11 citations · 34 across the 21 of their papers we have counts for
19 papers · 1 filter
Large-scale Grid Optimization: The Workhorse of Future Grid Computations
Amritanshu Pandey, Mads Almassalkhi, Sam Chevalier
Purpose: The computation methods for modeling, controlling and optimizing the transforming grid are evolving rapidly. We review and systemize knowledge for a special class of compu…
Scalable Bilevel Optimization for Generating Maximally Representative OPF Datasets
Ignasi Ventura Nadal, Samuel Chevalier
New generations of power systems, containing high shares of renewable energy resources, require improved data-driven tools which can swiftly adapt to changes in system operation. M…
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…
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…
Uncertainty Quantification in LV State Estimation Under High Shares of Flexible Resources
Nils Müller, Samuel Chevalier, Carsten Heinrich +2
The ongoing electrification introduces new challenges to distribution system operators (DSOs). Controllable resources may simultaneously react to price signals, potentially leading…
Learning without Data: Physics-Informed Neural Networks for Fast Time-Domain Simulation
Jochen Stiasny, Samuel Chevalier, Spyros Chatzivasileiadis
In order to drastically reduce the heavy computational burden associated with time-domain simulations, this paper introduces a Physics-Informed Neural Network (PINN) to directly le…
Handling Initial Conditions in Vector Fitting for Real Time Modeling of Power System Dynamics
Tommaso Bradde, Samuel Chevalier, Marco De Stefano +2
This paper develops a predictive modeling algorithm, denoted as Real-Time Vector Fitting (RTVF), which is capable of approximating the real-time linearized dynamics of multi-input…
Network Topology Invariant Stability Certificates for DC Microgrids with Arbitrary Load Dynamics
Samuel Chevalier, Federico Martin Ibanez, Kathleen Cavanagh +3
DC microgrids are prone to small-signal instabilities due to the presence of tightly regulated loads. This paper develops a decentralized stability certificate which is capable of…
Accelerated Probabilistic State Estimation in Distribution Grids via Model Order Reduction
Samuel Chevalier, Luca Schenato, Luca Daniel
This paper applies a custom model order reduction technique to the distribution grid state estimation problem. Specifically, the method targets the situation where, due to pseudo-m…
Accelerated Probabilistic Power Flow in Electrical Distribution Networks via Model Order Reduction and Neumann Series Expansion
Samuel Chevalier, Luca Schenato, Luca Daniel
This paper develops a computationally efficient algorithm which speeds up the probabilistic power flow (PPF) problem by exploiting the inherently low-rank nature of the voltage pro…
Dynamic Linepack Depletion Models for Natural Gas Pipeline Networks
Samuel Chevalier, Dan Wu
Given the critical role played by natural gas in providing electricity, heat, and other essential services, better models are needed to understand the dynamics of natural gas netwo…
A Passivity Interpretation of Energy-Based Forced Oscillation Source Location Methods
Samuel Chevalier, Petr Vorobev, Konstantin Turitsyn
This paper develops a systematic framework for analyzing how low frequency forced oscillations propagate in electric power systems. Using this framework, the paper shows how to mat…
Using Passivity Theory to Interpret the Dissipating Energy Flow Method
Samuel Chevalier, Petr Vorobev, Konstantin Turitsyn +2
Despite wide-scale deployment of phasor measurement unit technology, locating the sources of low frequency forced oscillations in power systems is still an open research topic. The…
A Bayesian Approach to Forced Oscillation Source Location Given Uncertain Generator Parameters
Samuel Chevalier, Petr Vorobev, Konstantin Turitsyn
Since forced oscillations are exogenous to dynamic power system models, the models by themselves cannot predict when or where a forced oscillation will occur. Locating the sources…
Mitigating the Risk of Voltage Collapse using Statistical Measures from PMU Data
Samuel Chevalier, Paul D. H. Hines
With the continued deployment of synchronized Phasor Measurement Units (PMUs), high sample rate data are rapidly increasing the real time observability of power systems. Prior rese…
Using Effective Generator Impedance for Forced Oscillation Source Location
Samuel Chevalier, Petr Vorobev, Konstantin Turitsyn
Locating the sources of forced low-frequency oscillations in power systems is an important problem. A number of proposed methods demonstrate their practical usefulness, but many of…