6 papers
A Novel Method for Differential-Algebraic Dynamic Model Discovery in Power Systems: An LLM-Based Multi-Agent Collaborative Framework
Xinming Wang, Fan Tang, Yingli Wei +6
With large-scale integration of emerging power electronic devices represented by grid-forming inverters, power system dynamics increasingly exhibit strong nonlinearity, multi-times…
SkillPager: Query-Adaptive Intra-Skill Navigation via Semantic Node Retrieval
Zicai Cui, Zihan Guo, Weiwen Liu +1
Skill-based LLM agents increasingly rely on long procedural documents, but full-document prompting wastes tokens and dilutes information critical to execution. We study this settin…
ProOPF: Benchmarking and Improving LLMs for Professional-Grade Power Systems Optimization Modeling
Chao Shen, Zihan Guo, Xu Wan +6
Growing renewable penetration introduces substantial uncertainty into power system operations, necessitating frequent adaptation of dispatch objectives and constraints and challeng…
OptArgus: A Multi-Agent System to Detect Hallucinations in LLM-based Optimization Modeling
Zhong Li, Zihan Guo, Xiaohan Lu +5
Large language models (LLMs) are increasingly used to translate natural-language optimization problems into mathematical formulations and solver code, but matching the reference ob…
Holos: A Web-Scale LLM-Based Multi-Agent System for the Agentic Web
Xiaohang Nie, Zihan Guo, Zicai Cui +20
As large language models (LLM)-driven agents transition from isolated task solvers to persistent digital entities, the emergence of the Agentic Web, an ecosystem where heterogeneou…
LLM-DMD: Large Language Model-based Power System Dynamic Model Discovery
Chao Shen, Zihan Guo, Ke Zuo +2
Current model structural discovery methods for power system dynamics impose rigid priors on the basis functions and variable sets of dynamic models while often neglecting algebraic…