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Jack Simpson

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.

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What is the Retailer Reliability Obligation (RRO)?

by Jack Simpson
written by Jack Simpson

Commencing in 2019, the Retailer Reliability Obligation (RRO) requires energy retailers (plus some large energy users) to support reliability in the National Electricity Market (NEM) by holding contracts or investing directly in generation/demand response.

The RRO is exercised through two instruments:

RRO T-3 Instrument

  • Triggered 3 years and 3 months before an identified gap to the reliability standard
  • AEMO forecasts breaches to the the reliability standard as part of their Long Term Projected Assessment of System Adequacy (LT PASA) modelling which they publish in their annual Electricity Statement of Opportunities (ESOO) report
  • If a breach is forecast, retailers need to increase their contracting with existing generators and/or unlock new investment

RRO T-1 Instrument

  • Triggered 1 year and 3 months before an identified gap
  • AEMO forecasts breaches to the reliability standard as part of their Medium Term Projected Assessment of System Adequacy (MT PASA) modelling
  • If a breach of the standard is forecast, liable entities must report their net contract position to the Australian Energy Regulator (AER) for assessment

Sources

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The future of energy storage, storage hybrids, and solar curtailment presentations

by Jack Simpson
written by Jack Simpson

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:

  • Operating reserve
  • Peak capacity
  • Diurnal capacity and energy time shifting
  • Multiday to seasonal capacity and energy time shifting
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AER State of the energy market 2021 Summary

by Jack Simpson
written by Jack Simpson

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:

  • In 2020:
    • 3,700 MW of of large-scale wind and solar generation capacity entered the National Electricity Market (NEM)
    • There was a new record for rooftop solar with almost 2,500 MW of capacity installed across the NEM
    • 70% of electricity in the NEM is generated by coal/gas units
  • Over the next 20 years:
    • 16 GW of thermal generation is expected to retire
    • 26-50 GW of of new large scale wind and solar are forecast to come online
    • 13-25 GW of rooftop solar is expected to be installed
  • Challenges
    • The impact of weather on generation requires investment in firming capacity, including:
      • Fast-start generation
      • Demand response
      • Battery storage
    • The inertia and system strength that coal/gas units provided to stablise the grid is declining as they continue to exit the market. This has led to shortages in multiple states in 2020, constraining the operation and connection of renewable units. There was also a record number of directions issued to power stations by AEMO to maintain system security.
    • Minimum demand in the middle of the day (due to rooftop solar) remains a significant challenge
    • Work continues on the renewable energy zones (REZs) initiative – the idea is to cluster new wind and solar projects in hubs so that it is easier to invest in transmission
    • Governments continue to intervene in the market e.g. big batteries, gas peakers
    • Electricity prices fell significantly throughout the NEM in 2020

If you have the time, it’s worth reading the full report, including the analysis of the gas markets.

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ContentData ScienceEnergyLinksMachine LearningResearch

Excellent seminar on the applications of machine learning in the energy sector

by Jack Simpson
written by Jack Simpson

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:

  • How researchers at NREL are using generative adversarial networks (GANs) to assist them with weather and climate modelling
  • How you can represent a wind farm as a graph neural network (GNN) with directed edges (this is brilliant!)
  • How hard it is to acquire enough data to train models for wind farms (this is why they mention having success with ensemble-based modelling approaches)
  • How they’ve been creating simulations to augment their wind farm datasets
  • A few key points which I agree with from personal experience
    • Features matter more than models – having enough input data, processed in the right way often matters more than the specific machine learning algorithm you’re using
    • Training models is expensive and time consuming, but once that stage is done, you can run them cheaply and quickly in production
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Learning to program in Julia (for energy modelling)

by Jack Simpson
written by Jack Simpson

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.

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Electricity Market Modelling Resources

by Jack Simpson
written by Jack Simpson

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.

Energy Markets

Energy Modelling

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Simple explanations of data science and statistics concepts

by Jack Simpson
written by Jack Simpson

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.

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ContentEnergyFeaturedVisualisations

Primary frequency response in action!

by Jack Simpson
written by Jack Simpson

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.

Power station units are in MW (left Y axis) while the system frequency units are in Hz (right Y axis).

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.

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