The rapid expansion of AI globally has led to the proliferation of energy-intensive hyperscale data centres (DCs), making them as a structurally challenging component in power system planning and operation. Using a spatially explicit optimisation model of Europe across 21 AI growth scenarios, we systematically quantify additional demand, capacity requirements, emissions, and operational impacts of DCs. Results indicate that AI could drive 73-723 TWh of extra demand by 2050, risking cumulative emissions overshoots of 67-181 MtCO2 between 2030 and 2050. Our analysis indicates that after 2030, the geography of AI infrastructure will be shaped more by firm power and system flexibility than by the mere abundance of clean energy. In moderate scenarios, AI requires an additional of 200 hours of firm generation, which increases LCOE by 35 EUR/MWh in key hubs. We show that even under the pessimistic scenarios, existing infrastructure would require 70 GW additional capacity, while under managed growth pathways, this expansion could reach 226 GW. We further find DCs workload dynamics strongly shape energy dispatch, system flexibility, and emissions, while improved efficiency significantly reduces capacity needs, and system peaks. While our findings suggest that net-zero targets for 2050 may be achieved, critical emission risks may appear in the intermediate years, and the EU may compromise its carbon-neutral goals unless policies adapt to this accelerating digital transformation.

翻译：全球人工智能的快速扩张推动了高能耗超大规模数据中心(Data Centres, DCs)的激增，使其成为电力系统规划与运行中一项结构性挑战。通过使用覆盖21种AI发展情景、空间显式优化的欧洲模型，我们系统地量化了数据中心带来的额外电力需求、容量要求、排放量及运行影响。结果表明，到2050年AI可能驱动73-723太瓦时的额外电力需求，导致2030年至2050年间累计排放超标67-181兆吨二氧化碳。分析表明，2030年后，AI基础设施的地理布局将更多地取决于稳定电源与系统灵活性，而非单纯的可再生能源丰裕度。在适度情景下，AI需额外增加200小时的稳定发电，这使主要枢纽的平准化电力成本(LCOE)上升35欧元/兆瓦时。我们证明，即使在悲观情景下，现有基础设施仍需新增70吉瓦容量，而在可控增长路径下，这一扩张规模可达226吉瓦。进一步研究发现，数据中心的工作负载动态深刻影响能源调度、系统灵活性与排放特性，而效率提升可显著降低容量需求与系统峰值。尽管研究结果表明2050年净零排放目标可能实现，但中期年份可能面临关键排放风险，若政策不能适应加速的数字转型，欧盟可能危及其中性目标。