17 papers
Operationally Feasible Synthetic Power-Grid Scenarios via Learning the AC-Operable Joint Distribution
Chenhan Xiao, Xinyu He, Haoran Li +2
Synthetic power-grid scenarios are essential for planning, resilience assessment, contingency analysis, and data-driven power-system applications. Recent synthetic grid generation…
Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference
Haoran Li, Lihao Mai, Muhao Guo +2
Accurate distribution system topology is essential for outage localization, voltage analytics, and operation of distribution grids, yet maintaining reliable connectivity records re…
Predicting Power-System Dynamic Trajectories with Foundation Models
Haoran Li, Lihao Mai, Chenhan Xiao +2
As power systems transition toward renewable-rich and inverter-dominated operations, accurate time-domain dynamic analysis becomes increasingly critical. Such analysis supports key…
Retrieval-Guided Photovoltaic Inventory Estimation from Satellite Imagery for Distribution Grid Planning
Muhao Guo, Lihao Mai, Erik Blasch +3
The rapid expansion of distributed rooftop photovoltaic (PV) systems introduces increasing uncertainty in distribution grid planning, hosting capacity assessment, and voltage regul…
Graph Structure Learning with Privacy Guarantees for Open Graph Data
Muhao Guo, Jiaqi Wu, Yizheng Liao +3
Publishing open graph data while preserving individual privacy remains challenging when data publishers and data users are distinct entities. Although differential privacy (DP) pro…
LASS-ODE: Scaling ODE Computations to Connect Foundation Models with Dynamical Physical Systems
Haoran Li, Chenhan Xiao, Lihao Mai +2
Foundation models have transformed language, vision, and time series data analysis, yet progress on dynamic predictions for physical systems remains limited. Given the complexity o…