no code implementations • 6 Feb 2024 • Yuqi Zhou, Ahmed Zamzam, Andrey Bernstein
Increasing amount of wildfires in recent years consistently challenges the safe and reliable operations of power systems.
no code implementations • 29 Dec 2023 • Killian Wood, Ahmed Zamzam, Emiliano Dall'Anese
This paper tackles the problem of solving stochastic optimization problems with a decision-dependent distribution in the setting of stochastic strongly-monotone games and when the distributional dependence is unknown.
1 code implementation • 6 Jun 2023 • Sofia Taylor, Gabriela Setyawan, Bai Cui, Ahmed Zamzam, Line A. Roald
As climate change increases the risk of large-scale wildfires, wildfire ignitions from electric power lines are a growing concern.
no code implementations • 26 Jul 2022 • Bai Cui, Ahmed Zamzam, Andrey Bernstein
This paper proposes efficient optimization formulations and solution approaches for the characterization of hourly as well as multi-time-step generation cost curves for a distribution system with high penetration of DERs.
no code implementations • 14 Jul 2021 • Rakshit Naidu, Harshita Diddee, Ajinkya Mulay, Aleti Vardhan, Krithika Ramesh, Ahmed Zamzam
In recent years, machine learning techniques utilizing large-scale datasets have achieved remarkable performance.
no code implementations • 8 Nov 2019 • David Biagioni, Peter Graf, Xiangyu Zhang, Ahmed Zamzam, Kyri Baker, Jennifer King
We propose a novel data-driven method to accelerate the convergence of Alternating Direction Method of Multipliers (ADMM) for solving distributed DC optimal power flow (DC-OPF) where lines are shared between independent network partitions.
no code implementations • 27 Sep 2019 • Ahmed Zamzam, Kyri Baker
In this paper, we develop an online method that leverages machine learning to obtain feasible solutions to the AC optimal power flow (OPF) problem with negligible optimality gaps on extremely fast timescales (e. g., milliseconds), bypassing solving an AC OPF altogether.