I thought I’d share three interesting pieces of energy-related content that I’ve come across recently.
1. Some incredible footage of the engineering behind a hydro turbine:
How the Turbines in the Kölnbrein Dam are 92% Efficient
If you’ve ever wanted to see the impact that machine learning is having in the energy sector, then I recommend watching this seminar released by the National Renewable Energy Laboratory (NREL).
Each talk describes an application of machine learning in the industry at different levels, from the big (weather and climate modelling) through to the small (optimising the aerodynamics of turbine blades).
Some of the topics discussed include:
Most people know that I’m a huge fan of the Python programming language – while that isn’t going to change, a recent encounter with some researchers at CSIRO has convinced me that I should pick up Julia for some of the energy modelling and optimisation work that I do.
I’ve known for a while that Julia was a language with a lot of benefits (as fast as a lower-level language but with the productivity benefits of a higher-level language). However, if you understand how to write efficient vectorised code in Python (using NumPy and Pandas), then except for some use-cases, you don’t really get that much of a boost out of switching to Julia.
So what changed my mind? Well, for the past few years, the National Renewable Energy Laboratory (NREL) have been working on a number of amazing open-source energy modelling packages for the Julia programming language. I’ve now updated my electricity modelling resources page with the links to some material about these packages.
A new podcast called Windfall has just launched, covering the engineering, economics, and politics of offshore wind farms in the USA.
It is being produced by the Outside/In podcast team, whom I’ve been a big fan of for the past few years.
I just finished listening to the first episode, and can thoroughly recommend it!
One of the amazing things about working in the energy industry is the sheer volume of reasonably well-structured datasets that are available. The real limitation when working with this data is the challenge of acquiring enough domain-expertise to know how to unlock its full potential.
Following on from my previous post, I’ve written this article to document some of the resources I’ve found helpful on my own journey. Most (aside from the physical textbooks) are freely available online, and I intend to continue to update and tweak the resources listed below in the future.
One of my favourite data science resources is the mini-episode series of the Data Skeptic podcast. These short episodes would feature the host explaining a data science concept to a non-expert in plain English.
I wanted to share a few of these with some colleagues from work and thought I’d catalogue them here.
On the 25th of May, an explosion and fire at Callide Power Station initiated a series of events that culminated in blackouts affecting hundreds of thousands of Queenslanders.
The figure below shows the coal power station units at Callide, Stanwell, and Gladstone tripping as this event unfolded between 1:45-2:10pm.
However, when I examined the two Gladstone units that did not trip, I saw something rather interesting. As you can see in the figures below, in response to a sharp drop in system frequency, both units momentarily boosted their output.
AEMO’s Quarterly Energy Dynamics (QED) report just came out today for Q1 2021. Of particular interest is the South Australian region which has been experiencing incredibly low prices thanks to the high levels of renewables in the market.
At the same time, these low prices have created system security challenges for the market operator. In fact over the past quarter, AEMO has had to intervene in the market a record 70% of the time to ensure that at least one gas powered generator remained running.
Back in 2017, I had the amazing opportunity to be one of 20 young Australians selected by Startup Catalyst to receive a scholarship for their Future Founders Mission to Silicon Valley. On this trip I had the opportunity to visit dozens of tech companies including Google, Facebook, Twitter, etc and learn from senior people within these organisations. At the time I was constantly jotting down little notes, and I thought I’d take some of them and write them up as a post now.