Quick summary

Battery storage is the fastest-growing power technology, but a battery's value comes from how it is operated, not simply from being installed. Dispatch and optimisation software decides when to charge and discharge to capture value across markets and grid services. This article explains why dispatch matters more than capacity, what makes the optimisation hard, and why it is fundamentally a software and data problem.

Introduction

Battery storage has moved from a niche technology to a central part of the power system at remarkable speed. It can absorb surplus renewable generation, release it when needed, and respond to grid signals in seconds, which makes it uniquely suited to a system with growing shares of variable wind and solar.

But installing a battery is only the start. Two identical batteries can earn very different returns depending on how they are operated, because the value lies in making the right charge and discharge decisions at the right moments, across volatile prices and multiple revenue streams. That decision-making is the job of dispatch and optimisation software, and it is where much of a storage asset's value is won or lost.

Battery storage is scaling fast

The growth figures are striking. According to the IEA, global battery storage additions reached 108 gigawatts in 2025, around 40 percent more than the previous year, leaving installed capacity roughly eleven times higher than in 2021, with utility-scale projects making up about 80 percent of new capacity (IEA, 2026).

This is now the fastest-growing commercially available power technology, helped by short construction times that let projects come online in around two years. Battery storage is well placed to provide short-term flexibility for periods of one to eight hours and to respond to market signals within seconds, which is exactly what a renewable-heavy grid needs (IEA, 2024). The reason this matters is that the sheer scale of deployment means a vast and growing fleet of assets whose economic performance now depends on how intelligently each one is operated.

The storage boom has created a large fleet of assets whose returns hinge on operation, not just on having been built.

Takeaway: Battery storage is scaling extraordinarily fast, which makes how each asset is dispatched a question of growing economic weight.

Value comes from dispatch, not capacity

A battery earns money by exploiting differences in the value of electricity over time and by providing services the grid will pay for. The clearest example is energy arbitrage: charging when power is cheap and discharging when it is expensive. On top of that, batteries can provide ancillary services such as frequency response, and contribute to peak capacity.

The most valuable strategies combine these, an approach often called revenue stacking, where a battery captures arbitrage and ancillary-service income from the same asset, and pairing with solar or wind adds dispatchability (IEA, 2024). The implication is that capacity alone says little about value: the same megawatt-hours can produce modest or strong returns depending entirely on how well the asset's operation is optimised across these competing uses.

Takeaway: A battery's returns come from how well its operation is optimised across arbitrage, ancillary services and capacity, not from its size alone.

The optimisation problem

Deciding how to operate a battery is genuinely hard. At any moment, the software has to weigh current and forecast prices, the state of charge, the commitments the battery has already made, and the relative value of competing uses, then choose a charge or discharge action, and do this continuously as conditions change.

It is fundamentally a forecasting-dependent problem. Good dispatch decisions rely on good predictions of prices, demand and renewable output, which is why battery optimisation and energy forecasting are closely linked. The software is, in effect, solving a constrained optimisation repeatedly in near real time, balancing the chance of higher value later against the value available now. The reason this matters is that small, consistent improvements in these decisions compound into materially better returns over the asset's life.

Takeaway: Battery dispatch is a continuous, forecast-driven optimisation, and small improvements in those decisions compound into large differences in return.

The constraints that make it hard

Real batteries are not idealised stores of energy. Every charge and discharge cycle contributes to degradation, so aggressive operation that maximises short-term revenue can shorten the asset's life and erode its value, forcing a trade-off between earning now and lasting longer. State-of-charge limits, efficiency losses and warranty terms all constrain what the software can do.

Markets add their own rules. Grid operators may impose requirements such as minimum state-of-charge levels to ensure batteries remain available during peak periods, and participating in multiple markets means respecting the obligations of each. The implication is that battery optimisation is not a pure profit-maximisation exercise; it is optimisation under a web of physical, contractual and regulatory constraints, and software that ignores any of them will make decisions that look good on paper but fail in practice.

Battery optimisation is constrained on every side, by chemistry, by contracts and by market rules, so respecting the constraints matters as much as chasing the value.

Takeaway: Effective dispatch optimises within real limits on degradation, state of charge and market rules, not just for maximum short-term revenue.

Why this is software and data territory

All of this means a battery is only as valuable as the software that operates it. Capturing the available value requires real-time optimisation, integration with price forecasts and market interfaces, awareness of the asset's physical state, and safe, reliable execution of decisions, none of which is delivered by the hardware itself.

This is the intersection of energy expertise and software engineering. The domain knowledge defines the constraints and value streams, while the engineering, the data pipelines, optimisation algorithms, market integrations and reliability, determines whether that knowledge is actually realised in operation. The reason this matters is that as the storage fleet grows, the competitive difference between assets will increasingly come from the quality of their control software rather than from the batteries themselves.

Takeaway: A battery's value is realised through its control software, making dispatch optimisation a core energy-and-software capability rather than a hardware feature.

Conclusion

Battery storage is scaling faster than almost any technology in the power sector, but its value is not fixed at installation. It is earned, continuously, through the decisions about when to charge and discharge, across arbitrage, ancillary services and capacity, and within real constraints on degradation, state of charge and market rules.

That makes dispatch and optimisation software the heart of a storage asset's economics. As the fleet grows and markets evolve, the operators who treat optimisation as a serious software and data discipline, grounded in solid forecasting and a clear-eyed view of the constraints, will be the ones whose batteries deliver their full value rather than a fraction of it.

FAQ

What is battery dispatch optimisation?

Battery dispatch optimisation is the process of deciding when a battery should charge and discharge to maximise its value, taking into account electricity prices, forecasts, the battery's state of charge, its physical limits and the rules of the markets it participates in. It is typically handled by software that solves this decision continuously as conditions change.

Why does dispatch matter more than capacity?

Because two identical batteries can earn very different returns depending on how they are operated. The value of storage comes from exploiting price differences over time and providing grid services, which depend on making the right charge and discharge decisions. Capacity sets the potential, but dispatch decisions determine how much of that potential is actually realised.

How do batteries earn money?

Mainly through energy arbitrage, charging when power is cheap and discharging when it is expensive, and by providing ancillary services such as frequency response, as well as contributing to peak capacity. The strongest strategies combine these, an approach called revenue stacking, where a single asset captures multiple income streams, often paired with solar or wind.

What makes battery optimisation difficult?

Real batteries face constraints that idealised models ignore. Cycling causes degradation, so maximising short-term revenue can shorten the asset's life, creating a trade-off. State-of-charge limits, efficiency losses, warranty terms and market rules such as minimum state-of-charge requirements all constrain decisions. Optimisation must respect this web of physical, contractual and regulatory limits, not just chase the highest immediate return.

Why is battery optimisation a software problem?

Because a battery cannot capture its available value without software to operate it intelligently. Realising the value requires real-time optimisation, integration with price forecasts and market interfaces, awareness of the asset's physical state, and reliable execution, all delivered by software rather than the hardware. As storage grows, control software increasingly determines how much value an asset delivers.

Sources

• Technology: battery storage - Global Energy Review 2026 - IEA - 2026 - https://www.iea.org/reports/global-energy-review-2026/technology-battery-storage
• Batteries and secure energy transitions, executive summary - IEA - 2024 - https://www.iea.org/reports/batteries-and-secure-energy-transitions/executive-summary