PowerAtlas: Feasibility-Aware Language Models for Electricity-Computing Co-Scheduling

arXiv:2607.26710 · cs.LG · Submitted 2026-07-29 · Read on arXiv

cs.LG

Submitted: 2026-07-29

Updated: 2026-10-15

Comments: 28 pages, 9 figures, 16 tables. Code: https://github.com/JAVA-Jiang/PowerAtlas

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

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 8 episodes
• Physical Constraints — 1× in this paper · explained in 7 episodes

Sources

• LLMs can Schedule
• The Llama 3 Herd of Models
• Rethinking Scientific Modeling: Toward Physically Consistent and Simulation-Executable Programmatic Generation
• Watts vs. Bytes: Turning Data Centers into Grid Assets via Storage Compute Co-Optimization
• A Novel Hierarchical Co-Optimization Framework for Coordinated Task Scheduling and Power Dispatch in Computing Power Networks
• Joint Energy Dispatch and Unit Commitment in Microgrids Based on Deep Reinforcement Learning
• Large Language Models for Power System Applications: A Comprehensive Literature Survey
• Proximal Policy Optimization Algorithms
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• Data Center Spatio-Temporal Load Flexibility in Security-Constrained Unit Commitment for Enhanced Grid Efficiency and Reliability
• Power-Flexible AI Data Centers: A New Paradigm for Grid-Responsive Compute
• Qwen3 Technical Report
• Exploring the Impacts of Power Grid Signals on Data Center Operations using a Receding-Horizon Scheduling Model
• ElecBench: a Power Dispatch Evaluation Benchmark for Large Language Models

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