{
  "abstract": "Hydrogen energy storage microgrids can improve renewable-energy accommodation and cross-energy flexibility, while repeated boundary-variable exchange in distributed scheduling may expose private operating information. This paper develops a privacy-aware electricity–carbon co-optimization framework that integrates day-ahead distributed clearing with intra-day model predictive control (MPC). The day-ahead model coordinates electricity, heat, hydrogen conversion, battery storage, and thermal storage while accounting for direct gas-boiler emissions and indirect emissions associated with imported electricity. A dynamically decaying Laplace mechanism is incorporated into the alternating direction method of multipliers (ADMM), and privacy performance is evaluated through finite-transcript privacy-budget accounting and reconstruction-based leakage analysis. Compared with fixed Laplace noise, the dynamic mechanism exhibits a higher baseline mean leakage-risk score (0.67 versus 0.58), but reduces the centralized cost gap from 2.85% to 0.94% and the number of iterations from 50 to 38. Matched-condition tests further show lower cost gaps at equal leakage risk and lower leakage risk at equal cost gap. An inverse-variance reconstruction attack reveals increased information exposure during later iterations. Meanwhile, the intra-day MPC maintains feasible multi-energy operation under the tested forecast errors. The base-case operational emissions are 2.09 t CO2, and the carbon-parameter sensitivity analysis shows a smooth variation in cleared cost. The results demonstrate the coordinated economic, privacy, and convergence characteristics of the proposed scheduling framework.",
  "abstract_shown": true,
  "affiliations": [
    "Economic and Technical Research Institute of State Grid Xinjiang Electric Power Co., Ltd., Urumqi, Xinjiang, China",
    "School of Electrical and Electronic Engineering, North China Electric Power University, Beijing, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Zhongzheng Li"
    },
    {
      "affs": [
        1
      ],
      "name": "Mengke Liao"
    },
    {
      "name": "Yan Wang"
    },
    {
      "name": "Ruixuan Zhang"
    },
    {
      "name": "Jiawen Sun"
    },
    {
      "name": "Hua Zheng"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bj18jqvd6ny6j.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0350884",
  "fulltext_url": null,
  "id": "bj18jqvd6ny6j",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0350884",
  "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",
      "2208|Integrated Energy Systems Optimization"
    ],
    [
      "plos",
      "/Engineering and technology/Energy and power/Alternative energy"
    ],
    [
      "plos",
      "/Engineering and technology/Energy and power/Alternative energy/Photovoltaic power"
    ],
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      "plos",
      "/Engineering and technology/Energy and power/Fuels"
    ],
    [
      "plos",
      "/Engineering and technology/Energy and power/Power distribution/Power grids"
    ],
    [
      "plos",
      "/Engineering and technology/Equipment"
    ],
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      "plos",
      "/Physical sciences/Chemistry/Chemical elements/Hydrogen"
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      "plos",
      "/Physical sciences/Materials science/Materials/Fuels"
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      "plos",
      "/Physical sciences/Mathematics/Statistics/Statistical methods/Forecasting"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Electricity"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Forecasting"
    ]
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  "openalex": "W7214105741",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0350884&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-23",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/bj18jqvd6ny6j.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:43Z",
      "native_id": "10.1371/journal.pone.0350884",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0350884"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "plos:materials science",
      "subfield": "materials-polymers"
    },
    {
      "field": "mathematics",
      "origin": "plos:statistical methods",
      "subfield": "statistics"
    }
  ],
  "title": "Distributed electricity–carbon co-optimization of hydrogen energy storage microgrids with dynamic laplace perturbation and model predictive control",
  "title_text": "Distributed electricity–carbon co-optimization of hydrogen energy storage microgrids with dynamic laplace perturbation and model predictive control",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:43Z",
  "url": "https://research.draconitic.com/paper/bj18jqvd6ny6j",
  "venue": "PLOS One",
  "versions": []
}