I thought I’d share a few interesting presentations released by the National Renewable Energy Laboratory (NREL).
1. A presentation outlining four phases of energy storage deployment, where storage provides:
The Australian Energy Regulator has just released their State of the energy market 2021 report – there’s a lot of content in it, so I’ve tried to summarise a few points pulled from the report below:
If you have the time, it’s worth reading the full report, including the analysis of the gas markets.
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:
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.
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.
In July 2020, Melbourne entered its second COVID-19 lockdown – a lockdown which would not be lifted until late October. I thought it would be rather interesting to visualise the impact of this event on the number of PV system installations in each postcode of the city. These figures are based on the data provided by the Clean Energy Regulator which I merged with the spatial postcode data provided by the Australian Bureau of Statistics.
Here’s how things looked in the months leading up to the lockdown – you can see how a few postcodes (in particular 3064 which appears to encompass Roxburgh Park) were having so many PV systems installed, I actually had to cap the maximum value of my heatmap at 300 so you could compare it to other postcodes in the figure.
This article will walk you through the construction of an extremely simple dispatch engine programmed in GMPL. While my preferred language is Python, GLPK lets us focus exclusively on the equations behind the dispatch engine, enabling us to understand the basics of how dispatch works in the energy market.
So what is a dispatch engine? It is a program that allows us to figure out the optimial volume of power that different power stations should dispatch into the grid. As power is instantly produced and consumed, the total volume of power generated must equal demand at any time. Given that power stations sell different volumes of power at varying prices, the purpose of a dispatch engine is to identify how much each power station should generate to meet demand, while minimizing the total cost to supply that power.
Another important detail is that all power stations are ultimately paid the same price for the power they generate within a given interval – the most expensive power station called on to dispatch in order to satisfy demand effectively becomes the ‘price setter’.
Our dispatch engine is based on the technique of linear optimization – you can find out more background on the technique here if you’re interested.
Install GLPK (GNU Linear Programming Kit) – you can do this with the following command once you have Anaconda installed:
conda install -c conda-forge glpkWe’re going to start by setting out all the parameters for our optimization: