11 citations · 34 across the 21 of their papers we have counts for
6 papers · 1 filter
Global Performance Guarantees for Neural Network Models of AC Power Flow
Samuel Chevalier, Spyros Chatzivasileiadis
Machine learning, which can generate extremely fast and highly accurate black-box surrogate models, is increasingly being applied to a variety of AC power flow problems. Rigorously…
Emission-Aware Optimization of Gas Networks: Input-Convex Neural Network Approach
Vladimir Dvorkin, Samuel Chevalier, Spyros Chatzivasileiadis
Gas network planning optimization under emission constraints prioritizes gas supply with the least CO intensity. As this problem includes complex physical laws of gas flow, sta…
Interpretable Machine Learning for Power Systems: Establishing Confidence in SHapley Additive exPlanations
Robert I. Hamilton, Jochen Stiasny, Tabia Ahmad +5
Interpretable Machine Learning (IML) is expected to remove significant barriers for the application of Machine Learning (ML) algorithms in power systems. This letter first seeks to…
Optimization-Based Exploration of the Feasible Power Flow Space for Rapid Data Collection
Ignasi Ventura Nadal, Samuel Chevalier
This paper provides a systematic investigation into the various nonlinear objective functions which can be used to explore the feasible space associated with the optimal power flow…
Towards Optimal Kron-based Reduction Of Networks (Opti-KRON) for the Electric Power Grid
Samuel Chevalier, Mads R. Almassalkhi
For fast timescales or long prediction horizons, the AC optimal power flow (OPF) problem becomes a computational challenge for large-scale, realistic AC networks. To overcome this…
Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems
Jochen Stiasny, Samuel Chevalier, Rahul Nellikkath +2
Deep decarbonization of the energy sector will require massive penetration of stochastic renewable energy resources and an enormous amount of grid asset coordination; this represen…