05:001C_N_QUORNP_080_G_H20260831241080002026/09/01 00:00:072026/01/29 00:00:0010
DDISPATCHCONSTRAINT52026/09/01 00:05:001C_V_GPWFWST_462202608312410462002026/09/01 00:00:072026/02/02 00:00:001124.95
DDISPATCHCONSTRAINT52026/09/01 00:05:001DATASNAP_DFS_LS20260831241010056.73454002026/09/01 00:00:072018/04/13 00:00:001-41
DDISPATCHCONSTRAINT52026/09/01 00:05:001DATASNAP_DFS_NCAN20260831241010395.73554002026/09/01 00:00:072024/07/11 00:00:001-41
DDISPATCHCONSTRAINT52026/09/01 00:05:001DATASNAP_DFS_NCWEST20260831241010535.33612002026/09/01 00:00:072024/07/11 00:00:001-41
DDISPATCHCONSTRAINT52026/09/01 00:05:001DATASNAP_DFS_NNTH20260831241010619.16943002026/09/01 00:00:072024/07/11 00:00:001-41
DDISPATCHCONSTRAINT52026/09/01 00:05:001DATASNAP_DFS_NSYD20260831241014543.72852002026/09/01 00:00:072024/07/11 00:00:001-41
DDISPATCHCONSTRAINT52026/09/01 00:05:001DATASNAP_DFS_QCEN20260831241010853.03595002026/09/01 00:00:072024/07/11 00:00:001-41
...
CEND OF REPORT1003

SETTLEMENTDATE,RUNNO,CONSTRAINTID,DISPATCHINTERVAL,INTERVENTION,RHS,MARGINALVALUE,VIOLATIONDEGREE,LASTCHANGED,DUID,GENCONID_EFFECTIVEDATE,GENCONID_VERSIONNO,LHS
"2026/09/01 00:05:00",1,C_N_NESBESS_150_G,20260831241,0,150,0,0,"2026/09/01 00:00:07",,"2026/02/18 00:00:00",2,0
"2026/09/01 00:05:00",1,C_N_NESBESS_150_L,20260831241,0,-150,0,0,"2026/09/01 00:00:07",,"2026/02/18 00:00:00",2,0
"2026/09/01 00:05:00",1,C_N_QUORNP_019_L_H,20260831241,0,-19.8,0,0,"2026/09/01 00:00:07",,"2026/01/29 00:00:00",1,0
"2026/09/01 00:05:00",1,C_N_QUORNP_080_G_H,20260831241,0,80,0,0,"2026/09/01 00:00:07",,"2026/01/29 00:00:00",1,0
"2026/09/01 00:05:00",1,C_V_GPWFWST_462,20260831241,0,462,0,0,"2026/09/01 00:00:07",,"2026/02/02 00:00:00",1,124.95
"2026/09/01 00:05:00",1,DATASNAP_DFS_LS,20260831241,0,10056.73454,0,0,"2026/09/01 00:00:07",,"2018/04/13 00:00:00",1,-41
"2026/09/01 00:05:00",1,DATASNAP_DFS_NCAN,20260831241,0,10395.73554,0,0,"2026/09/01 00:00:07",,"2024/07/11 00:00:00",1,-41
"2026/09/01 00:05:00",1,DATASNAP_DFS_NCWEST,20260831241,0,10535.33612,0,0,"2026/09/01 00:00:07",,"2024/07/11 00:00:00",1,-41
"2026/09/01 00:05:00",1,DATASNAP_DFS_NNTH,20260831241,0,10619.16943,0,0,"2026/09/01 00:00:07",,"2024/07/11 00:00:00",1,-41
"2026/09/01 00:05:00",1,DATASNAP_DFS_NSYD,20260831241,0,14543.72852,0,0,"2026/09/01 00:00:07",,"2024/07/11 00:00:00",1,-41
"2026/09/01 00:05:00",1,DATASNAP_DFS_QCEN,20260831241,0,10853.03595,0,0,"2026/09/01 00:00:07",,"2024/07/11 00:00:00",1,-41
...

C,SETP.WORLD,DVD_DISPATCHCONSTRAINT,AEMO,PUBLIC,2026/10/09,15:25:34,001791523534318,MONTHLY_ARCHIVE,001791523534318
I,DISPATCH,CONSTRAINT,5,SETTLEMENTDATE,RUNNO,CONSTRAINTID,DISPATCHINTERVAL,INTERVENTION,RHS,MARGINALVALUE,VIOLATIONDEGREE,LASTCHANGED,DUID,GENCONID_EFFECTIVEDATE,GENCONID_VERSIONNO,LHS
D,DISPATCH,CONSTRAINT,5,"2026/09/01 00:05:00",1,C_N_NESBESS_150_G,20260831241,0,150,0,0,"2026/09/01 00:00:07",,"2026/02/18 00:00:00",2,0
D,DISPATCH,CONSTRAINT,5,"2026/09/01 00:05:00",1,C_N_NESBESS_150_L,20260831241,0,-150,0,0,"2026/09/01 00:00:07",,"2026/02/18 00:00:00",2,0
D,DISPATCH,CONSTRAINT,5,"2026/09/01 00:05:00",1,C_N_QUORNP_019_L_H,20260831241,0,-19.8,0,0,"2026/09/01 00:00:07",,"2026/01/29 00:00:00",1,0
D,DISPATCH,CONSTRAINT,5,"2026/09/01 00:05:00",1,C_N_QUORNP_080_G_H,20260831241,0,80,0,0,"2026/09/01 00:00:07",,"2026/01/29 00:00:00",1,0
D,DISPATCH,CONSTRAINT,5,"2026/09/01 00:05:00",1,C_V_GPWFWST_462,20260831241,0,462,0,0,"2026/09/01 00:00:07",,"2026/02/02 00:00:00",1,124.95
D,DISPATCH,CONSTRAINT,5,"2026/09/01 00:05:00",1,DATASNAP_DFS_LS,20260831241,0,10056.73454,0,0,"2026/09/01 00:00:07",,"2018/04/13 00:00:00",1,-41
D,DISPATCH,CONSTRAINT,5,"2026/09/01 00:05:00",1,DATASNAP_DFS_NCAN,20260831241,0,10395.73554,0,0,"2026/09/01 00:00:07",,"2024/07/11 00:00:00",1,-41
D,DISPATCH,CONSTRAINT,5,"2026/09/01 00:05:00",1,DATASNAP_DFS_NCWEST,20260831241,0,10535.33612,0,0,"2026/09/01 00:00:07",,"2024/07/11 00:00:00",1,-41
D,DISPATCH,CONSTRAINT,5,"2026/09/01 00:05:00",1,DATASNAP_DFS_NNTH,20260831241,0,10619.16943,0,0,"2026/09/01 00:00:07",,"2024/07/11 00:00:00",1,-41
D,DISPATCH,CONSTRAINT,5,"2026/09/01 00:05:00",1,DATASNAP_DFS_NSYD,20260831241,0,14543.72852,0,0,"2026/09/01 00:00:07",,"2024/07/11 00:00:00",1,-41
D,DISPATCH,CONSTRAINT,5,"2026/09/01 00:05:00",1,DATASNAP_DFS_QCEN,20260831241,0,10853.03595,0,0,"2026/09/01 00:00:07",,"2024/07/11 00:00:00",1,-41
...
C,"END OF REPORT",1003

Availability

Visibility: Private & Public Next-Day

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 GB when zipped, 21 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:

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
DISPATCH CONSTRAINT 5

This means that the CSV files start with metadata columns:

I,DISPATCH,CONSTRAINT,5,...
D,DISPATCH,CONSTRAINT,5,...

Indexes

There is more than one index.

Index 1:

Index 2:

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: DISPATCH