This page contains a summary of the questions and answers from Modo Energy’s live stream “The Outlook for BESS in the NEM” held on August 6th 2025. Questions have been grouped into four themes: Modelling, Long-term assumptions, Market, and BESS Investment Case.

These questions will be included within our full FAQ in our forecast methodology.

You can watch a recording of the livestream here.

Modelling

• How do you forecast price spikes, and are these backtested to historical data?
  • Price spikes are forecast within our model by designing it to take into account as many actual drivers of pricing volatility as possible. This includes: 5-minute pricing, generator ramp rates, start-up costs and minimum stable levels, transmission constraints (both between and within states), generator outages, and bid curves based on historical analysis of the ten bid bands across all technologies. The combination of all of these factors can cause price spikes outside of traditional peak demand periods. For example transmission constraints, coal outages and changes in bid behaviour were responsible for extensive price spikes in June 2025, in conditions that weren’t seen as critical on the system.
  • We have spent a lot of time backtesting at 5 minute granularity across 2023-2025 to ensure our forecast is as well-calibrated as possible, we go into our backtesting a lot more in our methodology. We have analysed our backtest in all possible cuts of the data, we have compared our average monthly prices and monthly spreads, daily price shapes, price distribution, and even spent considerable time looking at individual days to make sure we are capturing as many dynamics as possible at a settlement period level.
  • See documentation for more
• How do you forecast major and minor contingency events?
  • We don't forecast major and minor contingency events in the same way as way as some of our competitors. Volatility is fundamental to the battery business case so it is important to model it organically as part of the main power price model, instead of adding it in as post-processing. Our forecast is at 5-minute granularity and models the conditions that lead to volatility, as opposed to just adding volatility in. This has all been tuned with thorough backtesting as can been seen in our methodology.
  • Volatility occurs in every single year to a certain extent, so to ensure we can capture volatility in the future, we have chosen a weather year to be used for solar/wind/demand as well as for generation and transmission outages.
  • We will be looking at the impact of events such as these within our model within scenario modelling, as part of our research offering.
• How do you consider weather years?
  • We model using the 2023 weather year. This was after an evaluation of all possible options (analysis of 10 weather years, alternating between weather years, synthetic weather year, stochastic weather/outages). In our view this is the best option, we go more into our reasoning in our methodology.
  • Our view is that a single weather year is the only way to ensure that the forecast is both realistic and transparent, but this does make it important to select the right weather year. After careful consideration of the past 10 years, we chose 2023 as our weather year, as this year was close to / slightly below average in terms of solar/wind traces, demand, temperature, and number of outages. This allows banks to have confidence in the level of volatility in our model as it is realistic, whilst being slightly conservative.
  • See documentation for more
• How is participant bidding modelled?
  • We use a combination of a unit commitment dispatch and price formation model, combined with “s-curves” representing the bid curve for each unit.
  • See documentation for more
• Do potential revenues consider external constraining factors such as congestion?
  • Yes, via our modelling of sub-regions and integration of fundamental model and battery dispatch model.
  • See documentation for more
• How do you map sub-regions?
  • We model transmission flows between nine major sub-regions, to capture the major constraint and pricing dynamics caused by the transmission network. The substates in our model map to substates in the ISP, or in some cases are the combination of two substates in the ISP.
  • See documentation for more
• What hurdle rate do we assume for our capacity buildout assumptions?
  • Our long-term capacity buildout relies on a 10% hurdle rate being met for new investment.
  • See documentation for more
• Does your dispatch model assume the BESS is a price taker?
  • Our power price model co-optimises all generators (including batteries) in a linear program in order to calculate the price at each 5 minute settlement period. Our battery dispatch model then takes those prices as well as the chosen configuration of battery, and optimises to maximise its own revenue.
  • For batteries 300 MW and under, the dispatch model is implicitly treating the battery as a price taker as it is given the fixed power prices, however the effect of this individual battery will already be accounted for in the power price model and the bidding logic we have there, based on our expectations of future BESS buildout. Historical analysis shows batteries of this scale can effectively capture the same returns as smaller systems in an energy-trading focused strategy.

Long-term assumptions

• What goes into your forward assumptions on Capex? What about Opex?
  • We have a house view on Capex, formed from a combination of the latest GenCost inputs and our global BESS Capex survey. This is validated against announced historic Capex and third-party resources i.e. BNEF.
  • For Opex we use latest numbers from ISP inputs, which in turn is based on analysis by Aurecon.
• How would an increase in DERs impact your forecast, especially if it increasingly participates in the market?
  • We expect DER and particularly DER storage to be a big sensitivity to the battery business case, but less so than the capacity of utility-scale storage. We currently expect most DER batteries to be non-aggregated, and even those that are aggregated aren't going to compete as aggressively as utility-scale storage.
  • The main effect DER will have on the battery business case is a reduction in volatility events as we can expect the majority of aggregated DER batteries to be discharging at these times. As for the specific impact of DER price visibility suggested in the Nelson Review, this will allow the market to operate more effectively, but we don't see this significantly changing the dynamics already mentioned.
• How will data centre growth impact your forecast?
  • We currently base our demand forecasts on the latest available AEMO ISP data, taking a combination of step change and progressive change scenarios. AEMO (based on modelling by Oxford Economics) project a 25% increase in data centre demand by 2030, representing 6% of system-wide demand.
  • There is a wide degree of uncertainty over future potential data centre demand growth causes, and this can have a large impact on future demand assumptions. We will be assessing these impacts as part of our sensitivity and scenario modelling.

Market

• Do you differentiate between global and local FCAS?
  • We don't currently differentiate between global and local FCAS. Increases in local FCAS requirement are small and with the rapid storage buildout we will quickly reach a point where even SA and QLD will be saturated on their own, meaning that frequency islanding will eventually not lead to local FCAS price spikes unless there is also an energy price spike. Additionally, we expect frequency islanding to get less and less frequent with further transmission buildout, such as Project EnergyConnect.
  • We will continue monitoring the market and local FCAS events as BESS capacity grows and will revise this assumption if necessary.
• Could FCAS value grow long-term due to an increasingly unstable grid?
  • The massive increase in battery capacity far outweighs any potential increased FCAS requirements, which is ultimately linked to physical aspects of the network i.e. potential largest loss. This is therefore only going to continue FCAS saturation. We don’t see FCAS as providing any real additional value to large batteries in the long-term.

BESS buildout and investment case