Post: Following the forecast: the hidden risk in battery optimization | Q&A

Following the forecast: the hidden risk in battery optimization | Q&A

As more batteries chase the same price signals, the signals themselves start to break down, and most of the market hasn’t fully priced in what that means.

Here we spoke to GridBeyond Senior Trader (USA), Ali Karimian to discuss this risk, why it’s growing, and why your choice of optimizer matters more than you might think.

Q: What is the “forecast curse” in quantitative trading?

As we noted in our recent whitepaper, changes in electricity markets are creating significant opportunities for value generation. As volatility increases, particularly during periods when thermal outages are high and battery storage resources are nearing the end of their charge, the potential for capturing price spreads grows. The advantage shifts toward operators who invest in sophisticated software, predictive analytics, and real-time optimization. These strategically captured gains, which exceed those of more passive or basic strategies, are often referred to as “alpha” (α).

But when too many market participants use the same forecast, the alpha disappears and can sometimes invert. This means a forecast only gives you an edge if you’re acting on information others aren’t. Once too many market participants use the same forecast, they all make the same trades at the same time. The opportunity gets competed away, so the trade that used to make money starts losing it. In other words, a forecast’s value isn’t fixed. It depends on how many people are acting on it.

Q: How does that apply to battery storage?

A battery makes money by charging when power is cheap and discharging when it’s expensive, and it decides when to do both based on a price forecast. Now imagine a large share of the batteries in a market are working from the same forecast. They all see the same peak hours coming, so they all discharge into them together. That flood of supply drags prices down, and the peak disappears.

The same thing happens in reverse at the other end of the day. The forecasted cheap charging windows fill up with batteries all charging at once, demand jumps, and prices spike. The forecast wasn’t wrong when it was made. But the value diminished because everyone believed and acted upon the same forecast.

Q: Doesn’t a more accurate forecast fix the problem?

Not on its own. That’s what makes this risk so slippery. A forecast can be excellent at predicting what prices would have done without the batteries responding to it and still lead you into a poor dispatch decision once the fleet acts on it.

Accuracy matters. But independence matters too. If your forecast tells you the same thing everyone else’s does, you’ll end up in the same crowded trade, however good the model is.

Q: Is this only a problem for battery arbitrage?

No. The same logic carries over to peak load forecasting for demand response and peak shaving. If enough sites respond to the same predicted peak, they flatten it, and the actual peak can land somewhere else. Whenever a large group of flexible assets acts on a shared signal, that signal starts to lose its meaning, or at least its value based on the situation vs the initial forecast.

Why is this risk so underappreciated?

Partly because it doesn’t show up until the market reaches a certain level of situation. When storage is a small slice of a market, batteries are price-takers. They can act on a forecast without changing the outcome. As fleets grow and more of them lean on similar signals, that stops being true, and it tends to happen gradually rather than all at once.

It’s also easy to miss in the numbers. Backcasts are run against historical prices, and those prices didn’t include today’s crowd of batteries reacting to today’s forecast. This means a strategy can look great on paper and still underdeliver in a market that’s changed underneath it. We’d argue it’s one of the most underappreciated risks in battery optimization right now.

Can’t the biggest operators just build their own forecasts?

They can, but scale creates a different version of the same problem. Some of the largest battery optimizers manage 5GW or more. At that size, your own dispatch moves the market. When you discharge, prices fall because you’re discharging. When you charge, prices rise because you’re charging.

That means your forecast and your behavior are tangled up with each other. You’re effectively crowding yourself out, cannibalizing the very signal you built to guide you. A proprietary model doesn’t help much if your own fleet is big enough to make its predictions come untrue.

Q: What about smaller players?

Small operators face the opposite problem. Their dispatch doesn’t move prices, but they often don’t have the resources to build forecasting models from scratch, so they rely on third-party or consensus signals. And that puts them right back in the crowd, acting on the same information as everyone else.

Q: Where does GridBeyond sit?

In what we think is the sweet spot. We’re large enough to run a dedicated data science team that builds our own forecasts from the ground up, independent of the consensus signals that shape how most of the market dispatches. And we’re small enough that our roughly 1GW of resources in ERCOT isn’t itself moving prices or distorting the signals we use.

That combination is less common than it sounds. Too small, and you can’t build proprietary models. Too large, and you can’t use them without getting in your own way.

Q: Does that mean bigger is worse?

Not at all. Scale brings real advantages in plenty of areas, from market access to operational resilience. But forecasting is one area where more isn’t automatically better. Past a certain point, size works against the quality of the signal you’re trading on.

The edge comes from having independent forecasts and a footprint that lets you act on them cleanly.

Q: What should asset owners ask their optimizer?

If you own or are investing in battery storage, it’s worth asking a few direct questions:

• where do your forecasts come from? are they built in-house, or bought in from the same providers much of the market uses?
• how independent are they? do they diverge meaningfully from consensus signals, or largely echo them?
• how big is your fleet in this market? and at that size, is your own dispatch moving prices?
• does your strategy account for crowding? not just whether the forecast is accurate, but what happens when everyone else acts on something similar

The answers will tell you a lot about whether your battery is set up to capture value, or simply to follow the crowd into the same trades as everyone else.