From AI Compute Growth to Grid Stress: A Conditional Scenario-Screening Framework for the EU-27 and the United Kingdom to 2035

Abstract

AI-led data-centre expansion requires distinctions between annual electricity, coincident power, conditional headroom exposure and emissions. We develop a scenario-screening framework for 28 European countries in 2030 and 2035. An illustrative 62.25 TWh regional benchmark combines an Irish metered anchor with assumed non-Irish allocations. Four scenarios separate service growth, computational efficiency, facility overhead, peak coincidence and grid intensity. Four analytical or conditional propositions replace empirical hypothesis tests. Reported uncertainty uses one archived 10,000-draw paired simulation; additional source checks and sensitivities are deterministic. Under full workload exposure, 2035 facility electricity ranges from 107.26 to 208.19 TWh. Accelerated AI adds 16.66 GW when national coincident-peak increments are summed, not a simultaneous European peak. At the same service index, coordinated transition reduces facility electricity by 22.4%, the peak increment by 62.2% and an assumption-based operational-emissions proxy from 30.11 to 14.25 Mt CO2, without procurement credits. This proxy is neither measured emissions nor an estimated marginal dispatch response. Published national benchmarks, alternative allocations and retrospective comparisons expose substantial boundary and growth-rate uncertainty. Synthetic hourly profiles additionally show that recovery demand can create a new peak. The contribution is a conditional comparison, not a validated national forecast, reliability assessment or estimate of AI-only electricity.