RESERVE

RESERVE sets out specific reserve requirements for dispatch, predispatch and STPASA, for each half-hour interval by region. Updates show as new versions for a date.

Deprecation Warning

This table is in the HISTORICAL TABLES package, so is likely deprecated.

Two fields specify Frequency Controlled Ancillary Services requirements for the regulation ancillary services. Another two fields specify the Lack of Reserve levels to be applied in the ST PASA solver.

Change Notice 324 (for the FCAS Constraint enhancements project) means that Dispatch no longer utilises the static FCAS requirements defined in the DELTAMW and RESERVE tables. These tables are replaced with constraint data as a source of FCAS requirements.

RESERVE data is public, so is available to all participants.

Source

RESERVE updates as AEMO updates forecasts, daily.

Tip

1 second, 6 second, 5 minute, 60 second and regulation refer to types of FCAS markets.

Column Schema

PERIODID

Market Trading Interval

SQL Type: NUMBER(2,0) Polars Type: pl.Int8

This column is mandatory, and a primary key.

Examples: 1, 48

REGIONID

Differentiates this region from all other regions

For more information, see the dedicated documentation page.

The SQL type is VARCHAR2(12). This is actually a factor/enum, with values:

QLD1
VIC1
TAS1
NSW1
SA1
SNOWY1

This column is mandatory, and a primary key.

SETTLEMENTDATE

Market date starting at 04:00am

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

This column is mandatory, and a primary key.

Example: 2018/05/03 00:00:00

Note that AEMO represents dates as datetimes with a midnight time component.

VERSIONNO

Version No of record for this date, the version of the file loaded to produce these reserve figures

SQL Type: NUMBER(3,0) Polars Type: pl.UInt16

This column is mandatory, and a primary key.

Example: 1

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: 2018/05/02 13:44:34

LOADREJECTIONRESERVEREQ

PASA Load rejection reserve requirement

SQL Type: NUMBER(10,0) Polars Type: pl.UInt64

This column is not mandatory.

LOR1LEVEL

PASA Lack of Reserve 1 Level

SQL Type: NUMBER(6,0) Polars Type: pl.UInt32

This column is not mandatory.

Examples: 420, 600

LOR2LEVEL

PASA Lack of Reserve 1 Level

SQL Type: NUMBER(6,0) Polars Type: pl.UInt32

This column is not mandatory.

Examples: 220, 350

LOWER5MIN

Lower 5 minute reserve requirement

SQL Type: NUMBER(6,0) Polars Type: pl.UInt32

This column is not mandatory.

LOWER6SEC

Lower 6 second reserve requirement

SQL Type: NUMBER(6,0) Polars Type: pl.UInt32

This column is not mandatory.

LOWER60SEC

Lower 60 second reserve requirement

SQL Type: NUMBER(6,0) Polars Type: pl.Int32

This column is not mandatory.

LOWERREG

Lower Regulation reserve requirement

SQL Type: NUMBER(6,0) Polars Type: pl.UInt32

This column is not mandatory.

PASARESERVE

PASA reserve requirement

SQL Type: NUMBER(6,0) Polars Type: pl.Int32

This column is not mandatory.

Example: -268

RAISE5MIN

Raise 5 minute reserve requirement

SQL Type: NUMBER(6,0) Polars Type: pl.UInt32

This column is not mandatory.

RAISE6SEC

Raise 6 second reserve requirement

SQL Type: NUMBER(6,0) Polars Type: pl.Int32

This column is not mandatory.

RAISE60SEC

Raise 60 second reserve requirement

SQL Type: NUMBER(6,0) Polars Type: pl.UInt32

This column is not mandatory.

RAISEREG

Raise Regulation reserve requirement

SQL Type: NUMBER(6,0) Polars Type: pl.UInt32

This column is not mandatory.

New Data Timing

Daily when AEMO updates forecasts

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"

# 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",
    )
    .with_columns(
        pl.col("LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
        pl.col("SETTLEMENTDATE").str.strptime(pl.Date, d_fmt),
    )
    .cast(
        {
            "PERIODID": int,  # or pl.Int8
            "REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
            "SETTLEMENTDATE": pl.Date(),
            "VERSIONNO": int,  # or pl.UInt16
            "LOADREJECTIONRESERVEREQ": int,  # or pl.UInt64
            "LOR1LEVEL": int,  # or pl.UInt32
            "LOR2LEVEL": int,  # or pl.UInt32
            "LOWER5MIN": int,  # or pl.UInt32
            "LOWER6SEC": int,  # or pl.UInt32
            "LOWER60SEC": int,  # or pl.Int32
            "LOWERREG": int,  # or pl.UInt32
            "PASARESERVE": int,  # or pl.Int32
            "RAISE5MIN": int,  # or pl.UInt32
            "RAISE6SEC": int,  # or pl.Int32
            "RAISE60SEC": int,  # or pl.UInt32
            "RAISEREG": int,  # or pl.UInt32
        }
    )
    # Deduplicate
    # We want one row per ['PERIODID', 'REGIONID', 'SETTLEMENTDATE', 'VERSIONNO']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(subset=["PERIODID", "REGIONID", "SETTLEMENTDATE", "VERSIONNO"], keep="last")
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_DVD_RESERVE_201805010000.zip

Table without MetadataTable with MetadataRaw Text without Metadata

SETTLEMENTDATE	VERSIONNO	REGIONID	PERIODID	LOWER5MIN	LOWER60SEC	LOWER6SEC	RAISE5MIN	RAISE60SEC	RAISE6SEC	LASTCHANGED	PASARESERVE	LOADREJECTIONRESERVEREQ	RAISEREG	LOWERREG	LOR1LEVEL	LOR2LEVEL
2018/05/03 00:00:00	1	SA1	1							2018/05/02 13:44:34	-268				600	350
2018/05/03 00:00:00	1	SA1	2							2018/05/02 13:44:34	-268				600	350
2018/05/03 00:00:00	1	SA1	3							2018/05/02 13:44:34	-268				600	350
2018/05/03 00:00:00	1	SA1	4							2018/05/02 13:44:34	-268				600	350
2018/05/03 00:00:00	1	SA1	5							2018/05/02 13:44:34	-268				600	350
2018/05/03 00:00:00	1	SA1	6							2018/05/02 13:44:34	-268				600	350
2018/05/03 00:00:00	1	SA1	7							2018/05/02 13:44:34	-268				600	350
2018/05/03 00:00:00	1	SA1	8							2018/05/02 13:44:34	-268				600	350
2018/05/03 00:00:00	1	SA1	9							2018/05/02 13:44:34	-268				600	350
2018/05/03 00:00:00	1	SA1	10							2018/05/02 13:44:34	-268				600	350
2018/05/03 00:00:00	1	SA1	11							2018/05/02 13:44:34	-268				600	350
...

C	SETP.WORLD	DVD_RESERVE	AEMO	PUBLIC	2018/06/07	02:25:04	0000000295553477		0000000295553313
I	RESERVE_DATA	RESERVE	1	SETTLEMENTDATE	VERSIONNO	REGIONID	PERIODID	LOWER5MIN	LOWER60SEC	LOWER6SEC	RAISE5MIN	RAISE60SEC	RAISE6SEC	LASTCHANGED	PASARESERVE	LOADREJECTIONRESERVEREQ	RAISEREG	LOWERREG	LOR1LEVEL	LOR2LEVEL
D	RESERVE_DATA	RESERVE	1	2018/05/03 00:00:00	1	SA1	1							2018/05/02 13:44:34	-268				600	350
D	RESERVE_DATA	RESERVE	1	2018/05/03 00:00:00	1	SA1	2							2018/05/02 13:44:34	-268				600	350
D	RESERVE_DATA	RESERVE	1	2018/05/03 00:00:00	1	SA1	3							2018/05/02 13:44:34	-268				600	350
D	RESERVE_DATA	RESERVE	1	2018/05/03 00:00:00	1	SA1	4							2018/05/02 13:44:34	-268				600	350
D	RESERVE_DATA	RESERVE	1	2018/05/03 00:00:00	1	SA1	5							2018/05/02 13:44:34	-268				600	350
D	RESERVE_DATA	RESERVE	1	2018/05/03 00:00:00	1	SA1	6							2018/05/02 13:44:34	-268				600	350
D	RESERVE_DATA	RESERVE	1	2018/05/03 00:00:00	1	SA1	7							2018/05/02 13:44:34	-268				600	350
D	RESERVE_DATA	RESERVE	1	2018/05/03 00:00:00	1	SA1	8							2018/05/02 13:44:34	-268				600	350
D	RESERVE_DATA	RESERVE	1	2018/05/03 00:00:00	1	SA1	9							2018/05/02 13:44:34	-268				600	350
D	RESERVE_DATA	RESERVE	1	2018/05/03 00:00:00	1	SA1	10							2018/05/02 13:44:34	-268				600	350
D	RESERVE_DATA	RESERVE	1	2018/05/03 00:00:00	1	SA1	11							2018/05/02 13:44:34	-268				600	350
...
C	END OF REPORT	99

SETTLEMENTDATE,VERSIONNO,REGIONID,PERIODID,LOWER5MIN,LOWER60SEC,LOWER6SEC,RAISE5MIN,RAISE60SEC,RAISE6SEC,LASTCHANGED,PASARESERVE,LOADREJECTIONRESERVEREQ,RAISEREG,LOWERREG,LOR1LEVEL,LOR2LEVEL
"2018/05/03 00:00:00",1,SA1,1,,,,,,,"2018/05/02 13:44:34",-268,,,,600,350
"2018/05/03 00:00:00",1,SA1,2,,,,,,,"2018/05/02 13:44:34",-268,,,,600,350
"2018/05/03 00:00:00",1,SA1,3,,,,,,,"2018/05/02 13:44:34",-268,,,,600,350
"2018/05/03 00:00:00",1,SA1,4,,,,,,,"2018/05/02 13:44:34",-268,,,,600,350
"2018/05/03 00:00:00",1,SA1,5,,,,,,,"2018/05/02 13:44:34",-268,,,,600,350
"2018/05/03 00:00:00",1,SA1,6,,,,,,,"2018/05/02 13:44:34",-268,,,,600,350
"2018/05/03 00:00:00",1,SA1,7,,,,,,,"2018/05/02 13:44:34",-268,,,,600,350
"2018/05/03 00:00:00",1,SA1,8,,,,,,,"2018/05/02 13:44:34",-268,,,,600,350
"2018/05/03 00:00:00",1,SA1,9,,,,,,,"2018/05/02 13:44:34",-268,,,,600,350
"2018/05/03 00:00:00",1,SA1,10,,,,,,,"2018/05/02 13:44:34",-268,,,,600,350
"2018/05/03 00:00:00",1,SA1,11,,,,,,,"2018/05/02 13:44:34",-268,,,,600,350
...

Raw Text with Metadata

C,SETP.WORLD,DVD_RESERVE,AEMO,PUBLIC,2018/06/07,02:25:04,0000000295553477,,0000000295553313
I,RESERVE_DATA,RESERVE,1,SETTLEMENTDATE,VERSIONNO,REGIONID,PERIODID,LOWER5MIN,LOWER60SEC,LOWER6SEC,RAISE5MIN,RAISE60SEC,RAISE6SEC,LASTCHANGED,PASARESERVE,LOADREJECTIONRESERVEREQ,RAISEREG,LOWERREG,LOR1LEVEL,LOR2LEVEL
D,RESERVE_DATA,RESERVE,1,"2018/05/03 00:00:00",1,SA1,1,,,,,,,"2018/05/02 13:44:34",-268,,,,600,350
D,RESERVE_DATA,RESERVE,1,"2018/05/03 00:00:00",1,SA1,2,,,,,,,"2018/05/02 13:44:34",-268,,,,600,350
D,RESERVE_DATA,RESERVE,1,"2018/05/03 00:00:00",1,SA1,3,,,,,,,"2018/05/02 13:44:34",-268,,,,600,350
D,RESERVE_DATA,RESERVE,1,"2018/05/03 00:00:00",1,SA1,4,,,,,,,"2018/05/02 13:44:34",-268,,,,600,350
D,RESERVE_DATA,RESERVE,1,"2018/05/03 00:00:00",1,SA1,5,,,,,,,"2018/05/02 13:44:34",-268,,,,600,350
D,RESERVE_DATA,RESERVE,1,"2018/05/03 00:00:00",1,SA1,6,,,,,,,"2018/05/02 13:44:34",-268,,,,600,350
D,RESERVE_DATA,RESERVE,1,"2018/05/03 00:00:00",1,SA1,7,,,,,,,"2018/05/02 13:44:34",-268,,,,600,350
D,RESERVE_DATA,RESERVE,1,"2018/05/03 00:00:00",1,SA1,8,,,,,,,"2018/05/02 13:44:34",-268,,,,600,350
D,RESERVE_DATA,RESERVE,1,"2018/05/03 00:00:00",1,SA1,9,,,,,,,"2018/05/02 13:44:34",-268,,,,600,350
D,RESERVE_DATA,RESERVE,1,"2018/05/03 00:00:00",1,SA1,10,,,,,,,"2018/05/02 13:44:34",-268,,,,600,350
D,RESERVE_DATA,RESERVE,1,"2018/05/03 00:00:00",1,SA1,11,,,,,,,"2018/05/02 13:44:34",-268,,,,600,350
...
C,"END OF REPORT",99

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).

The earliest available data is month 1 of 2015. The most recent available data is month 5 of 2018.

Warning

There are some months with no files.

Data Size

Each year of data is approximately 8 KB when zipped, 84 KB as unzipped CSV.

Deduplication and PDR Loader Configuration

The primary keys are:

PERIODID
REGIONID
SETTLEMENTDATE
VERSIONNO

The transaction type is "INSERT-UPDATE", 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
RESERVE_DATA	RESERVE	1

This means that the CSV files start with metadata columns:

I,RESERVE_DATA,RESERVE,1,...
D,RESERVE_DATA,RESERVE,1,...

ROW_FILTER_TYPE is LASTCHANGED

Indexes

LASTCHANGED

Links

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: HISTORICAL TABLES