164 citations · 851 across the 77 of their papers we have counts for
85 papers
RAPTOR: RAndom-projection Physics-informed Transient sOlveR
Petros Ellinas, Benjamin Vilmann, Spyros Chatzivasileiadis +1
The complexity of time-domain simulation of modern power systems has increased significantly because converter-based resources introduce control dynamics that must be simulated alo…
Tools to Explain Neural Networks for Power System Dynamics
Petros Ellinas, Johanna Vorwerk, Spyros Chatzivasileiadis
This paper presents, for the first time in power systems literature to our knowledge, analytical tools to explain the training performance of machine learning surrogate models for…
CONDUCTOR: An LLM-Orchestrated Digital Twin for Uncertainty-Aware Distribution Grid Operations
Antonio Alcántara, Aysegül Kahraman, Anosh Arshad Sundhu +1
Large language models (LLMs) are proposed as natural-language interfaces to power system analysis, yet existing frameworks are validated almost exclusively on synthetic benchmarks…
Digital Twin for Real-Time Security Assessment and Flexibility Activation in the Bornholm Distribution System
Anosh Arshad Sundhu, Aysegül Kahraman, Spyros Chatzivasileiadis
The increasing penetration of distributed energy resources (DERs) is transforming distribution networks into actively managed systems, introducing challenges related to voltage reg…
Verification and Validation of Physics-Informed Surrogate Component Models for Dynamic Power-System Simulation
Petros Ellinas, Indrajit Chaudhuri, Johanna Vorwerk +1
Physics-informed machine learning surrogates are increasingly explored to accelerate dynamic simulation of generators, converters, and other power grid components. The key question…
Trustworthiness Layer for Foundation Models in Power Systems: Application to N-k Contingency Screening
Antonio Alcántara, Spyros Chatzivasileiadis
We propose a model-agnostic trustworthiness layer that equips any foundation model (FM) for power systems with statistically valid prediction intervals. The layer offers two calibr…
Neural Operators for Power Systems: A Physics-Informed Framework for Modeling Power System Components
Ioannis Karampinis, Petros Ellinas, Johanna Vorwerk +1
Modern power systems require fast and accurate dynamic simulations for stability assessment, digital twins, and real-time control, but classical ODE solvers are often too slow for…
Neural Networks for AC Optimal Power Flow: Improving Worst-Case Guarantees during Training
Bastien Giraud, Rahul Nellikkath, Rahul Nellikath +3
The AC Optimal Power Flow (AC-OPF) problem is central to power system operations but remains computationally challenging to solve due to its non-convex, nonlinear nature. Neural ne…
Physics-Informed Neural Network Models for EMT Simulators
Ignasi Ventura Nadal, Nicolae Darii, Petros Aristidou +4
This is the first paper, to the best of our knowledge, to propose a framework that integrates Physics-Informed Neural Network (PINN) models in Electromagnetic Transient (EMT) simul…
Toolbox for Developing Physics Informed Neural Networks for Power Systems Components
Ioannis Karampinis, Petros Ellinas, Ignasi Ventura Nadal +2
This paper puts forward the vision of creating a library of neural-network-based models for power system simulations. Traditional numerical solvers struggle with the growing comple…
Physics-Informed Neural Networks in Power System Dynamics: Improving Simulation Accuracy
Ignasi Ventura Nadal, Rahul Nellikkath, Spyros Chatzivasileiadis
The importance and cost of time-domain simulations when studying power systems have exponentially increased in the last decades. With the growing share of renewable energy sources,…
A Dataset Generation Toolbox for Dynamic Security Assessment: On the Role of the Security Boundary
Bastien Giraud, Lola Charles, Agnes Marjorie Nakiganda +2
Dynamic security assessment (DSA) is crucial for ensuring the reliable operation of power systems. However, conventional DSA approaches are becoming intractable for future power sy…
Dual Pricing to Prioritize Renewable Energy and Consumer Preferences in Electricity Markets
Emilie Jong, Samuel Chevalier, Spyros Chatzivasileiadis +1
Electricity markets currently fail to incorporate preferences of buyers, treating polluting and renewable energy sources as having equal social benefit under a system of uniform cl…
Scalable Exact Verification of Optimization Proxies for Large-Scale Optimal Power Flow
Rahul Nellikkath, Mathieu Tanneau, Pascal Van Hentenryck +1
Optimal Power Flow (OPF) is a valuable tool for power system operators, but it is a difficult problem to solve for large systems. Machine Learning (ML) algorithms, especially Neura…
A hybrid Quantum-Classical Algorithm for Mixed-Integer Optimization in Power Systems
Petros Ellinas, Samuel Chevalier, Spyros Chatzivasileiadis
Mixed Integer Linear Programming (MILP) can be considered the backbone of the modern power system optimization process, with a large application spectrum, from Unit Commitment and…
Physics-Informed Neural Networks: a Plug and Play Integration into Power System Dynamic Simulations
Ignasi Ventura Nadal, Jochen Stiasny, Spyros Chatzivasileiadis
Time-domain simulations are crucial for ensuring power system stability and avoiding critical scenarios that could lead to blackouts. The next-generation power systems require a si…
Bayesian Physics-informed Neural Networks for System Identification of Inverter-dominated Power Systems
Simon Stock, Davood Babazadeh, Christian Becker +1
While the uncertainty in generation and demand increases, accurately estimating the dynamic characteristics of power systems becomes crucial for employing the appropriate control a…
Correctness Verification of Neural Networks Approximating Differential Equations
Petros Ellinas, Rahul Nellikath, Ignasi Ventura +2
Verification of Neural Networks (NNs) that approximate the solution of Partial Differential Equations (PDEs) is a major milestone towards enhancing their trustworthiness and accele…
Error estimation for physics-informed neural networks with implicit Runge-Kutta methods
Jochen Stiasny, Spyros Chatzivasileiadis
The ability to accurately approximate trajectories of dynamical systems enables their analysis, prediction, and control. Neural network (NN)-based approximations have attracted sig…
Graph Neural Networks for Fast Contingency Analysis of Power Systems
Agnes M. Nakiganda, Spyros Chatzivasileiadis
The successful integration of machine learning models into decision support tools for grid operation hinges on effectively capturing the topological changes in daily operations. Fr…
Stochastic Quantum Power Flow for Risk Assessment in Power Systems
Brynjar Sævarsson, Hjörtur Jóhannsson, Spyros Chatzivasileiadis
This paper introduces the first quantum computing framework for Stochastic Quantum Power Flow (SQPF) analysis in power systems. The proposed method leverages quantum states to enco…
Quantum Amplitude Estimation for Probabilistic Methods in Power Systems
Emilie Jong, Brynjar Sævarsson, Hjörtur Jóhannsson +1
This paper introduces quantum computing methods for Monte Carlo simulations in power systems which are expected to be exponentially faster than their classical computing counterpar…
GPU-Accelerated Verification of Machine Learning Models for Power Systems
Samuel Chevalier, Ilgiz Murzakhanov, Spyros Chatzivasileiadis
Computational tools for rigorously verifying the performance of large-scale machine learning (ML) models have progressed significantly in recent years. The most successful solvers…
Enriching Neural Network Training Dataset to Improve Worst-Case Performance Guarantees
Rahul Nellikkath, Spyros Chatzivasileiadis
Machine learning algorithms, especially Neural Networks (NNs), are a valuable tool used to approximate non-linear relationships, like the AC-Optimal Power Flow (AC-OPF), with consi…