A decentralized framework for the optimal coordination of distributed energy resources MF Anjos, A Lodi, M Tanneau IEEE Transactions on Power Systems 34 (1), 349-359, 2018 | 55 | 2018 |
Learning optimization proxies for large-scale security-constrained economic dispatch W Chen, S Park, M Tanneau, P Van Hentenryck Electric Power Systems Research 213, 108566, 2022 | 49 | 2022 |
Learning regionally decentralized ac optimal power flows with admm TWK Mak, M Chatzos, M Tanneau, P Van Hentenryck IEEE Transactions on Smart Grid 14 (6), 4863-4876, 2023 | 30 | 2023 |
End-to-End Feasible Optimization Proxies for Large-Scale Economic Dispatch W Chen, M Tanneau, P Van Hentenryck IEEE Transactions on Power Systems, 1-12, 2023 | 29 | 2023 |
Confidence-aware graph neural networks for learning reliability assessment commitments S Park, W Chen, D Han, M Tanneau, P Van Hentenryck IEEE Transactions on Power Systems, 2023 | 24 | 2023 |
Design and implementation of a modular interior-point solver for linear optimization M Tanneau, MF Anjos, A Lodi Mathematical Programming Computation 13 (3), 509-551, 2021 | 18* | 2021 |
Disjunctive cuts in mixed-integer conic optimization A Lodi, M Tanneau, JP Vielma Mathematical Programming 199 (1), 671-719, 2023 | 17 | 2023 |
Data-driven time series reconstruction for modern power systems research M Chatzos, M Tanneau, P Van Hentenryck Electric Power Systems Research 212, 108589, 2022 | 11 | 2022 |
Learning chordal extensions D Liu, A Lodi, M Tanneau Journal of Global Optimization 81 (1), 3-22, 2021 | 7 | 2021 |
Dual conic proxies for AC optimal power flow G Qiu, M Tanneau, P Van Hentenryck Electric Power Systems Research 236, 110661, 2024 | 6 | 2024 |
Strong mixed-integer formulations for transmission expansion planning with FACTS devices K Wu, M Tanneau, P Van Hentenryck Electric Power Systems Research 235, 110695, 2024 | 6 | 2024 |
Risk-aware control and optimization for high-renewable power grids N Barry, M Chatzos, W Chen, D Han, C Huang, R Joseph, M Klamkin, ... arXiv preprint arXiv:2204.00950, 2022 | 6 | 2022 |
Just-in-time learning for operational risk assessment in power grids O Stover, P Karve, S Mahadevan, W Chen, H Zhao, M Tanneau, ... arXiv preprint arXiv:2209.12762, 2022 | 5 | 2022 |
Bucketized Active Sampling for learning ACOPF M Klamkin, M Tanneau, TWK Mak, P Van Hentenryck Electric Power Systems Research 235, 110697, 2024 | 4* | 2024 |
Bound tightening using rolling-horizon decomposition for neural network verification H Zhao, H Hijazi, H Jones, J Moore, M Tanneau, P Van Hentenryck International Conference on the Integration of Constraint Programming …, 2024 | 4 | 2024 |
A linear outer approximation of line losses for DC-based optimal power flow problems H Zhao, M Tanneau, P Van Hentenryck Electric Power Systems Research 212, 108272, 2022 | 4 | 2022 |
Learning optimal power flow value functions with input-convex neural networks A Rosemberg, M Tanneau, B Fanzeres, J Garcia, P Van Hentenryck Electric Power Systems Research 235, 110643, 2024 | 3 | 2024 |
Weather-informed probabilistic forecasting and scenario generation in power systems H Zhang, R Zandehshahvar, M Tanneau, P Van Hentenryck Applied Energy 384, 125369, 2025 | 2 | 2025 |
Asset bundling for hierarchical forecasting of wind power generation H Zhang, M Tanneau, C Huang, VR Joseph, S Wang, P Van Hentenryck Electric Power Systems Research 235, 110771, 2024 | 2 | 2024 |
Real-time risk analysis with optimization proxies W Chen, M Tanneau, P Van Hentenryck Electric Power Systems Research 235, 110822, 2024 | 2 | 2024 |