AI Data Center Site Selection
IWE AI Infra uses the IWE Multicriteria Site Selection (IMSS) methodology. It’s a structured, systems-engineering approach developed from our work siting wind and solar projects and, more recently, planning a green AI data center hub with the State of Gujarat. IMSS weighs every site across the criteria that decide a campus’s cost, schedule and resilience for decades.
What we evaluate
- Power and energy cost: the lowest delivered cost of electricity over the life of the asset while meeting the campus’s clean-energy goal, whether that is an annual renewable share or 24/7 hourly carbon-free matching. This weighs the cost and availability of wind, solar, round-the-clock renewables and battery storage, transmission access, and the carbon credits needed to offset residual grid power.
- Time to power: how quickly the campus can be energized. This covers available capacity at nearby substations and on existing interconnection agreements, the length of the load-study queue, and upgrade lead times. It also covers faster routes: bringing your own power through co-located or contracted generation, accepting flexible or curtailable interim service, and phasing load to match grid capacity.
- Grid infrastructure: proximity to high-capacity transmission (typically 400 kV and above) and its long-term expansion plans.
- Digital connectivity: long-haul fiber, multiple carriers, latency, distance to cable landing stations, and redundant network paths.
- Water and cooling: reliable water supply, or suitability for water-efficient, closed-loop cooling.
- Land and geophysics: large contiguous parcels; areas with slopes above 5° are excluded; seismic and geotechnical risk.
- Risk and security: distance from borders and conflict zones, and other long-term security risks.
- Environment and community: environmentally sensitive areas are excluded and social impacts are assessed early.
- Climate resilience and readiness: flood, sea-level rise and extreme-weather exposure; roads, ports and construction supply chains.
Availability zones, not just sites
Hyperscale AI workloads need geographic redundancy. Rather than concentrating capacity in one place, we design triangular availability zones: clusters of campuses close enough to work together, far enough apart to avoid correlated risks. The result is redundant power and network paths, load balancing across campuses, and resilience to local disruptions.
Training campuses and edge sites
Not every AI workload needs a gigawatt campus. Training clusters belong at a few large sites on 400 kV transmission. Inference, which is the majority of AI compute by volume, can follow the power. Edge AI data centers of 10–50 MW placed next to renewable plants on existing 33/132 kV feeders can be built in 12–24 months instead of waiting 3-4 years for a transmission connection, while absorbing renewable energy that would otherwise be curtailed.
Outcome
A site decision that is defensible and bankable. Sites are ranked on lifecycle cost, time to power and risk across power, connectivity, land and environment, rather than on any single factor or short-term opportunity.
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