no code implementations • 20 Aug 2024 • Samuel Chevalier, Duncan Starkenburg, Krishnamurthy Dvijotham

In the field of formal verification, Neural Networks (NNs) are typically reformulated into equivalent mathematical programs which are optimized over.

no code implementations • 2 Jul 2024 • Omid Mokhtari, Samuel Chevalier, Mads Almassalkhi

Electrical networks contain thousands of interconnected nodes and edges, which leads to computational challenges in some power system studies.

1 code implementation • 19 Jun 2024 • Seide Saba Rafiei, Samuel Chevalier

After solving the dual problem on both CPUs and GPUs to find tight lower bounds, we benchmark against Gurobi and MOSEK, comparing convergence speed and tightness on the IEEE 2000 and 10000 bus systems.

no code implementations • 27 Nov 2023 • Dakota Hamilton, Samuel Chevalier, Amritanshu Pandey, Mads Almassalkhi

There is growing interest in understanding how interactions between system-wide objectives and local community decision-making will impact the clean energy transition.

no code implementations • 20 Nov 2023 • Samuel Chevalier, Robert Parker

Linear system solving is a key tool for computational power system studies, e. g., optimal power flow, transmission switching, or unit commitment.

1 code implementation • 10 Oct 2023 • Samuel Chevalier

Power system optimization problems which include the nonlinear AC power flow equations require powerful and robust numerical solution algorithms.

no code implementations • 18 Jun 2023 • Samuel Chevalier, Ilgiz Murzakhanov, Spyros Chatzivasileiadis

Our contributions achieve a speedup that can exceed 100x and allow higher degrees of verification flexibility.

1 code implementation • 21 Apr 2023 • Ignasi Ventura Nadal, Samuel Chevalier

However, generating training datasets that accurately represent the many possible combinations of these active constraints is a particularly challenging task, especially within the realm of nonlinear AC Optimal Power Flow (OPF), since most active constraints cannot be enforced explicitly.

1 code implementation • 14 Nov 2022 • Samuel Chevalier, Spyros Chatzivasileiadis

This paper develops a tractable neural network verification procedure which incorporates the ground truth of the non-linear AC power flow equations to determine worst-case neural network prediction error.

no code implementations • 18 Sep 2022 • Vladimir Dvorkin, Samuel Chevalier, Spyros Chatzivasileiadis

Gas network planning optimization under emission constraints prioritizes gas supply with the least CO$_2$ intensity.

no code implementations • 13 Sep 2022 • Robert I. Hamilton, Jochen Stiasny, Tabia Ahmad, Samuel Chevalier, Rahul Nellikkath, Ilgiz Murzakhanov, Spyros Chatzivasileiadis, Panagiotis N. Papadopoulos

To do so, we demonstrate that the Power Transfer Distribution Factors (PTDF) -- a physics-based linear sensitivity index -- can be derived from the SHAP values.

1 code implementation • 24 Jun 2022 • 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 problem.

no code implementations • 12 Apr 2022 • Samuel Chevalier, Mads R. Almassalkhi

To overcome this challenge, this paper presents a novel network reduction methodology that leverages an efficient mixed-integer linear programming (MILP) formulation of a Kron-based reduction that is optimal in the sense that it balances the degree of the reduction with resulting modeling errors in the reduced network.

1 code implementation • 14 Mar 2022 • Jochen Stiasny, Samuel Chevalier, Rahul Nellikkath, Brynjar Sævarsson, Spyros Chatzivasileiadis

Deep decarbonization of the energy sector will require massive penetration of stochastic renewable energy resources and an enormous amount of grid asset coordination; this represents a challenging paradigm for the power system operators who are tasked with maintaining grid stability and security in the face of such changes.

no code implementations • 21 Oct 2021 • Alyssa Kody, Samuel Chevalier, Spyros Chatzivasileiadis, Daniel Molzahn

Nonlinear power flow constraints render a variety of power system optimization problems computationally intractable.

no code implementations • 8 Oct 2021 • Nils Müller, Samuel Chevalier, Carsten Heinrich, Kai Heussen, Charalampos Ziras

The ongoing electrification introduces new challenges to distribution system operators (DSOs).

no code implementations • 30 Jun 2021 • 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 learn the solutions of power system dynamics.

no code implementations • 4 Jun 2021 • Samuel Chevalier, Jochen Stiasny, Spyros Chatzivasileiadis

In the second approach, we model the Newton solver at the heart of an implicit Runge-Kutta integrator as a contracting map iteratively seeking a fixed point on a time domain trajectory.

no code implementations • 25 Nov 2020 • Tommaso Bradde, Samuel Chevalier, Marco De Stefano, Stefano Grivet-Talocia, Luca Daniel

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 multi-output (MIMO) dynamical systems via rational transfer function matrices.

no code implementations • 13 Nov 2020 • Samuel Chevalier, Federico Martin Ibanez, Kathleen Cavanagh, Konstantin Turitsyn, Luca Daniel, Petr Vorobev

DC microgrids are prone to small-signal instabilities due to the presence of tightly regulated loads.

no code implementations • 28 Oct 2020 • Samuel Chevalier, Luca Schenato, Luca Daniel

This subspace is used to construct and update a reduced order model (ROM) of the full nonlinear system, resulting in a highly efficient simulation for future voltage profiles.

1 code implementation • 12 Jun 2019 • Samuel Chevalier, Petr Vorobev, Konstantin Turitsyn

The paper goes on to develop a simulation-free algorithm for predicting the performance of the DEF method in a generalized power system, and it analyzes the passivity of three non-classical load and generation components.

Systems and Control Systems and Control

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