Global data center electricity consumption is projected to reach about 945 TWh by 2030, roughly double current levels, according to the IEA. Within that total, power use from AI-focused data centers is poised to triple. That shift is changing the storage market fast: batteries are no longer serving only utilities, solar plants, or industrial sites. They are increasingly being pulled directly into the buildout of AI infrastructure.
The reason is straightforward. AI data centers are scaling into a power problem at the same time. Training GPT-4 alone consumed more than 50 GWh of electricity, according to research on AI data center grid impacts. In the U.S., Gartner projects data center electricity use will rise from 4% to 7.8% of regional consumption between 2025 and 2030. For hyperscalers and AI infrastructure developers, access to reliable power is becoming a gating factor for growth.
That is where BESS enters the story as a new customer category, not just a supporting technology.
A few numbers frame the shift:
- ~945 TWh by 2030: projected global electricity consumption for data centers, according to the IEA
- 3x growth by 2030: projected increase in electricity use from AI-focused data centers, according to the IEA
- $650 billion in 2026: estimated spending by major tech companies on AI data centers
- 4% to 7.8%: projected rise in U.S. data center electricity usage as a share of regional consumption from 2025 to 2030, according to Gartner
- 50+ GWh: estimated electricity consumed in training GPT-4
- 12 MW / 63 MWh: Redwood Materials microgrid built to power modular AI data centers
- Up to 12 GWh: Form Energy storage deal signed with Crusoe for AI data center support starting in 2027
The commercial implication is bigger than one vertical. If AI data centers keep running into interconnection delays, volatile power costs, and tighter uptime requirements, storage moves from optional resilience layer to core infrastructure.
AI growth is turning power availability into a deployment bottleneck
The AI buildout is often described in terms of chips, models, and capital spending. But the harder constraint may be electricity.
According to the IEA, electricity consumption from data centers is set to double by 2030, while AI-focused facilities are on track to triple their power use. At the same time, major tech companies are estimated to spend $650 billion on AI data centers in 2026. That combination matters because it points to a market expanding faster than conventional power delivery timelines.
The pressure is already visible in regional demand forecasts. Gartner says U.S. data center electricity usage is projected to rise from 4% to 7.8% of regional consumption between 2025 and 2030. That is not a marginal increase. It means data centers are becoming a much larger share of local load in a relatively short period.
OpenAI has publicly framed the issue in even starker terms. CNN reported that the company warned the White House of an “electron gap” that puts U.S. leadership in AI at risk, writing that “electrons are the new oil.” Euronews also reported that OpenAI has put UK and Norway investments on hold due to high electricity prices.
Those signals point to the same conclusion: AI expansion is no longer limited by compute demand alone. It is increasingly limited by how quickly developers can secure dependable, affordable power.
Why BESS is moving inside the data center stack
The storage opportunity here is not just about adding more megawatt-hours to the grid. It is about solving a specific operational problem for AI facilities.
Giovanni Rossi, Head of Marketing at ELECTRA AI, described the challenge this way: “Hyperscale operators are confronting a compounding set of challenges: surging power demand from AI workloads, multi-year grid interconnection queues, rising costs of downtime, and tightening regulatory scrutiny of backup and behind-the-meter storage.”
That quote captures why AI data centers are becoming a distinct BESS customer segment.
Several pressures are converging:
- Load growth is steep: AI workloads are pushing facility power demand higher
- Interconnection takes time: multi-year queue delays can slow project energization
- Downtime is expensive: resilience has direct commercial value
- Load profiles are harder to manage: AI data centers introduce unpredictable power demands that challenge existing grid assumptions
- Renewable integration matters: operators are using storage to pair cleaner generation with data center operations
That last point is already showing up in deployments. A reported example from Korea says firms such as LG Energy Solution are deploying advanced BESS directly into data center operations to improve renewable integration and provide grid stabilization.
This is an important distinction. In earlier storage cycles, data centers were often discussed mainly as backup-power users, with diesel generation doing most of the heavy lifting in contingency planning. The current wave is broader. Batteries are being positioned as part of the operating architecture itself: helping bridge interconnection gaps, support onsite generation, stabilize power quality, and reduce exposure to grid constraints.
Real projects show AI infrastructure is already buying storage
The strongest evidence that AI data centers are becoming a major storage customer is not theoretical. It is already visible in project announcements and signed deals.
Redwood Materials offers one of the clearest examples. At its Nevada site, the company built a 12 MW / 63 MWh microgrid that combines solar with second-life EV battery storage to power modular data centers operated by Crusoe. Redwood says the project was built and commissioned in under four months.
That case matters for two reasons.
First, it shows storage being used to accelerate “speed to power,” not just optimize an existing grid connection. Second, it ties BESS directly to modular AI compute deployment, where time-to-energization can be as important as long-term power cost.
Crusoe’s separate agreement with Form Energy points to the next phase. According to Data Center Dynamics, Crusoe signed a deal for up to 12 GWh of multi-day energy storage systems to support the AI data center sector beginning in 2027.
That is a very different scale signal from a pilot or demonstration project. It suggests that for at least some AI infrastructure developers, the storage requirement is moving beyond short-duration balancing and into longer-duration supply support.
The market is noticing. On Shoals Technologies Group’s earnings call, Moss said AI data centers are “probably the strongest and largest driver” for BESS bookings longer term.
That statement is notable because it comes from a company discussing actual bookings mix, not a broad industry forecast. It suggests AI demand is starting to register in commercial pipelines, even if grid firming and solar-plus-storage still account for most current bookings.
The load profile is changing what storage has to do
Not all large electricity customers create the same storage demand. AI data centers stand out because their power draw can be both massive and difficult to predict.
One source in the briefing notes that AI data centers are increasingly challenging assumptions about how the electrical grid operates by introducing unpredictable power demands. That matters because storage economics and system design depend heavily on the shape of the load, not just the total energy consumed.
For BESS providers, this changes the conversation from generic backup capacity to performance under more complex operating conditions.
The requirements now extend across several dimensions:
- Fast response: to manage sudden changes in facility demand
- Power quality support: where uptime sensitivity is high
- Renewables integration: especially in behind-the-meter or microgrid setups
- Longer discharge duration: where developers need more than short bridging capability
- Operational flexibility: to support modular or phased data center expansion
That is also why longer-duration storage keeps appearing in discussions around AI infrastructure.
Shiledar told Power Technology, “As long-duration battery systems continue to mature, that is where the [data centre] market will shift towards, rather than entirely new technologies.” He added that scaling will require “persistent innovation to enhance the energy density and longevity of storage solutions,” making storage more attainable and cost-effective, starting with policy support for R&D.
The implication is not that short-duration BESS disappears. It is that AI data center demand may widen the storage stack. Some projects will need batteries for fast-response balancing and resilience. Others will increasingly look for multi-hour or multi-day support as grid constraints tighten and power procurement becomes more difficult.
This is a new customer story, not just another BESS demand tailwind
The storage industry has spent years building around utility procurement, solar-plus-storage, and grid services. AI data centers introduce a different buyer logic.
These customers are not necessarily entering the market because they want to trade ancillary services or optimize merchant spreads. They are entering because storage can unlock compute deployment, reduce exposure to interconnection delays, and protect uptime in facilities where power interruptions carry outsized costs.
That changes the commercial framing of BESS in three ways.
1. Storage becomes part of capacity access
If a project cannot get enough grid power on the required timeline, batteries can help bridge the gap when paired with onsite generation or microgrids. The Redwood-Crusoe example is the clearest case in the briefing.
2. Storage becomes part of uptime strategy
Rising downtime costs were explicitly cited by Giovanni Rossi. For AI facilities, resilience is not a side benefit. It is tied to service continuity and asset utilization.
3. Storage becomes part of AI infrastructure capex
When major tech companies are estimated to spend $650 billion on AI data centers in 2026, even a modest shift toward integrated storage can create a large new demand pool for BESS vendors, integrators, and long-duration storage providers.
This is why the phrase “new customer” matters. The opportunity is not only that AI indirectly boosts electricity demand and therefore helps storage. It is that AI developers, hyperscalers, and AI factory operators are increasingly becoming direct storage buyers.
What comes next for BESS in the AI data center market
The next milestones are already visible in the current fact pattern.
One is the move from short-duration systems to longer-duration storage. Crusoe’s deal with Form Energy, beginning in 2027, is an early marker that some AI data center operators are looking beyond conventional battery durations.
Another is the pace of direct deployment into data center operations. The examples in Nevada and Korea suggest that batteries are already being integrated at the facility level, not only procured at the utility or grid edge.
A third watchpoint is the interconnection queue itself. Giovanni Rossi’s comments make clear that multi-year delays are part of the commercial case for storage. If those queues remain long, BESS adoption inside AI infrastructure could accelerate further.
There are also two broader market triggers to monitor:
- Electricity price pressure: especially in regions where high prices can delay or halt AI investment, as reported in OpenAI’s UK and Norway pause
- Regulatory scrutiny: particularly around backup and behind-the-meter storage, which Rossi identified as a growing issue for hyperscale operators
For the storage sector, the key question is no longer whether AI will affect power markets. It already is. The more immediate question is how much of that pressure converts into direct storage procurement by data center developers themselves.
AI data centers are moving from a future use case to an active customer class for BESS. For storage companies, that could become one of the market’s most important demand shifts over the next few years.
If your business tracks the next wave of BESS demand, this is one segment worth watching closely: not because AI is fashionable, but because power has become a hard constraint on AI growth.
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