PowerChain让电力系统分析自动执行，还能验证结果可靠性。
PowerChain: A Verifiable Agentic AI System for Automating Distribution Grid Analyses
- 用电网工具自动生成结构化分析上下文，支持新任务泛化
- 在真实电网数据上性能比基线提升最高144%
- 适合需要自动化、可验证的电力系统规划人员
快速电气化与脱碳化进程正加剧配电系统（DG）运行与规划的复杂性，亟需先进计算分析以保障系统可靠性与韧性。这些分析依赖于涵盖复杂模型、函数调用和数据流水线的异构工作流，高度依赖专家知识且难以自动化。人力与预算限制进一步制约了其规模化应用。为此，我们构建了自主分析配电系统复杂问题的智能体系统 PowerChain。现有智能体系统通常针对预定义任务定制开发，难以泛化到未见任务。相比之下，PowerChain通过利用自包含电网工具（如 GridLAB-D）提供的监督信号，结合专家标注并验证的推理轨迹，动态生成结构化上下文，实现对自然语言描述的复杂配电任务的泛化处理。在真实电网数据上的实证结果显示，PowerChain 在性能上相较基线最高提升 144%。
原文摘要 · Abstract (English)
Rapid electrification and decarbonization are increasing the complexity of distribution grid (DG) operation and planning, necessitating advanced computational analyses to ensure reliability and resilience. These analyses depend on disparate workflows comprising complex models, function calls, and data pipelines that require substantial expert knowledge and remain difficult to automate. Workforce and budget constraints further limit utilities' ability to apply such analyses at scale. To address this gap, we build an agentic system PowerChain, which is capable of autonomously performing complex grid analyses. Existing agentic AI systems are typically developed in a bottom-up manner with customized context for predefined analysis tasks; therefore, they do not generalize to tasks that the agent has never seen. In comparison, to generalize to unseen DG analysis tasks, PowerChain dynamically generates structured context by leveraging supervisory signals from self-contained power systems tools (e.g., GridLAB-D) and an optimized set of expert-annotated and verified reasoning trajectories. For complex DG tasks defined in natural language, empirical results on real utility data demonstrate that PowerChain achieves up to a 144/% improvement in performance over baselines.
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