Data Centers Bypassing the Grid to Obtain the Power They Need

As artificial intelligence (AI) drives an unprecedented surge in data center growth, operators are increasingly turning away from traditional power grids in favor of on-site power generation to meet pressing energy demands.

Data center developers and operators are facing significant challenges in sourcing power. The Boston Consulting Group estimates that the impending power shortage for data centers could exceed 45 gigawatts (GW). Over 12,000 active projects are currently seeking grid interconnection, reflecting a total of 1,570 GW in generator capacity and 1,030 GW in storage capacity. The demand for power by data centers is projected to escalate from 2.5% to 7.5% of U.S. electricity consumption in the next five years.

During a presentation at Data Center World, Bill Kleyman, the AFCOM program chair, highlighted the urgent power constraints faced by the industry. He pointed out that 62% of data centers are exploring on-site power generation to enhance energy efficiency or resilience. Notably, nearly 19% of those surveyed indicated they plan to implement some form of behind-the-meter power generation by the end of 2024.

Behind-the-meter power generation involves constructing renewable energy sources directly at data center sites, enabling operators to mitigate grid congestion, prevent transmission losses, and enhance facility reliability. However, despite a strong interest in renewable energy, its availability remains low, which prevents AI operators from fully relying on it. Consequently, many are leaning towards natural gas and small modular reactors (SMRs) as feasible solutions to ensure a reliable energy supply.

AI Power Demands Force New Ideas

The surge in AI workloads presents a critical challenge that demands immediate solutions. Industry analysts project that AI will account for over 50% of global data center capacity and 70% of revenue opportunities due to significant productivity gains across all sectors. Research estimates indicate that over 35 GW of power for data centers will be self-generated by the year 2030.

Developers of AI factories are pushed to act swiftly, as waiting for traditional utility services to provide capacity can take several years. This urgency compels AI factory developers to seek behind-the-meter and "Bring Your Own Power" (BYOP) solutions.

As capital investments in the data center sector are expected to reach $1 trillion globally by 2030, physical power infrastructure will likely benefit the most from this investment surge.

Natural Gas has Favorable Economics

Many next-generation data center developers are willing to venture to locations rich in natural gas. While traditional metropolitan areas are often overlooked due to power shortages, some developers are now establishing sites next to vast wind and solar farms, although the majority opt for locations near natural gas basins, where extraction technology is mature and costs are low.

Between 2024 and 2030, the natural gas consumption of data centers is expected to triple, with data centers projected to use approximately 4.5% of U.S. gas consumption for electricity generation. To optimize operations, experts advocate for setting up data centers close to established natural gas basins and pipeline networks across various states.

Time to Power

In the race to provide cutting-edge AI services, the rush is also on to secure the necessary electrical components and infrastructure. Some gas turbines and electrical equipment have lead times that extend for years, underscoring the urgency for quicker energy solutions. Nuclear energy from SMRs also demands significant development time, potentially extending beyond five years.

The competitive landscape favors data center builders who can quickly access power. For instance, Applied Digital, led by COO Laura Laltrello, is utilizing stranded power from a wind farm in North Dakota for their AI factory, which is expected to provide more than 400 MW of energy. The struggle for power remains a primary barrier as major cities face grid constraints and scaling difficulties.

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