AI is becoming one of the largest new loads on the global grid. Data-center electricity consumption is projected to reach ~945 TWh by 2030—more than doubling from 2022 levels [IEA]. AI-specific workloads will quadruple energy use this decade, reversing previous flat demand trends in advanced economies [IEA Exec Summary]. Energy abundance has therefore become a prerequisite for sovereign compute: while open-source models democratize algorithms, only nations with deep renewable reserves and strong grids can operate frontier-scale training clusters. Control of compute + data centers + energy is now considered a national-power lever.
Below are five regions best positioned—by 2030—to host large-scale, energy-intensive AI infrastructure.
CF - Capacity Factor (percentage of time a power plant operates at maximum output)
CREZ - [Competitive Renewable Energy Zones] (Texas initiative that built transmission infrastructure to connect wind-rich areas to major load centers)
EDWC - [Eastern Data, Western Compute] (China's national strategy to locate data centers and AI compute facilities in western energy-rich regions to relieve strain on coastal power grids)
ERCOT - Electric Reliability Council of Texas (manages Texas's electric grid)
GEM - Global Energy Monitor (organization tracking energy infrastructure projects)
GW - Gigawatt (unit of power equal to 1 billion watts)
IEA - International Energy Agency (global energy policy organization)
PPA - Power Purchase Agreement (long-term contracts between renewable energy generators and buyers like hyperscalers)
TWh - Terawatt-hour (unit of energy equal to 1 trillion watt-hours)
UHV - Ultra-High-Voltage (electricity transmission lines, specifically China's 800 kV+ lines capable of transmitting ~8 GW per line over long distances)
Inner Mongolia is expanding one of the world’s largest renewable portfolios, targeting 300+ GW wind+solar by 2030 (GEM global construction pipeline) [GEM]. The region already exports enormous electricity volumes via ultra-high-voltage (UHV) lines—315 TWh in 2024 alone [Inner Mongolia Gov.]. China’s Eastern Data, Western Compute strategy officially designates Inner Mongolia as a national compute zone, shifting training/inference workloads to energy-rich areas [EDWC]. Because AI data centers require continuous multi-GW draw, this combination of scale, climate, and grid capacity gives Inner Mongolia unmatched headroom for 2030.
The North Sea is building the planet’s densest offshore-wind basin. Regional governments target 100–120 GW of offshore wind by 2030 [NSWPH], forming the backbone of EU digital-sovereignty infrastructure. Grid development in the area—interconnectors, meshed subsea networks, and upcoming “energy islands”—creates bidirectional routing of multi-GW flows across the UK, Denmark, Germany, and the Netherlands. Hyperscalers already operate major cloud hubs here (Amsterdam, London, Frankfurt), supported by renewable PPAs. This is the most transmission-rich clean-energy zone in Europe, ideal for sustained AI workloads.
The Northeast hosts one of the world’s most productive wind belts: Brazil’s wind capacity exceeded 32 GW, projected to reach ~44 GW by 2028 [RatedPower]. Brazil’s grid interconnects the Northeast to the industrial southeast, enabling export of surplus clean power. Incentives under the national Redata program target hyperscale AI operators; one flagship ByteDance data center is powered by a dedicated 1 GW wind complex. Consistent wind (often 40%+ CF), strong solar, and large tracts of land combine to give Northeast Brazil a low-cost green-compute advantage within the Americas.
Xinjiang and Gansu are home to extreme-scale renewable projects, including the world’s 3.5 GW single-site solar farm built in 2025 [RenewablesNow]. China added 212 GW solar + 51 GW wind in the first half of 2025, much of it in western provinces [Reuters]. These regions feed eastern load centers through multiple 800 kV UHV lines capable of ~8 GW per line [UHV Spec]. Under EDWC, new cloud clusters and AI-processing hubs are sited directly in western renewables zones, reducing coastal strain and enabling continuous training at national scale.
Texas is the fastest-growing AI power hub in North America. It already hosts ~7.6 GW of data-center load, projected to soar to ~78 GW by 2031 [Powwr ERCOT Data]—a tenfold jump. Texas leads the U.S. in wind (40+ GW) and utility-scale solar (17+ GW and doubling by 2030) and benefits from rapid CREZ-era transmission buildouts. Combined with a permissive market and massive renewable queue, Texas could support multiple gigawatt-scale AI supercampuses, provided grid reliability and new gas peakers keep pace with load growth.
These five regions pair multi-gigawatt renewable surpluses with strong or rapidly scaling transmission, the two hard constraints on AI compute. Their buildouts align with the IEA’s observation that the U.S., China, and Europe will supply ~80% of global data-center growth through 2030 [IEA]. As AI becomes a national asset, regions that integrate energy → transmission → compute will define the next decade of geopolitical and technological leadership.