Online Feedback Optimization over Networks: A Distributed Model-Free Approach
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Date
2024
Publication Type
Conference Paper
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yes
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Abstract
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.
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Publication status
published
Editor
Book title
2024 IEEE 63rd Conference on Decision and Control (CDC)
Journal / series
Volume
Pages / Article No.
2403 - 2408
Publisher
IEEE
Event
63rd Conference on Decision and Control (CDC 2024)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
09478 - Dörfler, Florian / Dörfler, Florian
02650 - Institut für Automatik / Automatic Control Laboratory
Notes
Funding
180545 - NCCR Automation (phase I) (SNF)