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arXiv · 2607.28833

Hidden Economic Consequences of Adapting to Fast Ramping Datacenter Loads

Willa Gutowski·Charles Foltz·Nicholson Koukpaizan·Slaven Peles·Eve Tsybina
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Abstract

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.

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Willa Gutowski, Charles Foltz, Nicholson Koukpaizan, Slaven Peles, Eve Tsybina. 2026-07-30. Hidden Economic Consequences of Adapting to Fast Ramping Datacenter Loads. https://arxiv.org/abs/2607.28833

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