Aug 24, 2025
When batteries move the market, it’s time to rethink forecasting and operational strategies
The Australian energy market is rapidly transforming. Grid-scale battery trading and operations must keep up or risk falling behind.
Not long ago, batteries were minor players in a deep and liquid power market. Projects like Hornsdale Power Reserve (100 MW / 129 MWh), built in 2017 by Tesla and Neoen, set a bold precedent for what was possible. Rising renewable penetration, growing demand, and the realtime nature of the NEM created volatility that batteries were uniquely suited to capitalize on.
The result? Australia became the goto market for energy storage developers. Today, there are 7.8 GW of utilityscale batteries under construction across the country.
But along with this growth came another shift: the size of batteries has also increased drastically in the past few years. Projects like AGL’s 500 MW Liddell Battery and Neoen’s 560 MW Collie Battery are examples of a new era of largescale storage soon to come online.
And that changes everything.
Pricing Power of Batteries
So, what do these shifts mean for the “holy grail” of storage markets, the NEM?
Two things: market saturation and battery pricing power.
Historically — if you can use that phrase for a market only 6–7 years old — batteries in Australia operated in the shadow of large thermal fleets. They could participate in energy and frequency control ancillary service (FCAS) markets, and for the most part, enjoy the volatility.
Those days are gone.
As coal retires and renewable penetration accelerates, market elasticity has increased; meaning fewer megawatts are needed to shift prices. Meanwhile, battery capacity has surged. These assets are more often the marginal units in both energy and FCAS markets.
Today, batteries are no longer passive participants. However, most revenue models treat them as pricetakers, assuming that their bids don't move marketclearing prices. Such modeling approach forecasts prices, then schedules charge/discharge (and FCAS enablement) with simple constraints like stateofcharge and efficiency to capture volatility. This approach works when you're small relative to the stack, but it misses market feedback effects once your bids are often marginal.
As batteries scale, they stop being invisible to the market: bids from storage now frequently set prices in both energy and FCAS. When your actions move the price, pricetaker models can miscalculate optimal strategy because they ignore the feedback loop between your bids and the market’s response.
At Powerline, we believe it’s time for the industry to rethink how battery revenue forecasting and strategy modeling are done.
How often are batteries setting the price?
Powerline has analyzed market data across multiple NEM regions in the first half of 2025. We found that in average, 13% of the time batteries in fact have been setting the energy prices. What is even more interesting is that 25%-30% of those have been batteries smaller than 100MW.
Zooming in at interval-level stats, for example in VIC, the numbers are even more astonishing during peak hours.
Batteries can be the marginal price setter for up to 35% of the time during morning peak intervals, and 50% during evening peaks.
Taking one step further and breaking down the graph above by cleared battery capacity, we see that capacities as small as 5MW can play a role in market dynamics.
This trend is even more pronounced in FCAS markets, where fast responding batteries frequently dominate pricesetting intervals.
Let’s build a 500MW / 2000 MWh battery in NEM
To measure the magnitude of the pricing power of a battery of this size, we took a hypothetical 500 MW / 2,000 MWh battery and dropped it in one of the NEM regions in Powerline’s digital twin.
With a defined set of assumptions and configurations, the traditional “pricetaker” simulation, with perfect-foresight assumptions, estimated the upper bound of this battery’s revenue during the last 1.5 years to be around $400M.
We then ran the same scenario using Powerline’s Market Clearing Engine, which accounts for the battery’s pricing power and its impact on market dynamics. The picture changed dramatically.
Only one quarter (25%) of the revenue estimated using price-take approach could be realistically captured.
For developers, this deviation is the difference between entering a project with clear, data-driven expectations and a path towards profitability, versus committing capital to one that’s destined to underdeliver against projections.
This isn’t an academic concern — it’s material.
Traditional pricetaker models rely on “accurate” price forecasts to position batteries to capture expected volatility. But in reality, as shown above, a battery’s response to such forecasts often moves the market.
Such models fail to capture these feedback loops, leading to overstated revenue expectations and a blind spot around the interaction between dispatch strategies and market outcomes. When it comes to operations, this misalignment can have even higher performance consequences for portfolio owners.
Example in action - June 12, 2025
June 12, 2025, was a price spike day in New South Wales, with prices above $10k for over 3 hours. The light-blue line in the graph below shows the historical prices during these intervals.
Our example 500MW / 2000 MWh battery was expected to make over $17.5M in this day, using the price-taker assumptions. However, only $4.18M could be realistically captured if you ran those same bids through an accurate market-clearing engine.
The dark-blue line in the graph below shows the drastic decrease from the historical energy prices with addition of the new battery to the market.
Furthermore, missing the feedback loop between battery bidding and market response, many of the battery’s bids could not clear fully during these price spike intervals when using the price-taker approach.
Can pricing power make you powerful?
With the right tools, yes. Without them, pricing power can be a liability.
Capitalizing on the battery’s pricing power, especially during price spike intervals, can significantly increase the upside for the project.
We have demonstrated that revenue efficiency of battery portfolios can be increased 10%-40% if such dynamics are accounted for in interval-by-interval dispatch strategies, contract structures, and project design.
Powerline’s Market Clearing Engine
To tackle the challenges outlined in this article, Powerline has built its own Market Clearing Engine, an AI-powered digital twin that fully reconstructs the grid and market-clearing process.
Accuracy? Down to the penny.
This engine replicates actual market behavior at 5-minute intervals, capturing the intricate feedback loops between bidding strategies and price outcomes. It powers our platform to deliver scenario-based insights for everything from real-time dispatch decisions to multi-gigawatt investment planning.
Today, Powerline’s digital twins are running on every battery in the NEM, every 5 minutes, across multiple scenarios, helping customers unlock new revenue streams, improve risk management, and maintain profitability in an increasingly volatile market.