Abstract
The rapid expansion of AI data centers is straining electricity grids alarmingly, forcing data center planners to navigate two-pronged challenges: (1) lengthy interconnection queue delays undermining immediate grid access, and (2) volatile electricity prices that spike dramatically during high demand events. This convergence forces planners to reconsider traditional grid-only strategies. While behind-the-meter (BTM) generation offers a solution, existing research lacks comprehensive frameworks for identifying technology portfolios under combined uncertainties of grid access delays and market volatility. This study develops a two-stage stochastic optimization framework with binary capacity constraints co-optimizing data center location and BTM energy portfolios under these challenges. The model evaluates conventional (gas turbines), renewable (solar, wind, batteries), and emerging technologies (hydrogen fuel cells, small modular reactors) across four progressive scenarios spanning emission targets, demand flexibility, grid curtailment, land constraints, and queue delays, contrasting stochastic and deterministic solutions. Applied to a 5 GW data center expansion in ERCOT, three insights emerge: First, queue delays drive 2.7 GW bridging investments that transition to 92% grid reliance once interconnection is available, while stochastic optimization maintains higher BTM utilization against price volatility. Second, demand flexibility reduces required BTM capacity through load shifting during grid curtailment events, decreasing gas deployment from 2.1 to 1.7 GW. Third, land-constrained decarbonization shifts from wind-dominated portfolios to capital-intensive solar-hydrogen-SMR solutions, with stochastic optimization tripling SMR deployment to prioritize reliability under uncertainty.