Battery energy storage firms are increasingly positioning themselves at the center of the AI revolution, offering solutions designed to stabilize electricity usage, reduce strain on power grids and support the enormous energy requirements of next-generation computing facilities.
However, industry experts warn that despite growing demand, major obstacles — including lengthy grid connection delays and heavy reliance on Chinese supply chains — continue to slow the sector’s ability to expand quickly enough to meet the needs of the booming AI industry.
Battery energy storage systems work by storing electricity when supply is plentiful and releasing it back into the grid during periods of high demand. The technology has already become critical in renewable-energy-heavy states like California, where batteries help offset evening electricity shortages after solar generation declines.
Now, those same systems are becoming increasingly important for AI-driven data centers, many of which require vast amounts of uninterrupted power to operate advanced computing systems and train large language models.
Experts say battery storage offers several advantages for hyperscale data centers. Installed in front of the meter, batteries can help smooth energy demand and improve transmission efficiency. Behind the meter, they can manage sudden demand spikes, reduce pressure on strained grids, provide backup during outages and minimize dependence on diesel-powered emergency generators.
Still, scaling the technology nationwide remains difficult.
“Supply chain constraints and interconnection queues are two of the most important barriers,” said Harvest-Time Obadire, senior power and renewables analyst at BMI.
Obadire noted that while a modern data center can typically be completed within 18 to 24 months, connecting that facility to the electricity grid can take anywhere from three to seven years in several parts of the United States.
AI Growth Expected to Push Electricity Demand Sharply Higher
The surge in AI infrastructure is expected to significantly increase U.S. electricity consumption over the next decade.
According to the Electric Power Research Institute, data centers could account for between 9 percent and 17 percent of America’s total electricity demand by 2030 — equivalent to as much as 790 terawatt-hours annually. That would represent a dramatic increase from the roughly 4 percent share data centers consume today.
At the same time, the U.S. battery storage sector is growing at record speed.
Data from the Solar Energy Industries Association showed that the country added a record 57.6 gigawatt-hours of new battery storage capacity in 2025, bringing total deployed capacity to 166.1 GWh.
Industry forecasts suggest annual battery deployments could nearly double to 110 GWh by 2030, with AI data centers expected to drive a substantial portion of that demand.
Companies Rush to Secure Position in AI Power Market
The rising need for stable electricity is already fueling major investments and commercial agreements across the energy storage industry.
Fluence said it is currently involved in more than 30 GWh of data-center-related projects globally, including a significant number in the United States.
Chief Executive Officer Julian Nebreda said the company sees domestic manufacturing as a key competitive advantage as demand for AI infrastructure accelerates.
“We intend to continue growing and investing in it,” Nebreda said, referring to Fluence’s U.S.-based manufacturing expansion.
Meanwhile, Tesla reportedly generated $430 million in revenue last year from energy storage sales tied to xAI, the AI venture founded by Elon Musk.
Another major deal involves Calibrant Energy, which agreed to deploy a 31 MW/62 MWh battery storage system at an Aligned campus in the Pacific Northwest.
Industry analysts say battery systems are also becoming increasingly important alongside natural gas generation facilities, which many developers now view as essential backup power sources for AI operations.
“Batteries will be an essential resource at data centers reliant on onsite gas generation, as gas generators are not fast enough to follow volatile AI data center demand,” said Ben Hertz-Shargel, global head of grid transformation at Wood Mackenzie.
China Dependency and Grid Delays Remain Major Risks
Despite strong market momentum, experts caution that the industry still faces structural vulnerabilities.
A major concern is the continued dependence on China for battery materials and supply chain components, particularly lithium iron phosphate battery technology. Although the U.S. is increasing domestic manufacturing capacity, analysts say sourcing alternatives outside China remains underdeveloped.
“This is an opportunity to scale U.S. manufacturing which otherwise would have been priced out of the market, but sourcing materials from outside of China still needs to be developed further,” said Chris Dendrinos, analyst at RBC Capital Markets.
Another persistent challenge is the backlog of projects waiting to connect to the U.S. electricity grid.
Grid operators across the country continue to struggle with interconnection queues, which delay approvals for new battery and renewable energy projects for several years.
The situation became particularly severe at PJM Interconnection, the nation’s largest grid operator, which temporarily paused processing new connection applications in 2022 after becoming overwhelmed by the volume of proposed projects.
Although PJM has resumed accepting applications, industry leaders say delays remain a major barrier to rapid deployment.
“Were it not for multi-year interconnection queues, we could deploy a utility-scale battery storage system in under a year to meet the needs of the electric grid,” Nebreda said.
As the global AI race intensifies, energy experts believe battery storage will become one of the most critical technologies supporting the future of digital infrastructure — but only if supply chains, manufacturing capacity and grid modernization efforts can keep pace with soaring demand.