CONDUCTOR: An LLM-Orchestrated Digital Twin for Uncertainty-Aware Distribution Grid Operations

摘要

Large language models (LLMs) are proposed as natural-language interfaces to power system analysis, yet existing frameworks are validated almost exclusively on synthetic benchmarks and support only deterministic studies. We present CONDUCTOR, an LLM-orchestrated digital twin for distribution grid operations. An open-weights LLM orchestrates power system analysis and optimization solvers and, unlike prior systems, also performs uncertainty-aware studies: probabilistic security assessment, robust corrective dispatch, and flexibility-envelope and hosting-capacity characterization. We test it on the Bornholm 60 kV distribution network - a real Danish island power system - using one year of smart-meter measurements. An operator case study spans deterministic assessment, probabilistic risk quantification, and robust dispatch. Across a 68-prompt behavioral catalog scoring tool use, evidence consistency, state-mutation discipline, and refusal calibration, the orchestrator answers 98.5% of tasks correctly on the first attempt - the lone failure being a missing answer, not a wrong one. The full pipeline is released open source.