Battery energy storage systems (BESS) are rapidly becoming foundational to the clean energy transition. As grids worldwide integrate higher shares of intermittent renewable generation, batteries offer the flexibility needed to balance supply and demand in real time. Whether through peak shaving, frequency regulation, capacity provision, or energy arbitrage, BESS are enabling more reliable, resilient and decarbonised power systems.
This growing value, however, comes with a caveat. Much of the industry’s focus has been on optimising battery dispatch to capture revenue across multiple value streams, maximising returns in ancillary services, trading energy across price spreads, and supporting grid stability. But there’s a critical dimension that remains consistently underrepresented in dispatch models and operational decision- making: the physical cost of operating the battery itself.
Behind every dispatch lies a complex cost structure that goes far beyond the visible market price of electricity. Operators bear the costs such as the certain fees required to participate in markets. This is a minimum revenue to be achieved by the battery to offset the uncertainty of the market in which the battery operator is bidding and the operational expenses– the expected cost related to operating the battery in a certain market.
Alongside these costs there is a category of cost related to the ageing of the battery. Battery ageing takes two primary forms. Cyclic ageing occurs as a result of charging and discharging the battery, with wear accumulating more rapidly under certain usage conditions. Calendar ageing, on the other hand, happens even when the battery is idle, driven by the passage of time, environmental exposure, and internal chemical processes. Together, these forms of wear create the economic value lost as the battery’s usable life diminishes– what may be quantified as ‘degradation’ cost.
Every time a lithium-ion battery is charged or discharged, it experiences degradation. This isn’t just a technical side-effect but a financial liability. Each cycle depletes a small amount of the battery’s usable life, gradually reducing its capacity, efficiency and responsiveness, meaning change in state-of-health (SoH).
SoH decline can change how the battery is operated. To protect a degraded system from further harm, operational limits may be tightened, reducing the usable portion of its capacity. This can increase the effective cost of activation for certain services and lead to missed market opportunities when the battery is withheld from dispatch to preserve its remaining capability.
These effects are interconnected. A change in SoH can ripple through the entire cost structure, influencing operational decisions, altering performance guarantees, and creating opportunity costs. Even a small shift can cascade into a measurable loss of revenue potential. This is why modern dispatch models aim to optimise battery operations across all relevant costs, including activation, operational, opportunity and ageing-related expenses, while preserving system health. Achieving this balance is not straightforward, it requires a complex, mathematical optimisation process running in the background, constantly weighing short-term market gains against the long-term financial and technical consequences of each decision.
Dispatching a battery without factoring in degradation is like running a machine without accounting for maintenance or depreciation. It might generate revenue today, but it’s eroding value that could be captured tomorrow, because degradation cost directly links to operational choices and to asset life. While activation and operational costs are necessary for participation, degradation is the cost that determines how long and how well the battery can perform. In reality, degradation cost is dynamic. It shifts with operating conditions, dispatch decisions, and environmental influences. Especially in competitive markets, where batteries are cycled aggressively, failing to internalise degradation risk can significantly reduce total lifecycle returns.
Not all degradation is created equal
High-depth discharges, rapid cycling, and operations at high or low state-of-charge (SoC) levels can accelerate wear disproportionately. These effects are well understood in laboratory environments and increasingly modelled in advanced energy management systems. Yet in practice, many dispatch decisions are still made with revenue maximisation in mind, often ignoring how specific operating patterns impact long-term system health and profitability.
As the industry matures, it’s no longer enough to think of batteries as interchangeable revenue machines. They are finite-use assets with performance constraints that must be managed just as actively as their power output. Integrating degradation-aware dispatch strategies, ones that account for the trade-offs between short-term gain and long-term value, is essential to unlocking the full economic and operational potential of battery storage.
The economics of battery dispatch are evolving. Understanding degradation as a cost, and embedding it into both commercial and technical strategies, will be key to building bankable, profitable and durable energy storage assets in the years ahead.
What is battery degradation and why it matters
Battery degradation is the gradual loss of capacity that happens with every use. For lithium-ion batteries, which dominate grid-scale storage, it’s an unavoidable reality. This degradation is driven by a complex mix of chemical, thermal and operational factors.
Unlike traditional assets, batteries are finite-use components. Every charge and discharge cycle contributes to wear, and not all cycles are equal. High-depth discharges, fast charging, or operations at extreme temperatures can accelerate degradation disproportionately. As degradation progresses, usable capacity diminishes, efficiency drops, and system responsiveness can decline. The result is fewer megawatt-hours available for dispatch and reduced flexibility in participating in dynamic grid services or energy markets.
This capacity loss directly impacts revenue potential. For example, a 10MWh system that degrades to 8MWh represents a 20% reduction in tradable energy. This reduction isn’t just theoretical, but it materially constrains the operator’s ability to capture market opportunities, especially in high-volatility environments where performance and responsiveness are critical.
Moreover, degradation shifts the economic calculus for storage operators. Dispatch strategies focused solely on maximising near-term revenues risk overlooking the long-term implications of aggressive cycling. Without incorporating degradation cost into operational decision-making, operators may unknowingly sacrifice overall profits.
Ultimately, battery degradation is not simply a maintenance issue, it is a core financial consideration. Effective storage-asset management requires a nuanced understanding of how each dispatch decision affects both present earnings and future capability. As the energy storage industry matures, integrating degradation-aware strategies will be essential for sustaining asset value and maximising return on investment over time.
How batteries are really used and why that’s a problem
Battery dispatch refers to the operational strategy of charging and discharging BESS in real time, based on a variety of inputs such as market prices, grid requirements, and internal system constraints. In simple terms, it is the process of deciding when a battery should absorb energy and when it should release it, and by how much. This process is central to how grid-scale batteries generate revenue and support the stability of modern power systems.
In large-scale applications, batteries are dispatched to serve several key use cases. These include energy arbitrage, where the battery charges during periods of low electricity prices and discharges when prices are high; frequency regulation, where batteries rapidly inject or absorb energy to help maintain grid frequency within a narrow range; and capacity services, where batteries are held in reserve to support the grid during peak demand or unexpected outages. In many regions, batteries participate directly in electricity markets by bidding into day-ahead or real-time auctions, where they compete with generators and other flexible resources to provide energy or ancillary services.
While the grid-facing side of battery dispatch is built around extracting value from market signals, the battery itself is a physical system that experiences degradation with each cycle. This degradation is not linear, and it depends heavily on how the battery is operated, including the depth of discharge, the rate of power flow, and the temperature conditions during use. However, most battery operators today rely on relatively simple dispatch models that do not account for the internal cost of using the battery. Dispatch decisions are often made purely based on external signals, such as spot prices or regulation demand, without real-time feedback on how each action affects the long-term economic performance of the battery.
A common example is a utility-scale lithium-ion battery operating in a wholesale energy market. Each day, the operator reviews the day-ahead price curve and schedules the battery to charge during low-price hours and discharge when prices spike. The goal is to capture spreads between buying and selling prices to maximise daily revenue. But in this approach, the hidden cost of battery degradation is ignored. As a result, the battery may be cycled more aggressively than economically justified, especially when spreads are marginal. Over time, this can lead to faster capacity loss and reduced profitability, even though the system appears to be earning revenue on paper.
This disconnect between revenue-driven dispatch and battery degradation-aware decision-making highlights a growing need for more advanced operational models that treat the battery not just as a market instrument but as a valuable, finite asset whose use must be economically optimised.
Quantifying degradation costs
Battery degradation is often treated as an unavoidable technical consequence, but it is more accurately understood as a real and measurable financial cost. Every charge and discharge cycle reduces the long-term value of a battery system, and that value loss can be translated into currency per megawatt-hour.
Quantifying degradation in financial terms is essential for making smarter dispatch decisions, building accurate revenue models, and structuring more sustainable business cases for energy storage projects.
However, most current methods used to quantify the cost of degradation are overly simplistic and can lead to suboptimal results. One common approach is to divide the total cost of the battery system by its expected lifetime in years. Another method spreads the cost across an assumed number of charge-discharge cycles. While both methods are easy to calculate, they suffer from fundamental flaws.
The problem with using a fixed annualised cost is that it assumes a uniform rate of degradation and usage, ignoring the fact that batteries age differently depending on how they are used. Similarly, dividing the cost by a total number of cycles assumes that each cycle has the same impact on the battery’s health, which is not true. A shallow cycle at moderate temperature degrades the battery much less than a full cycle performed at high current and extreme temperatures. By treating all usage equally, these models ignore the complexities of real-world battery operation that have a big impact on battery degradation and returns.
The most accurate way to quantify degradation costs is to model them dynamically, based on how the battery is actually used and what market opportunities are available. This requires a system that continuously monitors operational factors such as temperature, SoC ranges, cycle depth, and charge/discharge rates, all of which influence battery ageing. Instead of using static assumptions, the system calculates a dispatch-specific degradation cost in real-time.
This real-time battery degradation cost can then be sent directly to the battery optimiser or energy management system. With this information, operators can weigh the true cost of dispatching the battery against potential market revenues, allowing for smarter, profit-maximising decisions. This method ensures that battery degradation is not treated as a fixed expense but as a dynamic, data-informed factor that varies with every operational decision. This approach can increase profits to 20% only in the first year, extracting the maximum value from energy storage.
By shifting from generic models to real-time, context-aware degradation costing, operators can avoid hidden losses, extend the economic viability of their assets, and significantly improve overall return on investment.
Conclusion
Battery operation may appear simple – charge when prices are low, discharge when they are high, but this level of logic conceals a critical layer of complexity. Every cycle affects the battery’s physical condition, and consequently, the system’s long-term economic value. As battery energy storage becomes foundational to renewable integration and grid stability, operators can no longer afford to overlook the financial implications of battery degradation.
There is a clear need. Markets are more volatile, and investor expectations are rising with OEM warranties getting more complex. Understanding the economics of degradation is no longer optional, it is a strategic necessity. In this context, dispatching batteries without the knowledge of the impact of battery degradation leads to faster capacity loss, missed revenue, and reduced project viability. What appears profitable in daily operations may, in fact, be eroding future returns.
However, degradation management alone cannot unlock the full potential of battery operations. Other parameters, such as market signals, service requirements, and operational constraints, must be factored into the decision-making process. This is where multi-market participation and optimisation become powerful. By strategically allocating the battery across multiple services and optimising for both health and revenue, operators can achieve greater profitability while maintaining asset performance.
The solution lies in real-time, dynamic models that assess the true cost of each decision in the context of all available market opportunities. These tools enable operators to move beyond single-factor thinking and optimise holistically, not only for revenue in one market, but for sustained value across the asset’s lifetime.
Ultimately, battery degradation should not be viewed in isolation. It is one of several core economic variables that must be integrated into day-today operations.
The most competitive storage assets will be those managed with intelligence, balancing market opportunities with the realities of battery wear. Those who master this balance will lead in returns, resilience and reliability.
