For a decade, cheap cells decided who won in battery storage. That advantage is fading. As prices converge and fleets scale, two batteries sitting on the same grid node, built from identical components, now earn very different returns.
What separates them is how they are run. The choice of which markets to bid into, and the price an operator puts on each charge and discharge cycle, now decides most of a battery's return. A storage asset has become a trading position with a physical footprint, and the trading is where the money is made or lost.
Few researchers have measured this as precisely as Dr Dirk Lauinger of MIT, whose work models the economics of electricity storage. We spoke with him in August 2026 about what the numbers show.
Run a battery flat out and you would expect it to earn the most. For storage the opposite often holds, because payment depends on what the battery is contracted to deliver.
It depends on what you sell. If you sell energy for arbitrage, you always need to cycle the battery. If you insure the grid against unforeseen events, for example by participating in ancillary services or capacity markets, you only need to cycle the battery if these events actually occur.
In a four-year backtest, we found that optimizing battery trading can triple profits. Of course, this result depends on how poor exactly your baseline is and on how well you can predict day-ahead and intraday market prices.
The set of markets open to storage keeps changing as each one fills up with competitors.
Historically, batteries first participated in low-volume high-value markets, frequency regulation for example. As these markets saturate, battery operators increasingly turn to optimizing the joint participation in both low-volume high-value and high-volume low-value markets, such as arbitrage.
In theory, batteries could also provide value to grid operators when used to relieve congestion in distribution or transmission grids. However, because of concerns over market distortion, only a few jurisdictions currently allow for that.
I would ask how confident the operator is in their market price projections, and I would be worried if they do not discuss the volatility of locational marginal pricing in the US or the potential shift to more zonal pricing in the EU. I would be relieved if they discussed how they hedge against such uncertainty, either physically by being able to move the battery, or financially.
The economics of storage now sit with the operators who trade well. Hardware still sets a floor on cost, but the return comes from disciplined bidding across markets and an honest price on degradation. As margins tighten and prices swing harder, that skill will separate the assets that earn their cost of capital from the ones that quietly disappoint their investors.
From Asset to Algorithm sets out the full case, with the market data, the degradation economics, and the operating model behind it.
Dr. Dirk Lauinger is a postdoctoral researcher at the MIT Energy Initiative and the MIT Sloan School of Management, where his work applies mathematical optimization to the economics of electricity storage. He holds a PhD in Operations Research from EPFL, along with earlier degrees in energy management and electrical engineering. His research covers the economics of energy storage and electric transportation, and the market rules that govern them.
