{
  "abstract": "Seasonal reservoir operation requires optimization methods that remain reliable under strongly periodic and uncertain inflow dynamics. We present InFlow, a verification-oriented framework combining periodic stochastic dual dynamic programming (SDDP), entropic robustness, control-space Bellman annealing, and an independently discretized Hamilton-Jacobi-Bellman (HJB) benchmark. The entropic transition operator is interpreted as a KL-penalized worst-case expectation and linked to fixed-radius KL ambiguity sets through Lagrange duality. We establish a uniform soft-Bellman approximation bound and prove the validity of Gibbs-tilted SDDP cuts. The benchmark relies on a normalized seasonal Cox-Ingersoll-Ross inflow process whose positivity condition fails during the driest weeks, requiring explicit treatment of the degenerate boundary. Independent verification shows strong agreement between HJB storage gradients and HiGHS storage-balance duals, with water-value correlations of 0.995 and 0.996 for the standard and exact-cut-only implementations. Refining the HJB grid from 61^2 to 81^2 states changes the annual mean water value by only 0.6% and the peak value by less than 0.1%. Out-of-sample simulations based on 3,000 independent trajectories show that a robustness level of gamma=5 increases nominal mean cost by 4.6% while reducing CVaR90 by 11.2%. Under a dry-season stress scenario with inflows reduced by 40%, the mean-cost difference becomes statistically insignificant whereas CVaR90 improves by 11.5%. A robustness sweep confirms monotonicity of the risk-adjusted value. Finally, a two-reservoir cascade reproduces grid-oracle marginal water values with correlation 0.999 and recovers the predicted upstream-to-downstream value ratio of two. InFlow is intended as a transparent methodological benchmark rather than a calibrated operational reservoir model.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.11339",
  "author_total": 4,
  "authors": [
    {
      "name": "Steeven B. Affognona"
    },
    {
      "name": "Babacar M. Ndiaye"
    },
    {
      "name": "Pierre Mendy"
    },
    {
      "name": "Cheikh M. F. Kebe"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/aejv0rprr7ked.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.11339",
  "fulltext_url": null,
  "id": "aejv0rprr7ked",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.11339",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "math.OC"
    ],
    [
      "openalex",
      "2212|Water resources management and optimization"
    ]
  ],
  "openalex": "W7212310400",
  "pdf_url": "https://arxiv.org/pdf/2609.11339",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/aejv0rprr7ked.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:30Z",
      "native_id": "2609.11339",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.11339"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "InFlow: entropic stochastic dual dynamic programming with HJB cross-certification for seasonal energy storage",
  "title_text": "InFlow: entropic stochastic dual dynamic programming with HJB cross-certification for seasonal energy storage",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:30Z",
  "url": "https://research.draconitic.com/paper/aejv0rprr7ked",
  "venue": "arXiv",
  "versions": []
}