45 citations · 67 across the 29 of their papers we have counts for
30 papers
The Proxy Benders Decomposition
Changkun Guan, El Mehdi Er Raqabi, Mathieu Tanneau +1
Benders decomposition is a fundamental framework for solving large-scale mixed-integer optimization problems with complicating variables that, when fixed, yield significantly easie…
A Hybrid Decomposition Approach for Stochastic Unit Commitment with Combined-Cycle Generators
Rosemary Barrass, Harsha Nagarajan, Mathieu Tanneau +2
The U.S. power grid is undergoing a paradigm shift as energy demand grows in scale and volatility. In response to this growing need, the U.S. has increased adoption of combined-cyc…
CoCopula: Conformal Calibration and Copula Aggregation for Reliable Renewable Energy Forecasting
Alireza Moradi, Mathieu Tanneau, Reza Zandehshahvar +1
The rapid growth of renewable energy penetration has intensified the need for reliable probabilistic forecasts to support grid operations at aggregated (fleet or system) levels. In…
Volt/VAR Optimization in Transmission Networks with Discrete-Control Devices
Shuaicheng Tong, Michael A. Boateng, Mathieu Tanneau +1
Voltage (Volt) and reactive-power (VAR) control in transmission networks is critical for reliability and increasingly needs fast, implementable decisions. This paper presents a tra…
Scalable Iterative Algorithm for Solving Optimal Transmission Switching with De-energization
Benoît Jeanson, Mathieu Tanneau, Simon Tindemans
Transmission System Operators routinely use transmission switching as a tool to manage congestion and ensure system security. Motivated by sub-transmission operations at RTE, this…
Self-Certifying Primal-Dual Optimization Proxies for Large-Scale Batch Economic Dispatch
Michael Klamkin, Mathieu Tanneau, Pascal Van Hentenryck
Recent research has shown that optimization proxies can be trained to high fidelity, achieving average optimality gaps under 1% for large-scale problems. However, worst-case analys…
Enhanced Renewable Energy Forecasting using Context-Aware Conformal Prediction
Alireza Moradi, Mathieu Tanneau, Reza Zandehshahvar +1
Artificial intelligence (AI) is increasingly used to support renewable energy forecasting and grid operations. As renewable penetration grows, reliable probabilistic forecasting is…
PGLearn -- An Open-Source Learning Toolkit for Optimal Power Flow
Michael Klamkin, Mathieu Tanneau, Pascal Van Hentenryck
Machine Learning (ML) techniques for Optimal Power Flow (OPF) problems have recently garnered significant attention, reflecting a broader trend of leveraging ML to approximate and/…
Enhanced Renewable Energy Forecasting and Operations through Probabilistic Forecast Aggregation
Alireza Moradi, Mathieu Tanneau, Reza Zandehshahvar +1
Accurate and reliable forecasting of renewable energy generation is crucial for the efficient integration of renewable sources into the power grid. In particular, probabilistic for…
Dual Conic Proxy for Semidefinite Relaxation of AC Optimal Power Flow
Guancheng Qiu, Mathieu Tanneau, Pascal Van Hentenryck
The nonlinear, non-convex AC Optimal Power Flow (AC-OPF) problem is fundamental for power systems operations. The intrinsic complexity of AC-OPF has fueled a growing interest in th…
Weather-Informed Probabilistic Forecasting and Scenario Generation in Power Systems
Hanyu Zhang, Reza Zandehshahvar, Mathieu Tanneau +1
The integration of renewable energy sources (RES) into power grids presents significant challenges due to their intrinsic stochasticity and uncertainty, necessitating the developme…
Compact Optimality Verification for Optimization Proxies
Wenbo Chen, Haoruo Zhao, Mathieu Tanneau +1
Recent years have witnessed increasing interest in optimization proxies, i.e., machine learning models that approximate the input-output mapping of parametric optimization problems…
Scalable Exact Verification of Optimization Proxies for Large-Scale Optimal Power Flow
Rahul Nellikkath, Mathieu Tanneau, Pascal Van Hentenryck +1
Optimal Power Flow (OPF) is a valuable tool for power system operators, but it is a difficult problem to solve for large systems. Machine Learning (ML) algorithms, especially Neura…
Dual Lagrangian Learning for Conic Optimization
Mathieu Tanneau, Pascal Van Hentenryck
This paper presents Dual Lagrangian Learning (DLL), a principled learning methodology for dual conic optimization proxies. DLL leverages conic duality and the representation power…
Dual Interior Point Optimization Learning
Michael Klamkin, Mathieu Tanneau, Pascal Van Hentenryck
In many practical applications of constrained optimization, scale and solving time limits make traditional optimization solvers prohibitively slow. Thus, the research question of h…
Bound Tightening using Rolling-Horizon Decomposition for Neural Network Verification
Haoruo Zhao, Hassan Hijazi, Haydn Jones +3
Neural network verification aims at providing formal guarantees on the output of trained neural networks, to ensure their robustness against adversarial examples and enable their d…
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…
Confidence-Aware Graph Neural Networks for Learning Reliability Assessment Commitments
Seonho Park, Wenbo Chen, Dahye Han +2
Reliability Assessment Commitment (RAC) Optimization is increasingly important in grid operations due to larger shares of renewable generations in the generation mix and increased…