Flexibility-aware framework for efficient planner-initiated siting of data center.

Area of Science:

• Power Systems Engineering
• Energy Economics
• Grid Modernization

Background:

• Rapid expansion of energy-intensive AI data centers is outpacing power grid infrastructure development.
• Current interconnection processes are reactive, leading to significant delays for new energy projects.

Purpose of the Study:

• To introduce a proactive planner-initiated siting framework for AI data centers.
• To assess the impact of operational flexibility on grid interconnection and siting.
• To accelerate the integration of large flexible loads while maintaining grid stability.

Main Methods:

• Developed a framework integrating reliability-gated screening, system-wide market impact assessment, and multi-criteria scoring.
• Evaluated standardized flexibility envelopes: firm, pause, and shift.
• Applied the framework to a synthetic Texas power system.

Main Results:

• Operational flexibility expanded the AI data center siting frontier by 9-21% depending on capacity (1-2 GW).
• Median energy prices remained stable, and the 'shift' flexibility envelope reduced peak-hour price dispersion by 3.4%.
• The framework enables interconnection in 12-18 months, a 3.5-4 year reduction compared to conventional methods.

Conclusions:

• The proposed framework facilitates proactive decision-making for system operators and regulators.
• It enables faster integration of flexible loads like AI data centers without compromising grid reliability or market stability.
• This approach streamlines the interconnection process, addressing critical infrastructure challenges posed by growing energy demands.