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Optimal Control Strategies for a Network of Electric Vehicle Charging Energy Hubs with Smart Scheduling via Distributed Optimization — arXiv:2609.16899

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.16899
License (as archived): CC0-1.0
Archived: 2026-10-15T22:39:24.282Z
SHA-256 of the archived text: f45591bb21e1bc5bf8f314242766393cdd10a15c776b11fb3b7df1cd34ec4b39
Stable URL of this page: https://metraipolis.com/documento/dc_aa2bcdf29f63

How to cite

Optimal Control Strategies for a Network of Electric Vehicle Charging Energy Hubs with Smart Scheduling via Distributed Optimization — arXiv:2609.16899 — arXiv (en), https://arxiv.org/abs/2609.16899 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_aa2bcdf29f63 (Metraipolis archive dc_aa2bcdf29f63, sha256 f45591bb21e1bc5bf8f314242766393cdd10a15c776b11fb3b7df1cd34ec4b39)

BibTeX

@misc{dcaa2bcdf29f63,
  title = {Optimal Control Strategies for a Network of Electric Vehicle Charging Energy Hubs with Smart Scheduling via Distributed Optimization — arXiv:2609.16899},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.16899},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_aa2bcdf29f63; sha256 f45591bb21e1bc5bf8f314242766393cdd10a15c776b11fb3b7df1cd34ec4b39},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.16899. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

This paper studies the cost-optimal operation of a network of charging energy hubs for electric vehicles, which provide onsite renewable energy sources and stationary battery storage and are connected with each other via DC-lines as well as with the distribution grid. Specifically, we first formulate a dynamic optimal control problem for the entire network as a convex quadratic program, whereby the charging power profiles of the individual vehicles and the energy flows between hubs and the grid are subject to optimization. Second, we propose a problem decomposition that allows for a distributed solution via ADMM algorithms that preserves global optimality guarantees and privacy of the individual stations. We showcase our framework on a case-study for the Netherlands considering a two-day ahead deterministic formulation with perfect foresight. Our results show that compared to the case where charging powers are fixed a priori, optimizing their profiles (V1G) can significantly reduce the operational costs and emissions by more than 25%. Moreover, we verify our distributed algorithm against a centralized solution, paving the way to the optimal operation of large networks and online implementations.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-09-16 in eess.SY).
Categorie arXiv: Systems and Control (eess.SY).
Pagina del lavoro: https://arxiv.org/abs/2609.16899 — 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 2026-10-15T22:15:42.172Z dalla pagina https://arxiv.org/catchup/eess.SY/2026-09-16?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).