Explore/agent app/PowerAtlas: Towards Electricity-Computing Co-Scheduling for Power Systems
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Kaiwen Jiang, Siya Xu, Ziyue Zhu, Chao Yang, Anh Tuan Luu, Haoran Luo/PowerAtlas: Towards Electricity-Computing Co-Scheduling for Power SystemsUnknown

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 https://github.com/JAVA-Jiang/PowerAtlas.

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55 / 100
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55Score
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30C
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70C+
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45C
Community
42C
Production
58C
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2026-07-312026-08-01
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◎ Strengths

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55
Score 55/100
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