{
  "abstract": "State-adaptive risk-aware rolling model predictive control (SA-RR-MPC) links experimentally calibrated battery action limits to grid-side tracking and ramp-risk costs in wind–solar–vanadium redox flow battery (VRFB) regulation. Protocol-matched PNNL cycling observations supply power envelopes conditioned on a continuous charge reference and measured capacity, while Mendeley pressure–flow measurements supply the normalized pump-loss shape. Pump-corrected net injection connects admitted stack actions to scenario conditional value at risk (CVaR). A two-pass linear program selects dispatch requests, and current-state projection provides minimum-correction execution with an explicit bound on the resulting ramp-risk perturbation. Whole-cycle calibration checks give 89.95% Q90 coverage, and the local power labels change by at most 0.205% under the common charge-reference comparison. A causal 184-day NSRDB/WIND Toolkit replay evaluates a nominal 20 MW/80 MWh VRFB under common empirical-envelope and energy protection for all storage methods. SA-RR-MPC reduces realized ramp-exceedance CVaR90 by 40.94% and request-to-execution correction energy by 65.64% relative to Fixed-MPC, with a 0.54% increase in tracking RMSE. Relative to a matched risk-aware controller differing only in predictive power bounds, correction energy falls by 58.48%. Two nonoverlapping 28-day weather windows and three paired scenario seeds retain 27.06–50.01% CVaR90 reductions relative to Fixed-MPC. Paired module tests identify complementary roles in request adaptation, net-power prediction, and tail-risk valuation. The 95th-percentile serial software-loop time is 18.54 ms over 624 cycles after 48 warm-up steps.",
  "abstract_shown": true,
  "affiliations": [
    "School of Electrical Engineering, Guangzhou City University of Technology, Guangzhou 510800, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yuanfa Cen"
    },
    {
      "affs": [
        0
      ],
      "name": "Ming Gao"
    },
    {
      "affs": [
        0
      ],
      "name": "Baichi Ou"
    },
    {
      "affs": [
        0
      ],
      "name": "Rushi Zhao"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiaoqing Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/c2y13h348ega2.bib",
  "cited_by": 0,
  "doi": "10.3390/batteries12110448",
  "fulltext_url": null,
  "id": "c2y13h348ega2",
  "issns": [
    "2313-0105"
  ],
  "landing_url": "https://www.mdpi.com/2313-0105/12/11/448",
  "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": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/c2y13h348ega2.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T00:24:21Z",
      "native_id": "10.3390/batteries12110448",
      "source": "crossref",
      "url": "https://www.mdpi.com/2313-0105/12/11/448"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "issn:2313-0105",
      "subfield": "electrochemistry-energy"
    }
  ],
  "title": "State-Adaptive and Tail-Risk-Aware Rolling Control of Wind–Solar–VRFB Systems with Experimentally Calibrated Power Envelopes",
  "title_text": "State-Adaptive and Tail-Risk-Aware Rolling Control of Wind–Solar–VRFB Systems with Experimentally Calibrated Power Envelopes",
  "type": "peer-reviewed",
  "updated_at": "2026-11-05T00:20:54Z",
  "url": "https://research.draconitic.com/paper/c2y13h348ega2",
  "venue": "Batteries",
  "versions": []
}