Online Feedback Optimization over Networks: A Distributed Model-free Approach

Fuente: arXiv
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Main Authors: Wang, Wenbin, He, Zhiyu, Belgioioso, Giuseppe, Bolognani, Saverio, Dörfler, Florian
Format: Preprint
Published: 2024
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author Wang, Wenbin
He, Zhiyu
Belgioioso, Giuseppe
Bolognani, Saverio
Dörfler, Florian
author_facet Wang, Wenbin
He, Zhiyu
Belgioioso, Giuseppe
Bolognani, Saverio
Dörfler, Florian
contents Online feedback optimization (OFO) enables optimal steady-state operations of a physical system by employing an iterative optimization algorithm as a dynamic feedback controller. When the plant consists of several interconnected sub-systems, centralized implementations become impractical due to the heavy computational burden and the need to pre-compute system-wide sensitivities, which may not be easily accessible in practice. Motivated by these challenges, we develop a fully distributed model-free OFO controller, featuring consensus-based tracking of the global objective value and local iterative (projected) updates that use stochastic gradient estimates. We characterize how the closed-loop performance depends on the size of the network, the number of iterations, and the level of accuracy of consensus. Numerical simulations on a voltage control problem in a direct current power grid corroborate the theoretical findings.
format Preprint
id arxiv_https___arxiv_org_abs_2403_19834
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Online Feedback Optimization over Networks: A Distributed Model-free Approach
Wang, Wenbin
He, Zhiyu
Belgioioso, Giuseppe
Bolognani, Saverio
Dörfler, Florian
Optimization and Control
Online feedback optimization (OFO) enables optimal steady-state operations of a physical system by employing an iterative optimization algorithm as a dynamic feedback controller. When the plant consists of several interconnected sub-systems, centralized implementations become impractical due to the heavy computational burden and the need to pre-compute system-wide sensitivities, which may not be easily accessible in practice. Motivated by these challenges, we develop a fully distributed model-free OFO controller, featuring consensus-based tracking of the global objective value and local iterative (projected) updates that use stochastic gradient estimates. We characterize how the closed-loop performance depends on the size of the network, the number of iterations, and the level of accuracy of consensus. Numerical simulations on a voltage control problem in a direct current power grid corroborate the theoretical findings.
title Online Feedback Optimization over Networks: A Distributed Model-free Approach
topic Optimization and Control
url https://arxiv.org/abs/2403.19834