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DISPATCH_LOCAL_PRICE

Sets out local pricing offsets associated with each DUID connection point for each dispatch period. Note that from 2014 Mid year release only records with non-zero Local_Price_Adjustment values are issued

Column Schema

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
  • ARWF1
  • BALB1
  • BALDHWF1
  • BANN1
  • BHB1
  • BLUFF1
  • BOMENSF1
  • BROKENH1
  • BRYB1WF1

SETTLEMENTDATE

Market date time 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

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: -1.97, 1.63, -1010.5, 51.66

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

New Data Timing

Every 5 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("SETTLEMENTDATE").str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast(
        {
            "DUID": pl.Categorical,
            "LOCAL_PRICE_ADJUSTMENT": pl.Float64,
            "LOCALLY_CONSTRAINED": int,  # or pl.Int8
        }
    )
    # Deduplicate
    # We want one row per ['DUID', 'SETTLEMENTDATE']
    # Tie-breaker: greatest value of ['SETTLEMENTDATE', 'DUID']
    .sort(["SETTLEMENTDATE", "DUID"], descending=False)
    .unique(subset=["DUID", "SETTLEMENTDATE"], keep="last")
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_DISPATCH_LOCAL_PRICE_FILE01_202609010000.zip

SETTLEMENTDATEDUIDLOCAL_PRICE_ADJUSTMENTLOCALLY_CONSTRAINED
2026/09/01 00:05:00ARWF116.611
2026/09/01 00:05:00BALB116.611
2026/09/01 00:05:00BALDHWF116.611
2026/09/01 00:05:00BANN116.611
2026/09/01 00:05:00BHB1-10.951
2026/09/01 00:05:00BROKENH1-10.951
2026/09/01 00:05:00BRYB1WF116.611
2026/09/01 00:05:00BRYB2WF216.611
2026/09/01 00:05:00BULBES116.611
2026/09/01 00:05:00BULGANA116.611
2026/09/01 00:05:00CHYTWF116.611
...

CSETP.WORLDDVD_DISPATCH_LOCAL_PRICEAEMOPUBLIC2026/10/0915:25:06001791523506333MONTHLY_ARCHIVE001791523506333
IDISPATCHLOCAL_PRICE1SETTLEMENTDATEDUIDLOCAL_PRICE_ADJUSTMENTLOCALLY_CONSTRAINED
DDISPATCHLOCAL_PRICE12026/09/01 00:05:00ARWF116.611
DDISPATCHLOCAL_PRICE12026/09/01 00:05:00BALB116.611
DDISPATCHLOCAL_PRICE12026/09/01 00:05:00BALDHWF116.611
DDISPATCHLOCAL_PRICE12026/09/01 00:05:00BANN116.611
DDISPATCHLOCAL_PRICE12026/09/01 00:05:00BHB1-10.951
DDISPATCHLOCAL_PRICE12026/09/01 00:05:00BROKENH1-10.951
DDISPATCHLOCAL_PRICE12026/09/01 00:05:00BRYB1WF116.611
DDISPATCHLOCAL_PRICE12026/09/01 00:05:00BRYB2WF216.611
DDISPATCHLOCAL_PRICE12026/09/01 00:05:00BULBES116.611
DDISPATCHLOCAL_PRICE12026/09/01 00:05:00BULGANA116.611
DDISPATCHLOCAL_PRICE12026/09/01 00:05:00CHYTWF116.611
...
CEND OF REPORT1003

SETTLEMENTDATE,DUID,LOCAL_PRICE_ADJUSTMENT,LOCALLY_CONSTRAINED
"2026/09/01 00:05:00",ARWF1,16.61,1
"2026/09/01 00:05:00",BALB1,16.61,1
"2026/09/01 00:05:00",BALDHWF1,16.61,1
"2026/09/01 00:05:00",BANN1,16.61,1
"2026/09/01 00:05:00",BHB1,-10.95,1
"2026/09/01 00:05:00",BROKENH1,-10.95,1
"2026/09/01 00:05:00",BRYB1WF1,16.61,1
"2026/09/01 00:05:00",BRYB2WF2,16.61,1
"2026/09/01 00:05:00",BULBES1,16.61,1
"2026/09/01 00:05:00",BULGANA1,16.61,1
"2026/09/01 00:05:00",CHYTWF1,16.61,1
...

C,SETP.WORLD,DVD_DISPATCH_LOCAL_PRICE,AEMO,PUBLIC,2026/10/09,15:25:06,001791523506333,MONTHLY_ARCHIVE,001791523506333
I,DISPATCH,LOCAL_PRICE,1,SETTLEMENTDATE,DUID,LOCAL_PRICE_ADJUSTMENT,LOCALLY_CONSTRAINED
D,DISPATCH,LOCAL_PRICE,1,"2026/09/01 00:05:00",ARWF1,16.61,1
D,DISPATCH,LOCAL_PRICE,1,"2026/09/01 00:05:00",BALB1,16.61,1
D,DISPATCH,LOCAL_PRICE,1,"2026/09/01 00:05:00",BALDHWF1,16.61,1
D,DISPATCH,LOCAL_PRICE,1,"2026/09/01 00:05:00",BANN1,16.61,1
D,DISPATCH,LOCAL_PRICE,1,"2026/09/01 00:05:00",BHB1,-10.95,1
D,DISPATCH,LOCAL_PRICE,1,"2026/09/01 00:05:00",BROKENH1,-10.95,1
D,DISPATCH,LOCAL_PRICE,1,"2026/09/01 00:05:00",BRYB1WF1,16.61,1
D,DISPATCH,LOCAL_PRICE,1,"2026/09/01 00:05:00",BRYB2WF2,16.61,1
D,DISPATCH,LOCAL_PRICE,1,"2026/09/01 00:05:00",BULBES1,16.61,1
D,DISPATCH,LOCAL_PRICE,1,"2026/09/01 00:05:00",BULGANA1,16.61,1
D,DISPATCH,LOCAL_PRICE,1,"2026/09/01 00:05:00",CHYTWF1,16.61,1
...
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 37 MB when zipped, 367 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
DISPATCH LOCAL_PRICE 1

This means that the CSV files start with metadata columns:

I,DISPATCH,LOCAL_PRICE,1,...
D,DISPATCH,LOCAL_PRICE,1,...

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: DISPATCH