🤖 AI Summary
This study addresses how interconnection queue constraints in traditional power grids limit AI data center siting, while existing cost assessments often overlook real-time electricity pricing, backup charges, and actual load profiles, thereby failing to inform investment decisions effectively. For the first time, this work endogenizes grid electricity prices and integrates empirical GPU load data, fuel costs, full lifecycle carbon emissions, and U.S. 45V/48E tax credits into a comprehensive site-level cost framework. Employing lifecycle cost analysis, carbon footprint tracking, and investment reverse-engineering, the paper evaluates the economic and environmental performance of nine on-site power generation technologies. Findings reveal that natural gas combined-cycle generation costs ($114/MWh) exceed grid prices ($92/MWh); green hydrogen-based power costs 1.9–2.7 times more than grid supply yet reduces emissions by 85%. On-site generation is fundamentally a product of interconnection capacity and load depth, making cost parity elusive and suggesting current investment strategies may be misaligned.
📝 Abstract
Interconnection queues, not electricity prices, now govern where data centers can be built, and the standard levelized-cost comparison answers a question no developer faces: it assumes a load profile, freezes the grid price while modeling the demand that moves it, and quotes busbar costs a facility cannot buy. This paper evaluates nine on-site supply technologies against a delivered grid whose price is endogenous to projected data-center demand, on a complete-site basis that retains standby charges, with measured GPU training load, delivered fuel prices, production-pathway carbon, and statutory 45V and 48E incentive mechanics. Nothing beats the wire: gas combined cycle produces at 47 USD/MWh but costs about 114 USD per megawatt-hour of complete site energy against a 92 USD grid; four-hour storage is physically capped near 18 percent of annual energy and, charged at the margin, dirtier than the grid; hydrogen from grid-priced power fails on cost and carbon together. An investment inversion converts these findings into capital terms: conversion-hardware learning buys nothing, because free hardware still exceeds the grid for every low-carbon arm, while global electrolyser deployment on sited sub-20 USD/MWh power brings PEM hydrogen power to about 2.2 times the grid at 300 billion USD and 1.9 times at 1 trillion USD (2.7 and 2.3 for the hydrogen engine), with a carbon reduction of roughly 85 percent (6.8-fold) against grid-power production. Grid parity is not purchasable at any budget. On-site supply is an access and depth product; most current investment targets the wrong term.