PowerAtlas: Towards Electricity-Computing Co-Scheduling for Power Systems

Abstract:The rapid growth of AI workloads is turning data centers into large-scale, volatile, yet spatiotemporally flexible grid loads, creating an urgent need for coordinated electricity-computing scheduling. Under stringent grid constraints, schedules from general-purpose large language models (LLMs) are often infeasible, causing line-flow violations and unserved load. We present PowerAtlas, an LLM-agent framework for electricity-computing co-scheduling that integrates historical instances, domain knowledge, and physical constraints to produce joint decisions satisfying both grid operational rules and the service-level agreements (SLAs) of computing tasks. Working with a provincial power utility in China, we built an experimental electricity-computing network and validated the decision loop on real data-center data; from de-identified operational data we further constructed ECBench, a benchmark of 2,000 scheduling instances with oracle-optimal solutions. Experiments across eleven LLMs demonstrate the effectiveness of PowerAtlas under realistic physical operating conditions, with consistent feasibility and cost gains across three open-weight backbones. Our code is publicly available at this https URL.

17 pages, 9 figures, 5 tables. Code: this https URL

Machine Learning (cs.LG)

arXiv:2607.26710 [cs.LG]

(or arXiv:2607.26710v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.26710 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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