基于技能的电力系统研究AI智能体
Skill-Based AI Agents for Power-System Studies
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中文总结 AI 辅助
本文提出基于MCP连接工程工具的技能型智能体框架，通过OpenAI SDK和Claude Code两条路径实现电力系统动态仿真自动化，显著加速输电规划研究流程。
中文摘要 AI 辅助
本文描述了一种基于技能的智能体框架，用于电力系统研究，该框架通过模型上下文协议（MCP）连接的工程工具实现。我们开发了一个自定义MCP服务器，以开放西门子PTI PSSE功能，用于潮流分析、动态仿真、结果提取和模型验证工作流。我们评估了基于可编程OpenAI Agents软件开发工具包（SDK）和Claude Code命令行界面（CLI）的两条实现路径，两者均使用可复用技能、子智能体、MCP工具、数据仓库连接以及本地Shell/Python执行。两种基于前沿模型的实现均成功执行了代表性研究任务。成功与否根据任务完成度、输出准确性以及人工专家干预的需求进行评估。基于公开数据集的结果表明，智能体系统可以极大加速输电规划研究中利用工业级仿真平台的电力系统动态仿真过程。这指向输电规划实践的转变，即智能体系统可以处理常规仿真设置和结果提取，使工程师能够将专家判断集中于场景设计和结果解读，而非工具操作。
英文摘要
This paper describes a skill-based agentic framework for power-system studies using Model Context Protocol (MCP)-connected engineering tools. A custom MCP server was developed to expose Siemens PTI PSSE functions for power-flow analysis, dynamic simulation, result extraction, and model-validation workflows. Two implementation pathways built on a programmable OpenAI Agents software development kit (SDK) and a Claude Code command-line interface (CLI) were evaluated, both using reusable skills, subagents, MCP tools, data-repository connections, and local shell/Python execution. Both frontier-model-based implementations successfully executed representative study tasks. Success was evaluated based on task completion, output accuracy, and the need for human expert interventions. Results based on public datasets show that agentic systems can greatly accelerate power system dynamic simulation process for transmission planning studies leveraging industry-grade simulation platforms. This points toward a shift in transmission planning practice, where agentic systems could handle routine simulation setup and result extraction, allowing engineers to focus expert judgment on scenario design and interpretation rather than tool operation.
发表机构
- PNNL（太平洋西北国家实验室）
机构由 AI 辅助整理，请以论文原文为准。
