让数据中心用电与电网调度协同，避免线路过载。
PowerAtlas: Towards Electricity-Computing Co-Scheduling for Power Systems
- 用大模型结合电网规则和历史数据，联合调度计算任务与电力供应。
- 在2000个真实场景中验证，方案可避免线路过载且降低运行成本。
- 适合电网调度、数据中心运维人员，尤其关注绿色算力的团队。
AI工作负载的快速增长使数据中心成为大规模、波动性强但时空灵活的电网负荷，亟需电力与计算协同调度。在严格电网约束下，通用大语言模型（LLMs）生成的调度计划常不可行，导致线路过载和未满足负荷。我们提出PowerAtlas，一种基于LLM代理的电力-计算协同调度框架，融合历史实例、领域知识与物理约束，生成同时满足电网运行规则与计算任务SLA的联合决策。与国内某省级电网机构合作，构建实验性电力-计算网络，并在真实数据中心数据上验证决策闭环；从脱敏运营数据中进一步构建ECBench基准，包含2000个调度实例及最优解。在11个LLM上实验表明，PowerAtlas在真实物理条件下持续实现可行性提升与成本优化，三种开源模型均表现稳定。代码已公开于https://github.com/JAVA-Jiang/PowerAtlas。
原文摘要 · Abstract (English)
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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