NEM timestamps: AEST, period-ending, and the 5MS transition

The NEM market clock

The National Electricity Market operates on Australian Eastern Standard Time (AEST), UTC+10, year-round. This is not the same as the wall-clock time in Sydney or Melbourne, which advance one hour to AEDT (UTC+11) during Daylight Saving Time (October to April each year).

The legal basis is NER clause 2.2.6. Using wall-clock time for any AEMO data analysis is a common error: every summer timestamp will be parsed one hour ahead of the file clock, and timestamps at the DST transition boundaries will become ambiguous or NA.

The aemo package uses "Australia/Brisbane" as the timezone for all data. Queensland observes AEST year-round (no DST), which gives the correct fixed UTC+10 offset:

library(aemo)

# All timestamps come back in AEST (UTC+10, no DST).
# The tzone attribute is "Australia/Brisbane" throughout.
p <- aemo_price("NSW1", "2024-01-15 14:00", "2024-01-15 15:00")
attr(p$settlementdate, "tzone")  # "Australia/Brisbane"

# January is summer in Australia. Australia/Sydney would give UTC+11 here,
# shifting every timestamp one hour ahead of the AEMO file clock.
# Australia/Brisbane correctly gives UTC+10.
format(p$settlementdate[1], "%Y-%m-%d %H:%M:%S %Z")

To display times in local wall-clock time for a specific state, convert explicitly:

# Convert market time to NSW wall clock (observes DST)
p$local_time_nsw <- format(p$settlementdate, tz = "Australia/Sydney")

# Convert market time to SA wall clock (UTC+9:30 / UTC+10:30 with DST)
p$local_time_sa <- format(p$settlementdate, tz = "Australia/Adelaide")

Period-ending timestamps

Every interval in a NEMweb file is labelled with its period-ending timestamp. A row with settlementdate = "2024-01-15 14:05:00" covers the 5-minute dispatch interval from 14:00:01 to 14:05:00 (inclusive).

This is the AEMO convention throughout: the timestamp marks when the interval closes, not when it opens. For 30-minute trading intervals the same rule applies: a row stamped 14:30:00 covers 14:00:01 to 14:30:00.

When computing time-of-day statistics (e.g. peak demand by hour) use the interval end-time directly. When joining to external time series that use period-start timestamps, subtract the interval length:

# Period-start timestamps for 5-min intervals
p$period_start <- p$settlementdate - as.difftime(5, units = "mins")

# Period-start timestamps for 30-min trading intervals
t$period_start <- t$settlementdate - as.difftime(30, units = "mins")

The 5-minute settlement transition (1 October 2021)

Before 1 October 2021 the NEM settled electricity on 30-minute trading intervals using TRADINGPRICE from the TRADINGIS reports. On that date five-minute settlement (5MS) commenced under NER clause 3.15.1A, and settlement moved to native 5-minute dispatch prices from DISPATCHPRICE.

Practical implications:

Query What aemo_price() returns
interval = "5min", any date Native 5-min dispatch price from DISPATCHPRICE (DISPATCHIS)
interval = "30min", date before 2021-10-01 30-min TRADINGPRICE from TRADINGIS
interval = "30min", date from 2021-10-01 Arithmetic mean of six 5-min dispatch prices within the trading interval
interval = "30min", spanning 2021-10-01 Pre-5MS portion from TRADINGIS; post-5MS aggregated from 5-min

When constructing long historical 30-minute price series that span the transition, the aggregation method matches: both the pre-5MS TRADINGPRICE and the post-5MS arithmetic mean are simple unweighted averages of the six dispatch prices within the trading interval (pre-5MS dispatch prices were held constant at the trading-interval price; post-5MS they vary).

Intervention runs

DISPATCHPRICE contains two types of dispatch runs in every file:

All aemo data functions default to intervention = FALSE, which filters to market-pricing rows only. Pass intervention = TRUE when you need to analyse the intervention pricing itself (e.g. the cost of directions during a suspension period).

# Default: market pricing run only
p_market <- aemo_price("NSW1", "2022-06-13", "2022-06-14")

# Both runs (for intervention analysis)
p_both <- aemo_price("NSW1", "2022-06-13", "2022-06-14",
                     intervention = TRUE)
table(p_both$intervention)