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Exploring the impact of constraints on a solar farm in the National Electricity Market

by Jack Simpson
written by Jack Simpson

Earlier this week I came across an article about how New England Solar was planning to sell off its solar farms in Australia due to the impact of network congestion:

New Energy Solar’s CEO Liam Thomas said a flood of new projects had resulted in curtailment challenges spreading to other parts of the grid and cut the output of the Manildra solar farm by 40 per cent just as the company was looking for a buyer.

I thought that it would be interesting to dig into some of AEMO’s constraints data in order to investigate exactly how Manildra Solar Farm has been impacted by network congestion in FY-21.

Interestingly, it appears that the vast majority of the time a constraint impacted this solar farm, it could be attributed to a single factor: the 132 KV Molong to Orange North transmission line which you can see in the map below. This transmission line has a thermal limit that determines how much power can flow through it from generators in the area.

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Virtual power plants, Texas ‘big freeze’ event, and grid visualisation tips video

by Jack Simpson
written by Jack Simpson

Here’s a few interesting pieces of energy-related content I’ve come across this week.

1. SolarQuotes have put up a video about their experience participating in a virtual power plant. In the video they cover:

  • How AGL was using their battery during the trial
  • Being aware of changes to feed in tariffs
  • The impact that electric vehicles can have on your consumption
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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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ContentEnergyFeaturedLinks

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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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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