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Capacity Optimization of Offshore Microgrids Considering Uncertainty and Conditional Risk.

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Title: Capacity Optimization of Offshore Microgrids Considering Uncertainty and Conditional Risk.
Authors: Fan, Honggang1 (AUTHOR) fanhg@tsinghua.edu.cn, Liu, Yan2 (AUTHOR), Chen, Zipeng1 (AUTHOR), Wang, Cui2 (AUTHOR), Wang, Wankun2 (AUTHOR)
Source: Energies (19961073). Jun2026, Vol. 19 Issue 11, p2585. 24p.
Subject Terms: *Microgrids, *Value at risk, *Forecasting methodology, *Stochastic models, *Resource allocation, *Mathematical optimization
Abstract: The high-penetration integration of offshore renewable energy introduces significant challenges, including high volatility, randomness, and insufficient energy accommodation, which place higher demands on the planning and operation of offshore integrated energy systems. To address these issues, this paper proposes an offshore multi-energy coupled DC microgrid system integrating wind, photovoltaic, tidal current, and wave energy, together with flexible loads such as seawater desalination and power-to-hydrogen. A hybrid forecasting model based on EMD-PCA-LSTM is developed to improve prediction accuracy under uncertain conditions. On this basis, a two-stage optimization framework considering both economic efficiency and operational risk is established. At the planning level, a joint operation–planning model incorporating Conditional Value-at-Risk (CVaR) is formulated to determine the optimal capacity configuration by minimizing the total annualized cost and risk cost. At the operational level, a multi-time-scale rolling optimization model is constructed to enhance system adaptability under renewable fluctuations. Case study results demonstrate that the proposed method significantly improves renewable energy accommodation, reduces the curtailment rate to 0.7%, and effectively balances economic performance and operational stability. The proposed framework provides a practical and efficient approach for capacity allocation and optimal operation of offshore multi-energy coupled systems. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 194587973
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PubType: Academic Journal
PubTypeId: academicJournal
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  Label: Title
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  Data: Capacity Optimization of Offshore Microgrids Considering Uncertainty and Conditional Risk.
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  Data: <searchLink fieldCode="AR" term="%22Fan%2C+Honggang%22">Fan, Honggang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> fanhg@tsinghua.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Yan%22">Liu, Yan</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Zipeng%22">Chen, Zipeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Cui%22">Wang, Cui</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Wankun%22">Wang, Wankun</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jun2026, Vol. 19 Issue 11, p2585. 24p.
– Name: Subject
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  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Microgrids%22">Microgrids</searchLink><br />*<searchLink fieldCode="DE" term="%22Value+at+risk%22">Value at risk</searchLink><br />*<searchLink fieldCode="DE" term="%22Forecasting+methodology%22">Forecasting methodology</searchLink><br />*<searchLink fieldCode="DE" term="%22Stochastic+models%22">Stochastic models</searchLink><br />*<searchLink fieldCode="DE" term="%22Resource+allocation%22">Resource allocation</searchLink><br />*<searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The high-penetration integration of offshore renewable energy introduces significant challenges, including high volatility, randomness, and insufficient energy accommodation, which place higher demands on the planning and operation of offshore integrated energy systems. To address these issues, this paper proposes an offshore multi-energy coupled DC microgrid system integrating wind, photovoltaic, tidal current, and wave energy, together with flexible loads such as seawater desalination and power-to-hydrogen. A hybrid forecasting model based on EMD-PCA-LSTM is developed to improve prediction accuracy under uncertain conditions. On this basis, a two-stage optimization framework considering both economic efficiency and operational risk is established. At the planning level, a joint operation–planning model incorporating Conditional Value-at-Risk (CVaR) is formulated to determine the optimal capacity configuration by minimizing the total annualized cost and risk cost. At the operational level, a multi-time-scale rolling optimization model is constructed to enhance system adaptability under renewable fluctuations. Case study results demonstrate that the proposed method significantly improves renewable energy accommodation, reduces the curtailment rate to 0.7%, and effectively balances economic performance and operational stability. The proposed framework provides a practical and efficient approach for capacity allocation and optimal operation of offshore multi-energy coupled systems. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.3390/en19112585
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 24
        StartPage: 2585
    Subjects:
      – SubjectFull: Microgrids
        Type: general
      – SubjectFull: Value at risk
        Type: general
      – SubjectFull: Forecasting methodology
        Type: general
      – SubjectFull: Stochastic models
        Type: general
      – SubjectFull: Resource allocation
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
    Titles:
      – TitleFull: Capacity Optimization of Offshore Microgrids Considering Uncertainty and Conditional Risk.
        Type: main
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          Name:
            NameFull: Fan, Honggang
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            NameFull: Liu, Yan
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            NameFull: Chen, Zipeng
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            NameFull: Wang, Cui
      – PersonEntity:
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            NameFull: Wang, Wankun
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          Dates:
            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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              Value: 19961073
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              Value: 19
            – Type: issue
              Value: 11
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            – TitleFull: Energies (19961073)
              Type: main
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