Skip to content

PD7DAY_CONSTRAINTSOLUTION

PD7DAY constraint solution

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:

  • C_N_NESBESS_150_G
  • C_N_NESBESS_150_L
  • C_N_QUORNP_019_L_H
  • C_N_QUORNP_080_G_H
  • C_V_GPWFWST_462
  • DATASNAP_DFS_LS
  • DATASNAP_DFS_NCAN
  • DATASNAP_DFS_NCWEST
  • DATASNAP_DFS_NNTH
  • DATASNAP_DFS_NSYD

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: 2026/09/01 07:30:00

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.

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

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 07:30:00

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 07:12:38

LHS

Aggregation of the constraints LHS term solution values in MW

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

This column is not mandatory.

Examples: -2.11088, 0.46306, 0, 1214

MARGINALVALUE

Marginal cost of constraint (>0 if binding) in $/MW

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

This column is not mandatory.

Examples: -0.02932, 0.01, 0, 4.95

RHS

Right Hand Side value in the capacity evaluation in MW

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

This column is not mandatory.

Examples: -3.21299, 18.71005, 0, 34731

VIOLATIONDEGREE

Amount of Violation (>0 if violating) in MW

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

This column is not mandatory.

Example: 0

New Data Timing

Publishes few times per day

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"

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("INTERVAL_DATETIME", "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})
    .cast(
        {
            "CONSTRAINTID": pl.Categorical,
            "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', 'INTERVENTION', 'RUN_DATETIME']
    # Tie-breaker: greatest value of LASTCHANGED
    .sort(LASTCHANGED, descending=False)
    .unique(
        subset=["CONSTRAINTID", "INTERVAL_DATETIME", "INTERVENTION", "RUN_DATETIME"],
        keep="last",
    )
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_PD7DAY_CONSTRAINTSOLUTION_FILE01_202609010000.zip

RUN_DATETIMEINTERVENTIONINTERVAL_DATETIMECONSTRAINTIDRHSMARGINALVALUEVIOLATIONDEGREELHSLASTCHANGED
2026/09/01 07:30:0002026/09/01 07:30:00C_N_NESBESS_150_G15000-82026/09/01 07:12:38
2026/09/01 07:30:0002026/09/01 07:30:00C_N_NESBESS_150_L-15000-82026/09/01 07:12:38
2026/09/01 07:30:0002026/09/01 07:30:00C_N_QUORNP_019_L_H-19.80026.1012026/09/01 07:12:38
2026/09/01 07:30:0002026/09/01 07:30:00C_N_QUORNP_080_G_H800026.1012026/09/01 07:12:38
2026/09/01 07:30:0002026/09/01 07:30:00C_V_GPWFWST_462462-988.3591604622026/09/01 07:12:38
2026/09/01 07:30:0002026/09/01 07:30:00DATASNAP_DFS_LS1005400-412026/09/01 07:12:38
2026/09/01 07:30:0002026/09/01 07:30:00DATASNAP_DFS_NCAN1059100-412026/09/01 07:12:38
2026/09/01 07:30:0002026/09/01 07:30:00DATASNAP_DFS_NCWEST1050400-412026/09/01 07:12:38
2026/09/01 07:30:0002026/09/01 07:30:00DATASNAP_DFS_NNTH1057700-412026/09/01 07:12:38
2026/09/01 07:30:0002026/09/01 07:30:00DATASNAP_DFS_NSYD1523300-412026/09/01 07:12:38
2026/09/01 07:30:0002026/09/01 07:30:00DATASNAP_DFS_QCEN1080500-412026/09/01 07:12:38
...

CSETP.WORLDDVD_PD7DAY_CONSTRAINTSOLUTIONAEMOPUBLIC2026/10/0916:52:12001791528732502MONTHLY_ARCHIVE001791528732502
IPD7DAYCONSTRAINTSOLUTION1RUN_DATETIMEINTERVENTIONINTERVAL_DATETIMECONSTRAINTIDRHSMARGINALVALUEVIOLATIONDEGREELHSLASTCHANGED
DPD7DAYCONSTRAINTSOLUTION12026/09/01 07:30:0002026/09/01 07:30:00C_N_NESBESS_150_G15000-82026/09/01 07:12:38
DPD7DAYCONSTRAINTSOLUTION12026/09/01 07:30:0002026/09/01 07:30:00C_N_NESBESS_150_L-15000-82026/09/01 07:12:38
DPD7DAYCONSTRAINTSOLUTION12026/09/01 07:30:0002026/09/01 07:30:00C_N_QUORNP_019_L_H-19.80026.1012026/09/01 07:12:38
DPD7DAYCONSTRAINTSOLUTION12026/09/01 07:30:0002026/09/01 07:30:00C_N_QUORNP_080_G_H800026.1012026/09/01 07:12:38
DPD7DAYCONSTRAINTSOLUTION12026/09/01 07:30:0002026/09/01 07:30:00C_V_GPWFWST_462462-988.3591604622026/09/01 07:12:38
DPD7DAYCONSTRAINTSOLUTION12026/09/01 07:30:0002026/09/01 07:30:00DATASNAP_DFS_LS1005400-412026/09/01 07:12:38
DPD7DAYCONSTRAINTSOLUTION12026/09/01 07:30:0002026/09/01 07:30:00DATASNAP_DFS_NCAN1059100-412026/09/01 07:12:38
DPD7DAYCONSTRAINTSOLUTION12026/09/01 07:30:0002026/09/01 07:30:00DATASNAP_DFS_NCWEST1050400-412026/09/01 07:12:38
DPD7DAYCONSTRAINTSOLUTION12026/09/01 07:30:0002026/09/01 07:30:00DATASNAP_DFS_NNTH1057700-412026/09/01 07:12:38
DPD7DAYCONSTRAINTSOLUTION12026/09/01 07:30:0002026/09/01 07:30:00DATASNAP_DFS_NSYD1523300-412026/09/01 07:12:38
DPD7DAYCONSTRAINTSOLUTION12026/09/01 07:30:0002026/09/01 07:30:00DATASNAP_DFS_QCEN1080500-412026/09/01 07:12:38
...
CEND OF REPORT1003

RUN_DATETIME,INTERVENTION,INTERVAL_DATETIME,CONSTRAINTID,RHS,MARGINALVALUE,VIOLATIONDEGREE,LHS,LASTCHANGED
"2026/09/01 07:30:00",0,"2026/09/01 07:30:00",C_N_NESBESS_150_G,150,0,0,-8,"2026/09/01 07:12:38"
"2026/09/01 07:30:00",0,"2026/09/01 07:30:00",C_N_NESBESS_150_L,-150,0,0,-8,"2026/09/01 07:12:38"
"2026/09/01 07:30:00",0,"2026/09/01 07:30:00",C_N_QUORNP_019_L_H,-19.8,0,0,26.101,"2026/09/01 07:12:38"
"2026/09/01 07:30:00",0,"2026/09/01 07:30:00",C_N_QUORNP_080_G_H,80,0,0,26.101,"2026/09/01 07:12:38"
"2026/09/01 07:30:00",0,"2026/09/01 07:30:00",C_V_GPWFWST_462,462,-988.35916,0,462,"2026/09/01 07:12:38"
"2026/09/01 07:30:00",0,"2026/09/01 07:30:00",DATASNAP_DFS_LS,10054,0,0,-41,"2026/09/01 07:12:38"
"2026/09/01 07:30:00",0,"2026/09/01 07:30:00",DATASNAP_DFS_NCAN,10591,0,0,-41,"2026/09/01 07:12:38"
"2026/09/01 07:30:00",0,"2026/09/01 07:30:00",DATASNAP_DFS_NCWEST,10504,0,0,-41,"2026/09/01 07:12:38"
"2026/09/01 07:30:00",0,"2026/09/01 07:30:00",DATASNAP_DFS_NNTH,10577,0,0,-41,"2026/09/01 07:12:38"
"2026/09/01 07:30:00",0,"2026/09/01 07:30:00",DATASNAP_DFS_NSYD,15233,0,0,-41,"2026/09/01 07:12:38"
"2026/09/01 07:30:00",0,"2026/09/01 07:30:00",DATASNAP_DFS_QCEN,10805,0,0,-41,"2026/09/01 07:12:38"
...

C,SETP.WORLD,DVD_PD7DAY_CONSTRAINTSOLUTION,AEMO,PUBLIC,2026/10/09,16:52:12,001791528732502,MONTHLY_ARCHIVE,001791528732502
I,PD7DAY,CONSTRAINTSOLUTION,1,RUN_DATETIME,INTERVENTION,INTERVAL_DATETIME,CONSTRAINTID,RHS,MARGINALVALUE,VIOLATIONDEGREE,LHS,LASTCHANGED
D,PD7DAY,CONSTRAINTSOLUTION,1,"2026/09/01 07:30:00",0,"2026/09/01 07:30:00",C_N_NESBESS_150_G,150,0,0,-8,"2026/09/01 07:12:38"
D,PD7DAY,CONSTRAINTSOLUTION,1,"2026/09/01 07:30:00",0,"2026/09/01 07:30:00",C_N_NESBESS_150_L,-150,0,0,-8,"2026/09/01 07:12:38"
D,PD7DAY,CONSTRAINTSOLUTION,1,"2026/09/01 07:30:00",0,"2026/09/01 07:30:00",C_N_QUORNP_019_L_H,-19.8,0,0,26.101,"2026/09/01 07:12:38"
D,PD7DAY,CONSTRAINTSOLUTION,1,"2026/09/01 07:30:00",0,"2026/09/01 07:30:00",C_N_QUORNP_080_G_H,80,0,0,26.101,"2026/09/01 07:12:38"
D,PD7DAY,CONSTRAINTSOLUTION,1,"2026/09/01 07:30:00",0,"2026/09/01 07:30:00",C_V_GPWFWST_462,462,-988.35916,0,462,"2026/09/01 07:12:38"
D,PD7DAY,CONSTRAINTSOLUTION,1,"2026/09/01 07:30:00",0,"2026/09/01 07:30:00",DATASNAP_DFS_LS,10054,0,0,-41,"2026/09/01 07:12:38"
D,PD7DAY,CONSTRAINTSOLUTION,1,"2026/09/01 07:30:00",0,"2026/09/01 07:30:00",DATASNAP_DFS_NCAN,10591,0,0,-41,"2026/09/01 07:12:38"
D,PD7DAY,CONSTRAINTSOLUTION,1,"2026/09/01 07:30:00",0,"2026/09/01 07:30:00",DATASNAP_DFS_NCWEST,10504,0,0,-41,"2026/09/01 07:12:38"
D,PD7DAY,CONSTRAINTSOLUTION,1,"2026/09/01 07:30:00",0,"2026/09/01 07:30:00",DATASNAP_DFS_NNTH,10577,0,0,-41,"2026/09/01 07:12:38"
D,PD7DAY,CONSTRAINTSOLUTION,1,"2026/09/01 07:30:00",0,"2026/09/01 07:30:00",DATASNAP_DFS_NSYD,15233,0,0,-41,"2026/09/01 07:12:38"
D,PD7DAY,CONSTRAINTSOLUTION,1,"2026/09/01 07:30:00",0,"2026/09/01 07:30:00",DATASNAP_DFS_QCEN,10805,0,0,-41,"2026/09/01 07:12:38"
...
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).

Tip

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

The earliest available data is month 8 of 2024. 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 5 GB when zipped, 49 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
PD7DAY CONSTRAINTSOLUTION 1

This means that the CSV files start with metadata columns:

I,PD7DAY,CONSTRAINTSOLUTION,1,...
D,PD7DAY,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: PD7DAY