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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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ContentEnergyExplanations

What is the Reliability Standard and Settings?

by Jack Simpson
written by Jack Simpson

The reliability standard is the threshold used to determine whether there will be enough future capacity in the National Electricity Market (NEM) to meet demand. A breach of the standard (based on the LT and MT PASA forecasts that AEMO conducts) signals to investors that there will be a supply/demand imbalance.

Every four years the Reliability Panel will review the reliability standard and settings in the NEM.

Reliability Standard

The reliability standard is used to determine whether there is sufficient capacity within the NEM to meet demand.

It is defined as the proportion of expected unserved energy (USE – measured in GWh) relative to the total demand in a region over a financial year. The standard is currently set at 0.002% – this means that 99.998% of forecast annual customer demand is to be met.

It is worth noting that:

  • The standard only considers the impact of credible contingencies (e.g. generators, interconnectors, transmission)
  • Distribution reliability isn’t counted
  • It’s possible that a combination of weather events in a particular year could lead to a higher than expected level of load shedding as the standard is defined in terms of the maximum expected unserved energy
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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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ContentEnergyExplanations

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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BloggingContentEnergyLinks

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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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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julia programming
ContentData ScienceLinksProgrammingResources

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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