适应快速变化的数据中心负载带来的隐性经济后果
Hidden Economic Consequences of Adapting to Fast Ramping Datacenter Loads
AI总结：
该研究针对数据中心负载快速变化的问题，通过电网模拟发现数据中心会加剧潜在负载瓶颈，导致非高峰时段系统成本上升，揭示了提前调整缓慢发电的隐性经济代价。
AI中文摘要：
人工智能工作负载推动数据中心基础设施快速扩张，给美国能源系统带来巨大压力。虽然高峰电价是可预见的结果，在高峰时段模拟中可测量，但非高峰时段的高电价是被严重低估的威胁。我们基于改进的IEEE 118节点电网，在可拥堵的5000节点系统上模拟不同的负载变化条件，结果显示在存在快速变化的负载和缓慢变化的发电时，数据中心会加剧潜在的负载瓶颈，导致数据中心高峰时段之外出现意外的高系统成本。我们用两种不同的负载条件测试数据中心的影响，发现在系统重合高峰时段，耦合模拟会使缓慢且昂贵的机组负载达到100%，边际成本平均上升8%。这些发现极为重要，因为它们揭示了为应对数据中心负载变化而提前调整缓慢发电所带来的隐性成本。
英文摘要：
Artificial intelligence workloads are driving the rapid expansion of datacenter infrastructure, which imposes substantial stress on the US energy system. While high peak electricity prices are an anticipated outcome, measurable under peak hour simulations, the high off peak prices are a significantly underestimated threat. We simulate different ramping conditions on a congestible 5000-bus system, based on a modified IEEE 118-bus grid, to show that, in the presence of fast ramping loads and slow ramping generation, datacenters can aggravate latent load pockets. This results in unexpectedly high system costs during periods outside of datacenter peak. We test the datacenter effects using two distinct load conditions. We find that in the system coincident peak, coupled simulations result in up to 100% loading of slow expensive units, with an average marginal cost increase of 8%. These findings are of extreme importance as they reveal the hidden costs of preventively ramping slow generation in anticipation of datacenter load changes.
