Search Results for author: Kyri Baker

Found 9 papers, 5 papers with code

OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets

1 code implementation1 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.

GridLearn: Multiagent Reinforcement Learning for Grid-Aware Building Energy Management

no code implementations12 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.

Multi-agent Reinforcement Learning reinforcement-learning

Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy Optimization

1 code implementation19 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.

Learning-Accelerated ADMM for Distributed Optimal Power Flow

no code implementations8 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.

14 Distributed Optimization

Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow

no code implementations27 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.

Data-based Distributionally Robust Stochastic Optimal Power Flow, Part II: Case studies

1 code implementation17 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

Data-based Distributionally Robust Stochastic Optimal Power Flow, Part I: Methodologies

1 code implementation17 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

Stochastic Optimal Power Flow Based on Data-Driven Distributionally Robust Optimization

1 code implementation13 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

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