DISPATCHCASESOLUTION_BNC
DISPATCHCASESOLUTION_BNC was discontinued on 30 September 2009. Prior: DISPATCHCASESOLUTION_BNC is the key data to indicate when a binding intra-regional network constraints (BNC) re-run actually occurred.
Deprecation Warning
This table is in the HISTORICAL TABLES package, so is likely deprecated.
DISPATCHCASESOLUTION_BNC was discontinued on 30 September 2009.
In accordance with the "Arrangements for Managing Risks Associated with Transmission Network Congestion" set of rule changes the Dispatch Binding Network Constraints re-run was discontinued on September 30, 2009.
Source
The occurrences of Over-constrained dispatch (OCD) or binding intra-regional network constraints (BNC) re-runs are ad hoc, with significant dependencies on the configuration or events in the physical power system. Potentially updated every 5 minutes.
Volume
Rows per day: ~72
Mb per month: 25% of
DISPATCHCASESOLUTIONThe estimates on the number of rows are based on a 25% occurrence rate for BNC runs.
Note
Due to the close dependency with the dispatch process, the OCD and BNC data models use a “CaseSolution” key table in the same manner as
DISPATCHCASESOLUTION.
Column Schema
INTERVENTION
Manual intervention flag
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.
SETTLEMENTDATE
End date and time of the dispatch interval
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: 2026/01/20 00:05:00
RUNNO
Dispatch run no; always 1
This column only ever takes one value: 1
(The SQL type is NUMBER(3,0).)
AEMO marked this column as mandatory, and a primary key. However, since it only ever takes one value, you can ignore it.
CASESUBTYPE
always BNC
SQL Type: VARCHAR2(3)
Polars Type: pl.Categorical
This column is not mandatory.
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: 2026/01/20 00:05:00
NONPHYSICALLOSSES
Non-Physical Losses algorithm invoked occurred during this run
SQL Type: NUMBER(1,0)
Polars Type: pl.Int8
This column is not mandatory.
SOLUTIONSTATUS
If non-zero indicated one of the following conditions: * 1 = Supply Scarcity, Excess generation or constraint violations * X = Model failure
SQL Type: NUMBER(2,0)
Polars Type: pl.Int8
This column is not mandatory.
SPDVERSION
Current version of SPD
SQL Type: NUMBER(10,3)
Polars Type: pl.Float64
This column is not mandatory.
STARTPERIOD
Period identifier of first interval of the case (yyyymmddppp)
SQL Type: VARCHAR2(20)
Polars Type: str
This column is not mandatory.
TOTAL5MINVIOLATION
Total of 5 minute ancillary service region violations
SQL Type: NUMBER(15,5)
Polars Type: pl.Float64
This column is not mandatory.
TOTAL6SECVIOLATION
Total of 6 second ancillary service region violations
SQL Type: NUMBER(15,5)
Polars Type: pl.Float64
This column is not mandatory.
TOTAL60SECVIOLATION
Total of 60 second ancillary service region violations
SQL Type: NUMBER(15,5)
Polars Type: pl.Float64
This column is not mandatory.
TOTALAREAGENVIOLATION
Total Region Demand violations
SQL Type: NUMBER(15,5)
Polars Type: pl.Float64
This column is not mandatory.
TOTALASPROFILEVIOLATION
Total of ancillary service trader profile violations
SQL Type: NUMBER(15,5)
Polars Type: pl.Float64
This column is not mandatory.
TOTALENERGYCONSTRVIOLATION
SQL Type: NUMBER(15,5)
Polars Type: pl.Float64
This column is not mandatory.
TOTALENERGYOFFERVIOLATION
Total of unit summated offer band violations
SQL Type: NUMBER(15,5)
Polars Type: pl.Float64
This column is not mandatory.
TOTALFASTSTARTVIOLATION
Total of fast start trader profile violations
SQL Type: NUMBER(15,5)
Polars Type: pl.Float64
This column is not mandatory.
TOTALGENERICVIOLATION
Total generic constraint violations
SQL Type: NUMBER(15,5)
Polars Type: pl.Float64
This column is not mandatory.
TOTALINTERCONNECTORVIOLATION
Total interconnector violations
SQL Type: NUMBER(15,5)
Polars Type: pl.Float64
This column is not mandatory.
TOTALOBJECTIVE
The Objective function from the LP
SQL Type: NUMBER(27,10)
Polars Type: pl.Float64
This column is not mandatory.
TOTALRAMPRATEVIOLATION
Total ramp rate violations
SQL Type: NUMBER(15,5)
Polars Type: pl.Float64
This column is not mandatory.
TOTALREGVIOLATION
Total of Regulation ancillary service region violations
SQL Type: NUMBER(15,5)
Polars Type: pl.Float64
This column is not mandatory.
TOTALUNITMWCAPACITYVIOLATION
Total unit capacity violations
SQL Type: NUMBER(15,5)
Polars Type: pl.Float64
This column is not mandatory.
New Data Timing
Adhoc, potentially 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", "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(
{
"INTERVENTION": pl.Boolean,
"RUNNO": pl.Enum(["1"]),
"CASESUBTYPE": pl.Categorical,
"NONPHYSICALLOSSES": int, # or pl.Int8
"SOLUTIONSTATUS": int, # or pl.Int8
"SPDVERSION": pl.Float64,
"STARTPERIOD": str,
"TOTAL5MINVIOLATION": pl.Float64,
"TOTAL6SECVIOLATION": pl.Float64,
"TOTAL60SECVIOLATION": pl.Float64,
"TOTALAREAGENVIOLATION": pl.Float64,
"TOTALASPROFILEVIOLATION": pl.Float64,
"TOTALENERGYCONSTRVIOLATION": pl.Float64,
"TOTALENERGYOFFERVIOLATION": pl.Float64,
"TOTALFASTSTARTVIOLATION": pl.Float64,
"TOTALGENERICVIOLATION": pl.Float64,
"TOTALINTERCONNECTORVIOLATION": pl.Float64,
"TOTALOBJECTIVE": pl.Float64,
"TOTALRAMPRATEVIOLATION": pl.Float64,
"TOTALREGVIOLATION": pl.Float64,
"TOTALUNITMWCAPACITYVIOLATION": pl.Float64,
}
)
# Drop deprecated/unused columns
.drop("RUNNO")
# Deduplicate
# We want one row per ['INTERVENTION', 'RUNNO', 'SETTLEMENTDATE']
# Tie-breaker: greatest value of ['LASTCHANGED']
.sort(["LASTCHANGED"], descending=False)
.unique(subset=["INTERVENTION", "RUNNO", "SETTLEMENTDATE"], keep="last")
)
Nemosis
This table is not yet available in Nemosis.
Availability
AEMO's documentation states that this table's visibility is "Public". However, it is not available in the public monthly MMSDM archive. AEMO probably call this public because it is available to all market participants over private FTP.
There is a chance that is is still public to non-participants. It may only be available for some months; or (rarely) it may be available in a non-standard location.
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.
| Report Name | Report Subtype | Version |
|---|---|---|
| DISPATCHBNC | CASESOLUTION | 1 |
This means that the CSV files start with metadata columns:
Indexes
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