We don’t have time to delve into detail, but the arrival of the ‘next day public’ data set allows us to use the ez2view ‘Trends Engine’ to present this summary chart of trended Dispatch Error aggregated by fuel type across the NEM through this period:
I’ve highlighted on the trend of 288 x Dispatch Intervals through the day the period of interest – featuring both:
Paul was one of the founders of Global-Roam in February 2000. He is currently the CEO of the company and the principal author of WattClarity. Writing for WattClarity has become a natural extension of his work in understanding the electricity market, enabling him to lead the team in developing better software for clients.
Before co-founding the company, Paul worked as a Mechanical Engineer for the Queensland Electricity Commission in the early 1990s. He also gained international experience in Japan, the United States, Canada, the UK, and Argentina as part of his ES Cornwall Memorial Scholarship.
This is the 2nd of two articles today … this one specifically focused on Wind (i.e. the Semi-Scheduled units) and the aggregate Dispatch Error across all 288 dispatch intervals on Tuesday 27th January 2026.
A first look at a frequency spike (outside the NOFB) focused in the 10:30 (excursion) and 10:35 (recovery) dispatch intervals NEM time on Friday 10th April 2026.
2 Commentson "Aggregate Dispatch Error by Fuel Type for Tuesday 15th September 2026"
Correct – just as the first band crossed, I was actually talking to some people about the challenges of forecasting short-duration fluctuations of rooftop PV output (and hence system demand) in high-density metropolitan areas. Events like this are entirely predictable, but quite hard to forecast.
I live in Melbourne, and this was likely caused by 2, very defined bands of rain passing over metropolitan Melbourne around that time.
Correct – just as the first band crossed, I was actually talking to some people about the challenges of forecasting short-duration fluctuations of rooftop PV output (and hence system demand) in high-density metropolitan areas. Events like this are entirely predictable, but quite hard to forecast.