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Main Authors: Behr, Nicholas Julian, Bianchi, Mattia, Moffat, Keith, Bolognani, Saverio, Dörfler, Florian
Format: Preprint
Published: 2025
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Online Access:https://arxiv.org/abs/2504.16048
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author Behr, Nicholas Julian
Bianchi, Mattia
Moffat, Keith
Bolognani, Saverio
Dörfler, Florian
author_facet Behr, Nicholas Julian
Bianchi, Mattia
Moffat, Keith
Bolognani, Saverio
Dörfler, Florian
contents Online Feedback Optimization (OFO) controllers iteratively drive a plant to an optimal operating point that satisfies input and output constraints, relying solely on the input-output sensitivity as model information. This paper introduces PRIME (PRoximal Iterative MarkEts), a novel OFO approach based on proximal-point iterations. Unlike existing OFO solutions, PRIME admits a market-based implementation, where self-interested actors are incentivized to make choices that result in safe and efficient operation, without communicating private costs or constraints. Furthermore, PRIME can handle non-smooth objective functions, achieve fast convergence rates and rapid constraint satisfaction, and effectively reject measurement noise. We demonstrate PRIME on an AC optimal power flow problem, obtaining an efficient real-time nonlinear local marginal pricing scheme.
format Preprint
id arxiv_https___arxiv_org_abs_2504_16048
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle PRIME: Fast Primal-Dual Feedback Optimization for Markets with Application to Optimal Power Flow
Behr, Nicholas Julian
Bianchi, Mattia
Moffat, Keith
Bolognani, Saverio
Dörfler, Florian
Systems and Control
Online Feedback Optimization (OFO) controllers iteratively drive a plant to an optimal operating point that satisfies input and output constraints, relying solely on the input-output sensitivity as model information. This paper introduces PRIME (PRoximal Iterative MarkEts), a novel OFO approach based on proximal-point iterations. Unlike existing OFO solutions, PRIME admits a market-based implementation, where self-interested actors are incentivized to make choices that result in safe and efficient operation, without communicating private costs or constraints. Furthermore, PRIME can handle non-smooth objective functions, achieve fast convergence rates and rapid constraint satisfaction, and effectively reject measurement noise. We demonstrate PRIME on an AC optimal power flow problem, obtaining an efficient real-time nonlinear local marginal pricing scheme.
title PRIME: Fast Primal-Dual Feedback Optimization for Markets with Application to Optimal Power Flow
topic Systems and Control
url https://arxiv.org/abs/2504.16048