Modelling Assumptions

Key assumptions that shape the forecast, covering capacity buildout, battery costs, market saturation, and dispatch modelling.

What CAPEX assumptions are used?

BESS CapEx assumptions are derived from the Modo Energy BESS CapEx Survey, which aggregates global project data, combined with NREL’s Annual Technology Baseline. These figures are also used by NESO’s Future Energy Scenarios team.

How do we get the future generation stack?

The Capacity Expansion Model determines how much generation and storage capacity gets built across Europe, based on economics. It minimises total system cost while ensuring reliability, so new plants are built when merchant and capacity market revenues justify the investment. Due to local policies or cost and build-time, some technologies are fixed - for example wind in GB, and nuclear everywhere.

For full methodology, see the Capacity Expansion Model documentation.

How is CCS and hydrogen capacity treated?

• CCS Gas CCGT: Included as a retrofit option in the Capacity Expansion Model. Existing gas plants can be retrofitted to CCS, constrained by plant age, size, and retrofit-ability rules.

• Hydrogen peakers: Buildout is based on policy-driven projections rather than pure economics, as hydrogen generation is still emerging with uncertain cost trajectories and limited commercial deployment.

What happens to revenues as more batteries are built?

The model captures the dynamic where increasing battery deployment leads to lower revenues per asset — but falling CAPEX partially offsets this effect.

• Market saturation: As battery capacity grows, competition intensifies. In Germany, intraday market revenues face diminishing returns as more batteries compete for the same spreads. Across all geographies, ancillary services markets (frequency response in GB, FCAS in Australia) become saturated — each battery wins a smaller share of contracts, pushing more dispatch into wholesale markets.

• Wholesale spread compression: As more batteries arbitrage price spreads, they compress. Batteries increasingly set the marginal price, reducing the spreads available to all participants. However, falling CAPEX means new projects can still achieve acceptable returns even with lower revenues.

• Self-correcting buildout: The Capacity Expansion Model sees these compressed revenues when making investment decisions. This prevents unrealistic overbuild — the model only adds capacity when the business case works given expected saturation effects.

Dispatch model assumptions

How many times can the battery cycle each day?

The dispatch model respects cycling limits to align with typical battery warranty terms. These constraints determine how many times a battery can fully charge and discharge within a given period.

• Daily cycling limits: The maximum number of full charge-discharge cycles per day is configurable in the forecast builder. A 1-cycle limit means the battery can discharge energy equivalent to its nameplate capacity once per day.

• Profitability-driven dispatch: Batteries may not cycle to their limit if it is not profitable. For example, on a constrained site with limited price spreads, the battery may only cycle once per day even if the limit is set to two cycles.

• Warranty alignment: Cycling limits reflect typical battery warranty terms, which often specify a maximum number of cycles over the asset’s lifetime. Operating within these limits helps preserve long-term asset value.

How does the battery’s capacity decline over its lifetime?

Degradation is defined as the decreasing capacity of a battery to store energy over time. A site that originally held 100 MWh may only have 80 MWh of usable capacity after several years of operation.

• Degradation modelling: The usable capacity decreases as a function of total cycles completed. Our standard degradation curve shows approximately 67% of original capacity remaining after 10,000 cycles. It is formed from Modo Energy’s unique market view of the latest OEM curves.

• Custom degradation curves: Users can input their own degradation curve via the forecast builder to reflect specific warranty terms or cell chemistry characteristics.

• Toggle option: Users can run forecasts results with or without degradation applied. Runs without degradation assume constant usable capacity throughout the forecast horizon, which is useful for understanding generic future revenues without a specific commissioning date. Conversly, when more is known about warranty parameters and degredation curves, more detailed forecasts can be run.

How are modelled revenues adjusted for real-world conditions?

The forecast is calibrated to real-world performance data using the GB Modo Energy BESS Index, which tracks revenues for battery assets above 7 MW across Great Britain.

• Benchmark comparison: Modelled revenues are compared against actual fleet performance over the trailing 12 months to capture the gap between perfect foresight and real-world trading.

• Calibration factors: Central case applies 80%, high case 85%, and low case 75% of modelled revenues.

• What calibration captures: Battery unavailability, imperfect price foresight, and competition in ancillary markets where contract wins are not guaranteed.

For full methodology and calibration charts, see the Model Calibration page.

When should battery cells be replaced?

Repowering refers to replacing degraded battery cells to restore usable capacity back to nameplate levels. This is typically considered in the later years of a battery’s operational life.

• Timing considerations: Repowering is often evaluated when usable capacity has declined significantly, typically after 10,000 cycles depending on the degradation profile and warranty terms.

• Custom run parameter: Users running custom forecasts can specify a repowering threshold in terms of cycle count. When the cumulative cycles reach this threshold, usable capacity resets to 100% of nameplate capacity.

• Impact on revenues: After repowering, the battery can capture larger price spreads again due to restored energy capacity, and revenues will increase. The degradation curve then restarts from zero cycles at the repowering date.

• Economic assessment: The decision to repower depends on comparing the cost of new cells against the incremental revenues from restored capacity over the remaining forecast horizon.