3 citations · 7 across the 11 of their papers we have counts for
9 papers · 1 filter
LLMs are Few-Shot Decision-Makers: Generalized Context-Aware Microgrid Frequency Control through Prompt Decision Transformer
Xu Yang, Chenhui Lin, Haotian Liu +3
The rapid evolution of energy structures has positioned microgrids as pivotal components of next-generation power systems, offering enhanced resilience and renewable energy integra…
Two-Stage Active Distribution Network Voltage Control via LLM-RL Collaboration: A Hybrid Knowledge-Data-Driven Approach
Xu Yang, Chenhui Lin, Xiang Ma +4
The growing integration of distributed photovoltaics (PVs) into active distribution networks (ADNs) has exacerbated operational challenges, making it imperative to coordinate diver…
One Request, Multiple Experts: LLM Orchestrates Domain Specific Models via Adaptive Task Routing
Xu Yang, Chenhui Lin, Haotian Liu +3
With the integration of massive distributed energy resources and the widespread participation of novel market entities, the operation of active distribution networks (ADNs) is prog…
Large Language Model as An Operator: An Experience-Driven Solution for Distribution Network Voltage Control
Xu Yang, Chenhui Lin, Licheng Sha +5
With the advanced reasoning, contextual understanding, and information synthesis capabilities of large language models (LLMs), a novel paradigm emerges for the autonomous generatio…
Physics-Informed Recurrent Network for State-Space Modeling of Gas Pipeline Networks
Siyuan Wang, Wenchuan Wu, Chenhui Lin +3
As a part of the integrated energy system (IES), gas pipeline networks can provide additional flexibility to power systems through coordinated optimal dispatch. An accurate pipelin…
RL2: Reinforce Large Language Model to Assist Safe Reinforcement Learning for Energy Management of Active Distribution Networks
Xu Yang, Chenhui Lin, Haotian Liu +1
As large-scale distributed energy resources are integrated into the active distribution networks (ADNs), effective energy management in ADNs becomes increasingly prominent compared…