1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
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…
Learning Optimization Proxies for Large-Scale Security-Constrained Economic Dispatch
Wenbo Chen, Seonho Park, Mathieu Tanneau +1
The Security-Constrained Economic Dispatch (SCED) is a fundamental optimization model for Transmission System Operators (TSO) to clear real-time energy markets while ensuring relia…
A Linear Outer Approximation of Line Losses for DC-based Optimal Power Flow Problems
Haoruo Zhao, Mathieu Tanneau, Pascal Van Hentenryck
This paper proposes a novel and simple linear model to capture line losses for use in linearized DC models, such as optimal power flow (DC-OPF) and security-constrained economic di…