Home » How does a gigawatt of renewables disappear?

How does a gigawatt of renewables disappear?

by Jack Simpson

One of the fun things about playing with hundreds of millions of rows of energy market data is that you can just go hunting through it for interesting anomalies. I recently found a fascinating anomaly exploring variable renewable energy (VRE – wind/solar farms) generation in Victoria on the 16th of November 2020

The figure below shows the combined output of every wind and solar farm in the state on a five minute basis between 11:30am and 3:30pm.

In 10 minutes, around 1 GW of renewable generation vanished for an hour, then reappeared over a 15 minute period.

So what happened? Initially I thought that some big weather event had occurred – maybe there was a huge amount of cloud cover over the state at the same time that the wind really dropped off.

Then I pulled in the dispatch prices for the state over the same period and it made sense.

They were curtailing because the market price floor of -$1,000 was hit! This means that the stations would have to pay to generate power (which they clearly do not want to do).

There’s still a lot more interesting analysis I could do with this data:

  • I could look into the dispatch targets of the units and whether this recent rule change would have made a difference
  • It’s also worth considering whether the switch to five minute settlement will reduce the length of time the generators voluntarily curtail

However, for now I think this highlights some of the fascinating pricing dynamics ocurring in the National Electricity Market (NEM). When analysing these huge datasets, it’s worth considering whether generator output is being driven by the weather, or is actually responding to market prices.

Addendum: I had a request to include the 4-second interval frequency data from this event. It looks like the frequency was creeping up leading up to 1:30pm, and then dropped once the curtailment occurred.

The following two tabs change content below.
Currently fascinated by energy markets and electrical engineering. In another life I was a beekeeper that did a PhD in computational biology writing image analysis software and using machine learning to quantify honeybee behaviour in the hive.

You may also like