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PREDISPATCH_LOCAL_PRICE

Sets out local pricing offsets associated with each DUID connection point for each dispatch period

Column Schema

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 04:30:00

DUID

Dispatchable unit identifier

Warning

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

For more information, see the dedicated documentation page.

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

This column is mandatory, and a primary key.

Examples:

  • AGLHAL
  • AGLSOM
  • ALDGASF1
  • ANGAST1
  • ARWF1
  • AVLSF1
  • BALB1
  • BALDHWF1
  • BANGOWF1
  • BANGOWF2

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 04:02:00

LOCAL_PRICE_ADJUSTMENT

Aggregate Constraint contribution cost of this unit: Sum(MarginalValue x Factor) for all relevant Constraints

SQL Type: NUMBER(10,2) Polars Type: pl.Float64

This column is not mandatory.

Examples: -0.22, 0.25, -26, -1025

LOCALLY_CONSTRAINED

Key for Local_Price_Adjustment: 2 = at least one Outage Constraint; 1 = at least 1 System Normal Constraint (and no Outage Constraint); 0 = No System Normal or Outage Constraints

SQL Type: NUMBER(1,0) Polars Type: pl.Int8

This column is not mandatory.

Examples: 1, 2

PERIODID

A period count, starting from 1 for each predispatch run. Use DATETIME to determine half hour period

SQL Type: VARCHAR2(20) Polars Type: str

This column is not mandatory.

Example: 01

PREDISPATCHSEQNO

Unique identifier of predispatch run in the form YYYYMMDDPP with 01 at 04:30

SQL Type: VARCHAR2(20) Polars Type: str

This column is mandatory (but not a primary key).

Examples:

  • 2026090101
  • 2026090110

New Data Timing

Every 30 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("DATETIME", "LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast(
        {
            "DUID": pl.Categorical,
            "LOCAL_PRICE_ADJUSTMENT": pl.Float64,
            "LOCALLY_CONSTRAINED": int,  # or pl.Int8
            "PERIODID": str,
            "PREDISPATCHSEQNO": str,
        }
    )
    # Deduplicate
    # We want one row per ['DATETIME', 'DUID']
    # Tie-breaker: greatest value of ['DATETIME', 'DUID']
    .sort(["DATETIME", "DUID"], descending=False)
    .unique(subset=["DATETIME", "DUID"], keep="last")
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_PREDISPATCH_LOCAL_PRICE_FILE01_202609010000.zip

PREDISPATCHSEQNODATETIMEDUIDPERIODIDLOCAL_PRICE_ADJUSTMENTLOCALLY_CONSTRAINEDLASTCHANGED
20260901012026/09/01 04:30:00BANN10111.9112026/09/01 04:02:00
20260901012026/09/01 04:30:00BHB101-35.8512026/09/01 04:02:00
20260901012026/09/01 04:30:00GANNSF10111.9112026/09/01 04:02:00
20260901012026/09/01 04:30:00BOMENSF101-35.0212026/09/01 04:02:00
20260901012026/09/01 04:30:00BROKENH101-35.8512026/09/01 04:02:00
20260901012026/09/01 04:30:00COHUNSF10111.9112026/09/01 04:02:00
20260901012026/09/01 04:30:00COLEASF101-35.0212026/09/01 04:02:00
20260901012026/09/01 04:30:00CRWASF101-35.0212026/09/01 04:02:00
20260901012026/09/01 04:30:00DARLSF101-35.0212026/09/01 04:02:00
20260901012026/09/01 04:30:00DARTM10130.1212026/09/01 04:02:00
20260901012026/09/01 04:30:00EILDON10130.1212026/09/01 04:02:00
...

CSETP.WORLDDVD_PREDISPATCH_LOCAL_PRICEAEMOPUBLIC2026/10/0916:26:51001791527211839MONTHLY_ARCHIVE001791527211839
IPREDISPATCHLOCAL_PRICE1PREDISPATCHSEQNODATETIMEDUIDPERIODIDLOCAL_PRICE_ADJUSTMENTLOCALLY_CONSTRAINEDLASTCHANGED
DPREDISPATCHLOCAL_PRICE120260901012026/09/01 04:30:00BANN10111.9112026/09/01 04:02:00
DPREDISPATCHLOCAL_PRICE120260901012026/09/01 04:30:00BHB101-35.8512026/09/01 04:02:00
DPREDISPATCHLOCAL_PRICE120260901012026/09/01 04:30:00GANNSF10111.9112026/09/01 04:02:00
DPREDISPATCHLOCAL_PRICE120260901012026/09/01 04:30:00BOMENSF101-35.0212026/09/01 04:02:00
DPREDISPATCHLOCAL_PRICE120260901012026/09/01 04:30:00BROKENH101-35.8512026/09/01 04:02:00
DPREDISPATCHLOCAL_PRICE120260901012026/09/01 04:30:00COHUNSF10111.9112026/09/01 04:02:00
DPREDISPATCHLOCAL_PRICE120260901012026/09/01 04:30:00COLEASF101-35.0212026/09/01 04:02:00
DPREDISPATCHLOCAL_PRICE120260901012026/09/01 04:30:00CRWASF101-35.0212026/09/01 04:02:00
DPREDISPATCHLOCAL_PRICE120260901012026/09/01 04:30:00DARLSF101-35.0212026/09/01 04:02:00
DPREDISPATCHLOCAL_PRICE120260901012026/09/01 04:30:00DARTM10130.1212026/09/01 04:02:00
DPREDISPATCHLOCAL_PRICE120260901012026/09/01 04:30:00EILDON10130.1212026/09/01 04:02:00
...
CEND OF REPORT1003

PREDISPATCHSEQNO,DATETIME,DUID,PERIODID,LOCAL_PRICE_ADJUSTMENT,LOCALLY_CONSTRAINED,LASTCHANGED
2026090101,"2026/09/01 04:30:00",BANN1,01,11.91,1,"2026/09/01 04:02:00"
2026090101,"2026/09/01 04:30:00",BHB1,01,-35.85,1,"2026/09/01 04:02:00"
2026090101,"2026/09/01 04:30:00",GANNSF1,01,11.91,1,"2026/09/01 04:02:00"
2026090101,"2026/09/01 04:30:00",BOMENSF1,01,-35.02,1,"2026/09/01 04:02:00"
2026090101,"2026/09/01 04:30:00",BROKENH1,01,-35.85,1,"2026/09/01 04:02:00"
2026090101,"2026/09/01 04:30:00",COHUNSF1,01,11.91,1,"2026/09/01 04:02:00"
2026090101,"2026/09/01 04:30:00",COLEASF1,01,-35.02,1,"2026/09/01 04:02:00"
2026090101,"2026/09/01 04:30:00",CRWASF1,01,-35.02,1,"2026/09/01 04:02:00"
2026090101,"2026/09/01 04:30:00",DARLSF1,01,-35.02,1,"2026/09/01 04:02:00"
2026090101,"2026/09/01 04:30:00",DARTM1,01,30.12,1,"2026/09/01 04:02:00"
2026090101,"2026/09/01 04:30:00",EILDON1,01,30.12,1,"2026/09/01 04:02:00"
...

C,SETP.WORLD,DVD_PREDISPATCH_LOCAL_PRICE,AEMO,PUBLIC,2026/10/09,16:26:51,001791527211839,MONTHLY_ARCHIVE,001791527211839
I,PREDISPATCH,LOCAL_PRICE,1,PREDISPATCHSEQNO,DATETIME,DUID,PERIODID,LOCAL_PRICE_ADJUSTMENT,LOCALLY_CONSTRAINED,LASTCHANGED
D,PREDISPATCH,LOCAL_PRICE,1,2026090101,"2026/09/01 04:30:00",BANN1,01,11.91,1,"2026/09/01 04:02:00"
D,PREDISPATCH,LOCAL_PRICE,1,2026090101,"2026/09/01 04:30:00",BHB1,01,-35.85,1,"2026/09/01 04:02:00"
D,PREDISPATCH,LOCAL_PRICE,1,2026090101,"2026/09/01 04:30:00",GANNSF1,01,11.91,1,"2026/09/01 04:02:00"
D,PREDISPATCH,LOCAL_PRICE,1,2026090101,"2026/09/01 04:30:00",BOMENSF1,01,-35.02,1,"2026/09/01 04:02:00"
D,PREDISPATCH,LOCAL_PRICE,1,2026090101,"2026/09/01 04:30:00",BROKENH1,01,-35.85,1,"2026/09/01 04:02:00"
D,PREDISPATCH,LOCAL_PRICE,1,2026090101,"2026/09/01 04:30:00",COHUNSF1,01,11.91,1,"2026/09/01 04:02:00"
D,PREDISPATCH,LOCAL_PRICE,1,2026090101,"2026/09/01 04:30:00",COLEASF1,01,-35.02,1,"2026/09/01 04:02:00"
D,PREDISPATCH,LOCAL_PRICE,1,2026090101,"2026/09/01 04:30:00",CRWASF1,01,-35.02,1,"2026/09/01 04:02:00"
D,PREDISPATCH,LOCAL_PRICE,1,2026090101,"2026/09/01 04:30:00",DARLSF1,01,-35.02,1,"2026/09/01 04:02:00"
D,PREDISPATCH,LOCAL_PRICE,1,2026090101,"2026/09/01 04:30:00",DARTM1,01,30.12,1,"2026/09/01 04:02:00"
D,PREDISPATCH,LOCAL_PRICE,1,2026090101,"2026/09/01 04:30:00",EILDON1,01,30.12,1,"2026/09/01 04:02:00"
...
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 8 of 2024. 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 7 MB when zipped, 73 MB as unzipped CSV.

Deduplication and PDR Loader Configuration

The primary keys are:

The transaction type is "UPDATE-INSERT", which means that you will likely see duplicate data with different values.

You should deduplicate by sorting based on LASTCHANGED, within each group of primary keys.

PDR Loader configuration is:

Report Name Report Subtype Version
PREDISPATCH LOCAL_PRICE 1

This means that the CSV files start with metadata columns:

I,PREDISPATCH,LOCAL_PRICE,1,...
D,PREDISPATCH,LOCAL_PRICE,1,...

ROW_FILTER_TYPE is LASTCHANGED

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: PRE_DISPATCH