1 code implementation • 17 Apr 2018 • Yi Guo, Kyri Baker, Emiliano Dall'Anese, Zechun Hu, Tyler H. Summers
We propose a data-based method to solve a multi-stage stochastic optimal power flow (OPF) problem based on limited information about forecast error distributions.
Optimization and Control Systems and Control
1 code implementation • 17 Apr 2018 • Yi Guo, Kyri Baker, Emiliano Dall'Anese, Zechun Hu, Tyler H. Summers
Here, we present extensive numerical experiments in both distribution and transmission networks to illustrate the effectiveness and flexibility of the proposed methodology for balancing efficiency, constraint violation risk, and out-of-sample performance.
Optimization and Control Systems and Control
1 code implementation • 13 Jun 2017 • Yi Guo, Kyri Baker, Emiliano Dall'Anese, Zechun Hu, Tyler Summers
We propose a data-driven method to solve a stochastic optimal power flow (OPF) problem based on limited information about forecast error distributions.
Optimization and Control Systems and Control
1 code implementation • 1 Nov 2021 • Trager Joswig-Jones, Kyri Baker, Ahmed S. Zamzam
Increasing levels of renewable generation motivate a growing interest in data-driven approaches for AC optimal power flow (AC OPF) to manage uncertainty; however, a lack of disciplined dataset creation and benchmarking prohibits useful comparison among approaches in the literature.
1 code implementation • 19 May 2021 • Bingqing Chen, Priya Donti, Kyri Baker, J. Zico Kolter, Mario Berges
Specifically, we incorporate a differentiable projection layer within a neural network-based policy to enforce that all learned actions are feasible.
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.
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 • 12 Oct 2021 • Aisling Pigott, Constance Crozier, Kyri Baker, Zoltan Nagy
Increasing amounts of distributed generation in distribution networks can provide both challenges and opportunities for voltage regulation across the network.
no code implementations • 22 Nov 2021 • My H. Dinh, Ferdinando Fioretto, Mostafa Mohammadian, Kyri Baker
Optimal Power Flow (OPF) is a fundamental problem in power systems.
no code implementations • 21 Jun 2022 • Mostafa Mohammadian, Kyri Baker, Ferdinando Fioretto
The application of deep learning methods to speed up the resolution of challenging power flow problems has recently shown very encouraging results.
no code implementations • 11 Jul 2022 • Meiyi Li, Yuhan Du, Javad Mohammadi, Constance Crozier, Kyri Baker, Soummya Kar
Linear approximations of the AC power flow equations are of great significance for the computational efficiency of large-scale optimal power flow (OPF) problems.