12 papers
An End-to-End Explainable AI Framework with Automated LLM-Based Natural Language Explanation Generation for Energy Systems
Venkata Sesha Sai Raj Nanduri, Akthar Hussain, Van-Hai Bui
Explainable AI (XAI) is important for deploying machine learning systems in domains where stakes are very high and where transparency, trust and accountability are critical. Althou…
Scalable Impedance Identification of Diverse IBRs via Cluster-Specialized Neural Networks
Quang Manh Hoang, Guilherme Vieira Hollweg, Bang Nguyen +3
Modern machine learning approaches typically identify the impedance of a single inverter-based resource (IBR) and assume similar impedance characteristics across devices. In modern…
Scalable Fairness Shaping with LLM-Guided Multi-Agent Reinforcement Learning for Peer-to-Peer Electricity Markets
Shrenik Jadhav, Birva Sevak, Srijita Das +3
Peer-to-peer (P2P) energy trading is becoming central to modern distribution systems as rooftop PV and home energy management systems become pervasive, yet most existing market and…
ANN-Based Grid Impedance Estimation for Adaptive Gain Scheduling in VSG Under Dynamic Grid Conditions
Quang-Manh Hoang, Van Nam Nguyen, Taehyung Kim +3
In contrast to grid-following inverters, Virtual Synchronous Generators (VSGs) perform well under weak grid conditions but may become unstable when the grid is strong. Grid strengt…
Enhancing Power Flow Estimation with Topology-Aware Gated Graph Neural Networks
Shrenik Jadhav, Birva Sevak, Srijita Das +2
Accurate and scalable surrogate models for AC power flow are essential for real-time grid monitoring, contingency analysis, and decision support in increasingly dynamic and inverte…
FairMarket-RL: LLM-Guided Fairness Shaping for Multi-Agent Reinforcement Learning in Peer-to-Peer Markets
Shrenik Jadhav, Birva Sevak, Srijita Das +3
Peer-to-peer (P2P) trading is increasingly recognized as a key mechanism for decentralized market regulation, yet existing approaches often lack robust frameworks to ensure fairnes…