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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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ContentEnergyFeaturedVisualisations

Big Battery Bidding and Rebidding!

by Jack Simpson
written by Jack Simpson

In the National Electricity Market (NEM), power stations can bid in their availability in 10 different pricing tranches. They’re able to rebid this availability right up until dispatch is run.

Recently, I was exploring this bidding data and thought I’d take a peak at what the bidding behaviour of the Hornsdale (Tesla) battery looked like in the energy market.

I’d known for a while that the big batteries did a lot of rebidding, but I was blown away by just how frequent it was! In a single day (2019-01-01) the battery made almost 13,500 rebids! In fact, for the first interval of the day, the battery made almost 300 rebids in the week leading up to dispatch.

I do find it interesting how they appeared to be doing so many identical rebids – I suspect there must be some automated process going on that will rebid in all markets regardless of whether the rebid in a particular market is any different to the bid that it replaced.

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ContentEnergyFeaturedVisualisations

How does a gigawatt of renewables disappear?

by Jack Simpson
written 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.

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ContentData ScienceFeaturedProgrammingTips & Tutorials

Data science tip: store constants in their own file

by Jack Simpson
written by Jack Simpson

Sometimes when you work on large projects, you can end up with code that looks like this:

if num_values < 15:
    do_thing()

Now, what does the 15 mean? Well, I’m sure you would have known when you first wrote the code, but what happens in 3 months when you try to modify it or pass it on to someone else in your team? Also, what happens if you determine that the number should actually be 16? Now you have to go through all your files and swap numbers in all the right places (and if you miss any you can introduce subtle bugs).

So what’s the solution? Well, what you really need is a single source of truth for your clearly defined constants, which you can:

  • Easily look up
  • Modify/update
  • Import into other code

Python makes this really easy – all you have to do is create a “constants.py” file in your project directory (I suppose you could call it whatever you like, but that’s beside the point). From here, you can import your variables as if you were importing a library:

from constants import NOM_FREQ_HZ, VALUES_IN_MIN

A screenshot of this file from a recent project looks a little like this:

constants code file

This approach helped me write much more readable code, when working with an extremely complicated dataset where numbers were used to represent different value categories.

Obviously for smaller scripts it might not be worth setting up a dedicated file, but for this project, my constants file ended up being a couple of hundred lines long, and is far easier to maintain than if they were defined multiple times across all the files in the project.

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Summary of reliability forecasting in the NEM

by Jack Simpson
written by Jack Simpson

I’ve written this article as a guide to the different reliability forecasts that AEMO undertakes in the National Electricity Market (NEM). These forecasts help AEMO identify supply shortfalls, and plan for the future. For example, AEMO’s annual Summer Readiness Plan draws on the outputs of most of the models described here.

This article is under active revision. My plan is to continue to tweak and expand on this content as I continue to learn more about these models. At the moment, this article covers the following topics:

  • LT PASA
  • MT PASA
  • ST PASA
  • EAAP

Long Term Projected Assessment of System Adequacy (LT PASA)

Purpose
  • Published annually looking out 10 years as part of the Electricity Statement of Opportunities (ESOO)
  • The forecast is used to identify reliability gaps in the first five years (which helps inform the RRO)
  • The final five years indicates whether there will be any future gaps in reliability
  • Informs the market on the need for new investment
  • Helps AEMO determine whether it needs to procure additional reserves
Inputs
  • Demand traces
    • Two scenarios: Probability of Exceedance (PoE) 10 and 50 (expect to exceed every 10 or 2 years)
  • Weather traces – 8 historic weather patterns:Wind/solar generation traces
    • Rooftop solar generation traces
    • Generator ratings and forced outages
  • Demand-side participation (DSP)
  • Transmission constraint equations (e.g. transmission line thermal ratings)
Outputs
  • ESOO report flagging breaches to the reliability standard
  • The ESOO Plexos model and input traces is available for download from AEMO’s site
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Hornsdale Power Reserve
ContentEnergyFeaturedVisualisations

Visualising the Tesla big battery at Hornsdale

by Jack Simpson
written by Jack Simpson

Lately I’ve been working with AEMO’s 4-second interval data from the National Electricity Market (NEM). Visualising the grid at this resolution is so fascinating, and I wanted to share a couple of figures I’d generated looking at the Hornsdale Power Reserve (the big Tesla battery). The figures have also raised a couple of questions for me which I’ve recorded below.

In all the figures, the battery has no dispatch target – rather it is responding to the automatic generation control (AGC) regulation instructions. In the figures:

  • Left y axis:
    • MW is what the battery was doing
      • Positive values mean the battery is discharging
      • Negative values mean the battery is recharging
    • Target Ramp is the dispatch target of the generator
    • Target Ramp + Reg adds in the regulation component to the target
  • Right y axis:
    • Hz is the mainland system frequency
2020-08-08 09:00-09:30
2020-08-08 23:00-23:30
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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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ContentEnergyFeaturedLinksResources

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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ContentData ScienceFeaturedLinksMachine LearningResourcesStatistics

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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