4 papers
Benchmarking Graph Neural Networks for AC Power Flow: The Role of Inductive Biases and Physical Constraints
Federico Clerici, Ferran Bohigas-Daranas, Hamid Latif-Martínez +3
Fast and accurate models for AC power flow (ACPF) are increasingly important as modern electrical grids require more frequent system analyses. Graph neural networks (GNNs) naturall…
Cybersecurity in Power Grids: Standards and Research Challenges
Ferran Bohigas-Daranas, Hamid Latif-Martinez, Nicolas Llorens +4
This paper examines Smart Grid cybersecurity, emphasizing the critical distinctions between IT and OT environments. It analyzes grid architecture, substation threats, and key inter…
Unified Heterogeneous Graph Neural Network solver for Power Flow, Optimal Power Flow and State Estimation
Ferran Bohigas-Daranas, Hamid Latif-Martínez, Eduardo Prieto-Araujo +2
Power Flow (PF), Optimal Power Flow (OPF), and State Estimation (SE) are fundamental problems in power system analysis, but solving them is computationally expensive. Graph Neural…
Power Flow Feasibility Assessment Using Variational Graph Autoencoders
Ferran Bohigas-Daranas, Hamid Latif-Martinez, Eduardo Prieto-Araujo +2
Data-driven methods, including graph neural networks, have been studied for accelerating power flow calculations in recent years, but very little attention has been paid to the sol…