Every 5 minutes, AEMO will dispatch generators across the National Electricity Market (NEM) in order to meet demand. To achieve this, AEMO needs to predict what demand will look like 5 minutes in the future.
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
Long Term Projected Assessment of System Adequacy (LT PASA)
- 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
- 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)
- ESOO report flagging breaches to the reliability standard
- The ESOO Plexos model and input traces is available for download from AEMO’s site
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.
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
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
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 glpk
We’re going to start by setting out all the parameters for our optimization:
- We’re simulating a single region with a demand target of 150 MW
- We’ll have 3 power stations – 2 coal, and 1 gas
- Coal 1 sells power at $10/MWh and has a max output of 100 MW
- Coal 2 sells power at $20/MWh and has a max output of 100 MW
- Gas 1 sells power at $100/MWh and has a max output of 50 MW