{
  "abstract": "This paper addresses a class of robust stochastic optimal control problems. Its main contribution lies in the introduction of a general optimization model with variance penalization and an associated solution algorithm that improves out-of-sample robustness while preserving numerical complexity. The proposed variance-penalized model is inspired by a well-established machine learning practice that aims to limit overfitting and extends this idea to stochastic optimal control. Using the Douglas–Rachford splitting method, the authors develop a Variance-Penalized Progressive Hedging Algorithm (VPPHA) that retains the computational complexity of the standard PHA while achieving superior out-of-sample performance. In addition, the authors propose a three-step control framework comprising (i) a random scenario generation method, (ii) a scenario reduction algorithm, and (iii) a scenario-based optimal control computation using the VPPHA. Finally, the proposed method is validated through simulations of a stationary battery Energy Management System using ground-truth electricity consumption and production measurements from a predominantly commercial building in Solaize, France. The results demonstrate that the proposed approach outperforms a classical Model Predictive Control strategy, which itself performs better than the standard PHA.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Paul Malisani"
    },
    {
      "name": "Adrien Spagnol"
    },
    {
      "name": "Vivien Smis-Michel"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bexh8nyd8nq58.bib",
  "cited_by": 0,
  "doi": "10.1007/s10589-026-00802-3",
  "fulltext_url": null,
  "id": "bexh8nyd8nq58",
  "issns": [
    "0926-6003",
    "1573-2894"
  ],
  "landing_url": "https://ifp.hal.science/hal-05732626v1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.math"
    ],
    [
      "openalex",
      "2207|Advanced Control Systems Optimization"
    ]
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  "openalex": "W7165380912",
  "pdf_url": "https://ifp.hal.science/hal-05732626/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-20",
  "publisher": "Springer Verlag",
  "ris": "https://research.draconitic.com/paper/bexh8nyd8nq58.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "hal-05732626",
      "source": "hal",
      "url": "https://ifp.hal.science/hal-05732626v1"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "hal:0.math",
      "subfield": "general"
    }
  ],
  "title": "Robust stochastic optimal control via variance penalization: application to energy management systems",
  "title_text": "Robust stochastic optimal control via variance penalization: application to energy management systems",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/bexh8nyd8nq58",
  "venue": "Computational Optimization and Applications",
  "versions": []
}