{
  "abstract": "Large-scale blackouts increasingly arise from heterogeneous causes such as natural disasters, equipment failures and cyber attacks, which force power utilities to decide, before any event is known, both how large a financial reserve to hold and how to convert it into emergency response capability. This paper develops a coordinated optimization of reserve fund scale and emergency resource allocation under multi-type large-scale blackout risk. A unified set of multi-type blackout scenarios is constructed by Monte Carlo sampling and condensed by a tail-aware scenario reduction into a tractable representative set. The restoration technology distinguishes a repair channel, in which crews and materials enter as complementary inputs, from a re-energization channel supplied by mobile equipment, and both channels carry cause-dependent productivity so that the response mechanism differs across disasters, failures and cyber attacks. A two-stage risk-averse stochastic program co-optimizes the first-stage reserve fund and resource pre-positioning together with the second-stage post-event response, embeds a conditional-value-at-risk measure to limit extreme losses, and is solved by a sample-average-approximation scheme with an L-shaped decomposition. Case studies on a stylized 118-node planning instance show that the coordinated risk-averse plan attains an out-of-sample risk-adjusted cost of 352.1 $M against 558.2 $M for a decoupled scheme and 604.7 $M for a risk-neutral scheme, while holding CVaR0.95 to 154.3 $M and expected unserved energy to 1478 MWh. The tail-aware reduction keeps the CVaR0.95 error at 1.08% of its raw-sample value with 200 retained scenarios, against 11.91% for random subsampling. The cause mix governs the resource portfolio, and the value of lost load is the dominant economic driver of the appropriate reserve scale, with a standardized regression coefficient of 0.66 under joint parameter uncertainty. The study is presented as a methodological proof of concept: the instance is synthetic and network operability is not modelled, so the results characterize the structure of the preparedness trade-off rather than a validated prescription for a specific utility.",
  "abstract_shown": true,
  "affiliations": [
    "School of Accounting, Chengdu College of Arts and Sciences, Chengdu, China",
    "School of Economics and Management, Southwest Petroleum University, Chengdu, China",
    "School of Science, Xihua University, Chengdu, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yongju Gu"
    },
    {
      "affs": [
        1
      ],
      "name": "Zhiwei Zhang"
    },
    {
      "affs": [
        2
      ],
      "name": "Liyun Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cd3g52p8t0xnb.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0352511",
  "fulltext_url": null,
  "id": "cd3g52p8t0xnb",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0352511",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2205|Infrastructure Resilience and Vulnerability Analysis"
    ],
    [
      "plos",
      "/Computer and information sciences/Network analysis/Network resilience"
    ],
    [
      "plos",
      "/Earth sciences/Natural disasters"
    ],
    [
      "plos",
      "/Engineering and technology/Equipment"
    ],
    [
      "plos",
      "/Engineering and technology/Management engineering/Risk management/Insurance"
    ],
    [
      "plos",
      "/Social sciences/Economics"
    ],
    [
      "plos",
      "/Social sciences/Economics/Commerce/Procurement"
    ],
    [
      "plos",
      "/Social sciences/Economics/Finance"
    ],
    [
      "plos",
      "/Social sciences/Economics/Finance/Financial management/Budgets"
    ]
  ],
  "openalex": "W7214201871",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0352511&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-24",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/cd3g52p8t0xnb.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:43Z",
      "native_id": "10.1371/journal.pone.0352511",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0352511"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "plos:earth sciences",
      "subfield": "general"
    }
  ],
  "title": "Coordinated optimization of reserve fund scale and emergency resource allocation for power utilities under multi-type large-scale blackout risks",
  "title_text": "Coordinated optimization of reserve fund scale and emergency resource allocation for power utilities under multi-type large-scale blackout risks",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:43Z",
  "url": "https://research.draconitic.com/paper/cd3g52p8t0xnb",
  "venue": "PLOS One",
  "versions": []
}