🤖 AI Summary
This work addresses the projected shortage of 1.5 million distribution engineering professionals by 2030 by proposing a natural language interaction framework based on the Model Context Protocol (MCP), which enables the first deep integration of large language models (LLMs) with OpenDSS. The framework supports a range of engineering tasks—including power flow analysis, voltage profiling, quasi-static time-series simulation, and automated optimization—through 36 domain-specific tools and three core optimization capabilities, such as capacitor placement and overvoltage mitigation. Designed for both cloud-edge协同 and offline deployment, the system reduces execution time for typical workflows like distributed energy resource interconnection screening from several hours to under two minutes, while maintaining full fidelity with native OpenDSS scripts. An interactive web platform provides real-time visualization and immediate user feedback.
📝 Abstract
The power distribution engineering workforce faces a projected shortage of up to 1.5 million engineers by 2030, creating urgent demand for more accessible analysis tools. This paper introduces Grid-Orch, a framework that bridges Large Language Models (LLMs) and power system simulation through the Model Context Protocol (MCP), enabling engineers to perform complex distribution analyses via natural language. Using OpenDSS as the reference implementation, Grid-Orch provides 36 domain-specific tools across eleven categories, covering power flow, voltage analysis, quasi-static time series (QSTS) simulation, and automated optimization. A provider-agnostic LLM layer supports both cloud-hosted (Gemini, Claude) and locally deployed (Ollama, llama-cpp) models, enabling air-gapped operation for security-sensitive utility environments. Three optimization skills, capacitor placement, voltage violation analysis, and overvoltage mitigation, extend the platform beyond single-tool queries to multi-step engineering workflows. Grid-Orch is delivered as an interactive web platform with chat-based interaction, a QSTS dashboard, and feeder topology visualization, and renders simulation results inline. Workflow demonstrations show that distribution analyses formerly requiring hours of scripting, such as distributed energy resource (DER) interconnection screening, complete in under two minutes through natural language, producing numerically identical results to direct OpenDSS scripting.