Why an uncertain financial landscape is slowing the scaleup of batteries

In the wake of the European elections, new research reveals Europe’s battery rollout is lagging behind the rate required for renewable energy targets, and growth could slow further over the next three years, explains Jean-Marc Guillou, chief technology officer for energy storage systems at Socomec.

Despite the critical role of batteries in decarbonising power grids and the economy, the utilities and commercial and industrial (C&I) sectors are also currently seeing the slowest pace of adoption.

This sluggish deployment is linked to fluctuating costs amidst surging insurance premiums from rising lithium-ion fire risks, and rising operational and supply chain costs for some sectors. Profitability is similarly uncertain with concerns over reliability causing batteries to be underpriced in capacity markets where future reserve energy capacity is traded, while arbitrage strategies selling stored power during peak prices risk accelerating battery degradation and thus uncertainty around future costs.

This uncertain financial picture raises questions over our ability to deliver the 14-fold increase in storage capacity needed by 2030 to meet Paris Agreement climate targets. 

A fluctuating, uncertain battery landscape

The central challenge is the difficulty in understanding how different use cases from arbitrage strategies to ancillary services regulating grid frequencies can reduce battery capacity and life expectancy. For example, poorly-managed energy arbitrage strategies where batteries are rapidly discharged to sell power whenever prices are high, can accelerate degradation and create uncertainty over future battery capacity.

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Uncertainty around the reliability of battery performance means that some regulators do not allow battery operators to sell as much of their capacity as gas or nuclear in the capacity markets.  Poor battery management by end-users is also exacerbating costs and risks. For example, call-outs for lithium-ion battery fires increased 46% in London recently, and similar incidents across commercial and industrial sectors increases operational costs and insurance premiums.

Garbage in, garbage out

The uncertainty around battery costs arises from the fact that current BESS data is often inaccurate, incomplete and riddled with uncertainties. Battery performance is susceptible to a range of external conditions, internal chemistries, and patterns of use that have until now not been fully understood.

As a result, many Battery Energy Storage Systems (BESS) can only provide around 90% accurate estimates of battery capacity, health and performance and cannot predict how different patterns of use may reduce lifespans. Some BESS data comes with missing files or even timestamps. BESSs often only flag warning signs of accelerated degradation, such as abnormal temperature or voltage levels, hours, or even minutes before the incident.

This means battery insurance premiums and maintenance costs are often unnecessarily high and batteries get unfairly low ‘de-rating’ factors, which set the percentage of the auction tariff that batteries receive in capacity markets. With battery costs and risks being driven by inadequate data, batteries risk falling prey to the principle of ‘garbage in, garbage out.’

AI-powered investments and insurance

Recent advances in AI now hold the promise of bringing unprecedented certainty to battery performance, creating more accurate insurance premiums and de-rating factors, and improving costs and profits. For example, analytics tools can now be customised to automatically find missing files, timestamps or other flaws and transform raw battery information into refined insights visualised on live digital dashboards. This transforms confidence in battery data, enabling it to be automatically checked for completion or consistency and cleansed of errors.

Forms of artificial intelligence, such as machine learning and deterministic AI, can then harness this rich data to predict the root causes of degradation, such as thermal runaway caused by cell imbalances or lithium plating, several months in advance.

This would allow operators to implement predictive maintenance strategies that avert hazards such as lithium-ion fires, and thus reduce insurance premiums. Predictive maintenance could also help avoid the need for costly replacement of parts at a time when recent tariffs and inflation are raising supply chain costs for some components

New advances in Artificial Intelligence now enable operators to predict battery state of charge or state of health with 98% accuracy, allowing operators to reliably predict lifelong capacity, health and performance. Tests have shown AI can even predict the optimal usage cycles to reduce degradation and maintain a high level of capacity 20 years ahead.

The resulting data can be used to optimise battery operations and create far greater certainty around future battery reliability, safety, and performance.  Battery strategies can also be optimised to reduce energy waste and emissions, further improving cost and carbon efficiency.

AIs provide data that helps optimise future arbitrage strategies, ancillary services, or off-take agreements to maximise profits while also maintaining battery capacity, and life expectancy.  This could also help operators demonstrate that batteries can provide reliable future capacity, attracting fairer prices for battery storage in the capacity markets and incentivising more deployments.

AIs can even harness this data to refine future battery designs, creating a virtuous circle where step-changes in today’s batteries drive sea-changes in tomorrow’s battery technologies.

Unlocking the energy transition

Batteries pave the way to net zero by smoothing out volatility in renewable energy supplies and costs and balancing supply and demand across renewable grids whilst providing dependable power for mission-critical commercial and industrial applications.

Yet the necessary investment is being hindered by uncertainties around this complex new asset class driving high insurance premiums, volatile costs, and trading profits. This is partly because BESS often lacks the same predictability and reliability around future capacity and life expectancy as other forms of flexible power capacity from pumped-hydro to gas power plants.  

Game-changing new advances in AI and analytics will finally overcome this hurdle, dramatically reducing volatility and uncertainty around battery costs to de-risk and accelerate the rollout of battery storage.

About the author

Jean-Marc Guillou started his career in renewable energy with groups like Conergy AG and BP Solar almost 20 years ago. Since 2019, he has specifically addressed the world of renewable energy integration, vehicle charging infrastructure, and C&I with storage solutions for Socomec Group across the NAM, EMEA and APAC regions.