{
  "abstract": "Day-ahead battery arbitrage requires assessing the return and downside risk of complete charge–discharge cycles. We propose Scenario-Conditioned Risk Optimization (SCARO), which corrects supply and demand forecasts and maps jointly resampled whole-day residuals to price paths. An exact planner identifies adverse outcomes separately for each feasible schedule and combines mean and lower-tail profit to select an operating cycle or no operation. A 182-day rolling evaluation in the Western Inner Mongolia real-time electricity market compares SCARO with 21 baselines for a battery with zero initial and terminal states of charge and at most one daily cycle. The configurations use a single price model (SCARO-S) or ensembles (SCARO-E and SCARO-L), with the ensemble configurations differing in boundary correction. SCARO-S achieves the highest mean profit in 13 of 16 efficiency–cost conditions. At nominal efficiency and zero cycle cost, its mean and worst-month profits exceed the strongest baseline for each metric by 4.72% and 8.76%, respectively; SCARO-L achieves the highest oracle capture. With forecasts held fixed, lower-tail scheduling improves mean profit and loss frequency for SCARO-E and SCARO-L relative to their risk-neutral controls.",
  "abstract_shown": true,
  "affiliations": [
    "School of Computer and Information Management, Inner Mongolia University of Finance and Economics, Hohhot 010070, China",
    "School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jinchen Li",
      "orcid": "0000-0002-3408-2951"
    },
    {
      "affs": [
        1
      ],
      "name": "Xingxing Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4htg6kgnqtf4t.bib",
  "cited_by": 0,
  "doi": "10.3390/batteries12100404",
  "fulltext_url": null,
  "id": "4htg6kgnqtf4t",
  "issns": [
    "2313-0105"
  ],
  "landing_url": "https://www.mdpi.com/2313-0105/12/10/404",
  "language": null,
  "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": [
    [
      "openalex",
      "2208|Electric Power System Optimization"
    ]
  ],
  "openalex": "W7220846729",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/4htg6kgnqtf4t.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:47Z",
      "native_id": "10.3390/batteries12100404",
      "source": "crossref",
      "url": "https://www.mdpi.com/2313-0105/12/10/404"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "issn:2313-0105",
      "subfield": "electrochemistry-energy"
    }
  ],
  "title": "SCARO: Scenario-Conditioned Risk Optimization for Day-Ahead Battery Energy Storage Scheduling",
  "title_text": "SCARO: Scenario-Conditioned Risk Optimization for Day-Ahead Battery Energy Storage Scheduling",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:47Z",
  "url": "https://research.draconitic.com/paper/4htg6kgnqtf4t",
  "venue": "Batteries",
  "versions": []
}