45 citations · 67 across the 29 of their papers we have counts for
7 papers · 1 filter
Learning Optimal Power Flow Value Functions with Input-Convex Neural Networks
Andrew Rosemberg, Mathieu Tanneau, Bruno Fanzeres +2
The Optimal Power Flow (OPF) problem is integral to the functioning of power systems, aiming to optimize generation dispatch while adhering to technical and operational constraints…
Real-Time Risk Analysis with Optimization Proxies
Wenbo Chen, Mathieu Tanneau, Pascal Van Hentenryck
The increasing penetration of renewable generation and distributed energy resources requires new operating practices for power systems, wherein risk is explicitly quantified and ma…
Dual Conic Proxies for AC Optimal Power Flow
Guancheng Qiu, Mathieu Tanneau, Pascal Van Hentenryck
In recent years, there has been significant interest in the development of machine learning-based optimization proxies for AC Optimal Power Flow (AC-OPF). Although significant prog…
Strong Mixed-Integer Formulations for Transmission Expansion Planning with FACTS Devices
Kevin Wu, Mathieu Tanneau, Pascal Van Hentenryck
Transmission Network Expansion Planning (TNEP) problems find the most economical way of expanding a given grid given long-term growth in generation capacity and demand patterns. Th…
Asset Bundling for Wind Power Forecasting
Hanyu Zhang, Mathieu Tanneau, Chaofan Huang +3
The growing penetration of intermittent, renewable generation in US power grids, especially wind and solar generation, results in increased operational uncertainty. In that context…
On the Viability of Stochastic Economic Dispatch for Real-Time Energy Market Clearing
Haoruo Zhao, Mathieu Tanneau, Pascal Van Hentenryck
Over the past decade, the rapid adoption of intermittent renewable energy sources (RES), especially wind and solar generation, has posed challenges in managing real-time uncertaint…
End-to-End Feasible Optimization Proxies for Large-Scale Economic Dispatch
Wenbo Chen, Mathieu Tanneau, Pascal Van Hentenryck
The paper proposes a novel End-to-End Learning and Repair (E2ELR) architecture for training optimization proxies for economic dispatch problems. E2ELR combines deep neural networks…