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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.16048 |
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| _version_ | 1866916941396967424 |
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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 |