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Safe Receding Horizon Mixed-Integer Differentiable Predictive Control for Degradation-Aware Battery Dispatch — arXiv:2609.28698

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.28698
License (as archived): CC0-1.0
Archived: 2026-10-08T08:27:33.598Z
SHA-256 of the archived text: 9ccf74928e95b9dae2fae23de426d5fbbbb2d7954e2c5133f1fbd5be1ea10c9c
Stable URL of this page: https://metraipolis.com/documento/dc_7ded7bb4200a

How to cite

Safe Receding Horizon Mixed-Integer Differentiable Predictive Control for Degradation-Aware Battery Dispatch — arXiv:2609.28698 — arXiv (en), https://arxiv.org/abs/2609.28698 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7ded7bb4200a (Metraipolis archive dc_7ded7bb4200a, sha256 9ccf74928e95b9dae2fae23de426d5fbbbb2d7954e2c5133f1fbd5be1ea10c9c)

BibTeX

@misc{dc7ded7bb4200a,
  title = {Safe Receding Horizon Mixed-Integer Differentiable Predictive Control for Degradation-Aware Battery Dispatch — arXiv:2609.28698},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.28698},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7ded7bb4200a; sha256 9ccf74928e95b9dae2fae23de426d5fbbbb2d7954e2c5133f1fbd5be1ea10c9c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.28698. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

We present a safe receding-horizon mixed-integer differentiable predictive control methodology for residential battery energy storage dispatch that combines neural-network speed with recursive feasibility guarantees. Unlike open-loop learning-to-optimize methods, it incorporates real-time state-of-charge feedback and sinusoidal time-of-day conditioning, enabling closed-loop re-planning at every timestep without re-solving a mixed-integer program. A differentiable rainflow cycle-counting layer enables self-supervised training of the mixed-integer policy on exact degradation physics. The controller is a hybrid closed-loop system pairing a neural mode-selection and continuous-action policy with a quadratic-programming safety filter that guarantees recursive feasibility independent of network weights or mode optimality. We establish mode-conditioned Lipschitz continuity and a conditional regret decomposition into training-quality, mode-mismatch, and forecast-error terms. On a 7-day net-metering evaluation, the method attains a 6.9% cost gap versus the closed-loop mixed-integer MPC benchmark with a 25x speedup (0.11 s vs 2.7 s per step), while average regret rises by under 4% across 0-30% forecast noise. The learned and benchmark modes agree at every step, so the bound reduces to its training-quality and forecast-error terms, both empirically validated.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-09-25 in eess.SY).
Categorie arXiv: Systems and Control (eess.SY).
Pagina del lavoro: https://arxiv.org/abs/2609.28698 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T07:40:07.037Z dalla pagina https://arxiv.org/catchup/eess.SY/2026-09-25?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).