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MCC_CONSTRAINTSOLUTION

Constraint solution data from the MCC dispatch rerun process. Note only constraints with a non-zero marginal value are published.

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:

  • #ERB02_E1
  • #SSE_PPCCGT_20
  • F_I+BIP_ML_L1
  • F_I+GFT_TG_R5
  • F_I+GFT_TG_R60
  • F_I+LREG_0210
  • F_I+NIL_APD_TL_L5
  • F_I+NIL_APD_TL_L6
  • F_I+NIL_APD_TL_L60
  • F_I+NIL_MG_R1

RUN_DATETIME

5-minute Dispatch Run identifier

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

This column is mandatory, and a primary key.

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

MARGINALVALUE

Generic Constraint Marginal Value for this MCC run

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

This column is not mandatory.

Examples: -0.38951, 0.00028, -1010.50362, 23206.62311

RHS

Generic Constraint RHS Value for this MCC run

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

This column is not mandatory.

Examples: -0.01, 0.01, -125, 441

New Data Timing

Each MCC dispatch rerun

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").str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast(
        {"CONSTRAINTID": pl.Categorical, "MARGINALVALUE": pl.Float64, "RHS": pl.Float64}
    )
    # Deduplicate
    # We want one row per ['CONSTRAINTID', 'RUN_DATETIME']
    .unique(subset=["CONSTRAINTID", "RUN_DATETIME"], keep="any")
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_MCC_CONSTRAINTSOLUTION_FILE01_202609010000.zip

RUN_DATETIMECONSTRAINTIDRHSMARGINALVALUE
2026/09/01 00:05:00#ERB02_E16-64.95006
2026/09/01 00:05:00F_I+NIL_APD_TL_L5554.56360.01
2026/09/01 00:05:00F_I+NIL_APD_TL_L6383.180560.01
2026/09/01 00:05:00F_I+NIL_APD_TL_L60483.991760.01
2026/09/01 00:05:00F_I+NIL_MG_R1349.865520.02
2026/09/01 00:05:00F_I+NIL_MG_R5653.397130.01
2026/09/01 00:05:00F_I+NIL_MG_R6582.825280.04
2026/09/01 00:05:00F_I+NIL_MG_R60582.825280.03
2026/09/01 00:05:00F_T+LREG_0050501.65
2026/09/01 00:05:00F_T+RREG_0050505.38
2026/09/01 00:05:00F_TASCAP_LREG_02101600.99
...

CSETP.WORLDDVD_MCC_CONSTRAINTSOLUTIONAEMOPUBLIC2026/10/0915:53:31001791525211594MONTHLY_ARCHIVE001791525211594
IMCCCONSTRAINTSOLUTION1RUN_DATETIMECONSTRAINTIDRHSMARGINALVALUE
DMCCCONSTRAINTSOLUTION12026/09/01 00:05:00#ERB02_E16-64.95006
DMCCCONSTRAINTSOLUTION12026/09/01 00:05:00F_I+NIL_APD_TL_L5554.56360.01
DMCCCONSTRAINTSOLUTION12026/09/01 00:05:00F_I+NIL_APD_TL_L6383.180560.01
DMCCCONSTRAINTSOLUTION12026/09/01 00:05:00F_I+NIL_APD_TL_L60483.991760.01
DMCCCONSTRAINTSOLUTION12026/09/01 00:05:00F_I+NIL_MG_R1349.865520.02
DMCCCONSTRAINTSOLUTION12026/09/01 00:05:00F_I+NIL_MG_R5653.397130.01
DMCCCONSTRAINTSOLUTION12026/09/01 00:05:00F_I+NIL_MG_R6582.825280.04
DMCCCONSTRAINTSOLUTION12026/09/01 00:05:00F_I+NIL_MG_R60582.825280.03
DMCCCONSTRAINTSOLUTION12026/09/01 00:05:00F_T+LREG_0050501.65
DMCCCONSTRAINTSOLUTION12026/09/01 00:05:00F_T+RREG_0050505.38
DMCCCONSTRAINTSOLUTION12026/09/01 00:05:00F_TASCAP_LREG_02101600.99
...
CEND OF REPORT1003

RUN_DATETIME,CONSTRAINTID,RHS,MARGINALVALUE
"2026/09/01 00:05:00",#ERB02_E1,6,-64.95006
"2026/09/01 00:05:00",F_I+NIL_APD_TL_L5,554.5636,0.01
"2026/09/01 00:05:00",F_I+NIL_APD_TL_L6,383.18056,0.01
"2026/09/01 00:05:00",F_I+NIL_APD_TL_L60,483.99176,0.01
"2026/09/01 00:05:00",F_I+NIL_MG_R1,349.86552,0.02
"2026/09/01 00:05:00",F_I+NIL_MG_R5,653.39713,0.01
"2026/09/01 00:05:00",F_I+NIL_MG_R6,582.82528,0.04
"2026/09/01 00:05:00",F_I+NIL_MG_R60,582.82528,0.03
"2026/09/01 00:05:00",F_T+LREG_0050,50,1.65
"2026/09/01 00:05:00",F_T+RREG_0050,50,5.38
"2026/09/01 00:05:00",F_TASCAP_LREG_0210,160,0.99
...

C,SETP.WORLD,DVD_MCC_CONSTRAINTSOLUTION,AEMO,PUBLIC,2026/10/09,15:53:31,001791525211594,MONTHLY_ARCHIVE,001791525211594
I,MCC,CONSTRAINTSOLUTION,1,RUN_DATETIME,CONSTRAINTID,RHS,MARGINALVALUE
D,MCC,CONSTRAINTSOLUTION,1,"2026/09/01 00:05:00",#ERB02_E1,6,-64.95006
D,MCC,CONSTRAINTSOLUTION,1,"2026/09/01 00:05:00",F_I+NIL_APD_TL_L5,554.5636,0.01
D,MCC,CONSTRAINTSOLUTION,1,"2026/09/01 00:05:00",F_I+NIL_APD_TL_L6,383.18056,0.01
D,MCC,CONSTRAINTSOLUTION,1,"2026/09/01 00:05:00",F_I+NIL_APD_TL_L60,483.99176,0.01
D,MCC,CONSTRAINTSOLUTION,1,"2026/09/01 00:05:00",F_I+NIL_MG_R1,349.86552,0.02
D,MCC,CONSTRAINTSOLUTION,1,"2026/09/01 00:05:00",F_I+NIL_MG_R5,653.39713,0.01
D,MCC,CONSTRAINTSOLUTION,1,"2026/09/01 00:05:00",F_I+NIL_MG_R6,582.82528,0.04
D,MCC,CONSTRAINTSOLUTION,1,"2026/09/01 00:05:00",F_I+NIL_MG_R60,582.82528,0.03
D,MCC,CONSTRAINTSOLUTION,1,"2026/09/01 00:05:00",F_T+LREG_0050,50,1.65
D,MCC,CONSTRAINTSOLUTION,1,"2026/09/01 00:05:00",F_T+RREG_0050,50,5.38
D,MCC,CONSTRAINTSOLUTION,1,"2026/09/01 00:05:00",F_TASCAP_LREG_0210,160,0.99
...
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 22 MB when zipped, 220 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
MCC CONSTRAINTSOLUTION 1

This means that the CSV files start with metadata columns:

I,MCC,CONSTRAINTSOLUTION,1,...
D,MCC,CONSTRAINTSOLUTION,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: MCC_DISPATCH