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.
@article{arxiv.2504.16048,
title = {PRIME: Fast Primal-Dual Feedback Optimization for Markets with Application to Optimal Power Flow},
author = {Nicholas Julian Behr and Mattia Bianchi and Keith Moffat and Saverio Bolognani and Florian Dörfler},
journal= {arXiv preprint arXiv:2504.16048},
year = {2025}
}
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Source code available at https://github.com/NicholasBehr/prime