{
  "abstract": "Net power injection at the point of common coupling (PCC) is an important indicator of grid-friendly hydropower operation. High-power station-service equipment may consume regulation margin and cause short-term power disturbances if it is not coordinated with unit dispatch and automatic generation control (AGC) tracking. This paper models hydropower-station crane groups as safety-bounded, forecastable, and dispatchable station-service flexibility resources, and proposes a forecast-embedded low-carbon demand response framework. The framework develops a PCC-coupled station-service model, uses phase-aware probabilistic forecasting to generate crane consumption templates, regeneration templates, and uncertainty intervals, and coordinates rigid, semi-flexible, and flexible crane tasks through rolling mixed-integer optimization to reduce station-service peaks, PCC tracking deviations, and carbon-opportunity costs. A station-oriented digital validation case shows that, while satisfying all task deadlines and safety constraints, the proposed method reduces the representative-day station-service peak by 16.1%, PCC RMSE by 55.1%, and carbon-opportunity cost by 29.7%, and increases regenerative-energy utilization from 54.8% to 86.8%, compared with earliest-start scheduling. An archive replay on an actual hydropower station with a 15.00 MW installed crane fleet then shows that start-time shifting remains available under that station’s work-order windows. On a high-duty maintenance day the representative-day peak falls by 15.4%, and over 20 high- duty days the mean peak falls by 17.3%, while regenerative utilization rises from 65.6% to 90.0%. The two evaluations indicate that crane groups at stations that issue work-order-based crane tasks with usable flexibility windows can be co-scheduled as station-service flexibility resources with phase structure, safety boundaries, and temporal regulation value. The reported magnitudes are those of the two cases and are not claimed for other layouts.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Wanwan Wei"
    },
    {
      "name": "Shangfeng Guo"
    },
    {
      "name": "Xiaolong Cheng"
    },
    {
      "name": "Wanli Dong"
    },
    {
      "name": "Bo Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5w7s10fmkbjpy.bib",
  "cited_by": 0,
  "doi": "10.4108/ew.15670",
  "fulltext_url": "https://publications.eai.eu/index.php/ew/article/view/15670",
  "id": "5w7s10fmkbjpy",
  "issns": [
    "2032-944X"
  ],
  "landing_url": "https://doi.org/10.4108/ew.15670",
  "language": "en",
  "licence": "CC-BY-NC-SA",
  "licence_label": "CC BY-NC-SA",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-sa/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "QA1-939"
    ],
    [
      "lcc",
      "QA75.5-76.95"
    ],
    [
      "openalex",
      "2208|Smart Grid Energy Management"
    ]
  ],
  "openalex": "W7223181965",
  "pdf_url": "https://publications.eai.eu/index.php/ew/article/download/15670/4489",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "European Alliance for Innovation (EAI)",
  "ris": "https://research.draconitic.com/paper/5w7s10fmkbjpy.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:42:45Z",
      "native_id": "2c33fec7aa5b418896e937b36f8c76fc",
      "source": "doaj",
      "url": "https://doi.org/10.4108/ew.15670"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "lcc:QA1-939",
      "subfield": "general"
    }
  ],
  "title": "Forecasting and Low-Carbon Demand Response Scheduling of Crane-Group Station-Service Loads for Grid-Friendly Hydropower Station Operation",
  "title_text": "Forecasting and Low-Carbon Demand Response Scheduling of Crane-Group Station-Service Loads for Grid-Friendly Hydropower Station Operation",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:42:45Z",
  "url": "https://research.draconitic.com/paper/5w7s10fmkbjpy",
  "venue": "EAI Endorsed Transactions on Energy Web",
  "versions": []
}