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

• URL: http://arxiv.org/abs/2607.26710v1
• Date: Wed, 29 Jul 2026 09:53:50 GMT
• Title: PowerAtlas: Towards Electricity-Computing Co-Scheduling for Power Systems
• Abstract summary: PowerAtlas is a framework for electricity-computing co-scheduling.<n>It integrates historical instances, domain knowledge, and physical constraints.<n>It produces joint decisions satisfying grid operational rules and service-level agreements.
• Score: 42.32975870695121
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 https://github.com/JAVA-Jiang/PowerAtlas.

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