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Reliability Assessment of AC/DC Hybrid Distribution Networks with Large-Scale Renewable Energy Integration.

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Title: Reliability Assessment of AC/DC Hybrid Distribution Networks with Large-Scale Renewable Energy Integration.
Authors: Fan, Chuanguang1 (AUTHOR), Shi, Nian1,2 (AUTHOR), Zhao, Lu1 (AUTHOR), Cheng, Jie1 (AUTHOR), Liu, Xiaozhu2 (AUTHOR)
Source: Energies (19961073). Jun2026, Vol. 19 Issue 11, p2549. 27p.
Subject Terms: *Hybrid power systems, *Electric power system reliability, *Dimensional reduction algorithms, *Electric power systems, *Reliability in engineering, *Clustering algorithms, *K-means clustering
Abstract: With the advancement of carbon peaking and carbon neutrality goals, the increasing penetration of renewable energy sources such as wind and photovoltaic power poses severe challenges to the power supply reliability of AC/DC hybrid distribution networks due to their fluctuating, intermittent, and stochastic outputs. This paper proposes a reliability assessment method for AC/DC hybrid distribution networks under large-scale renewable energy integration based on clustering of typical operating scenarios. The net load duration curve is adopted as the feature variable to characterize typical operating scenarios. An improved t-distributed Stochastic Neighbor Embedding (t-SNE) nonlinear dimensionality reduction method with Kullback–Leibler (KL) divergence elbow correction is proposed for effective reduction of high-dimensional time-series data. An adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) parameter optimization method based on the k-nearest-neighbor curve and a secondary K-means clustering method based on entropy-weighted multi-objective optimization are further developed, forming a hybrid t-SNE-DBSCAN–K-means clustering algorithm. The power supply reliability is then assessed based on the clustered typical operating scenarios. A typical AC/DC hybrid distribution network is used as the test system. Results show that the DB index of the proposed clustering method improves by at least 22% compared with conventional methods, the maximum relative error between the typical-day-based and full time-series simulation results is less than 6%, and the computational efficiency improves by about 8.8 times, achieving a good balance between accuracy and efficiency. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 194587937
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Label: Title
  Group: Ti
  Data: Reliability Assessment of AC/DC Hybrid Distribution Networks with Large-Scale Renewable Energy Integration.
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  Data: <searchLink fieldCode="AR" term="%22Fan%2C+Chuanguang%22">Fan, Chuanguang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shi%2C+Nian%22">Shi, Nian</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Lu%22">Zhao, Lu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cheng%2C+Jie%22">Cheng, Jie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Xiaozhu%22">Liu, Xiaozhu</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, p2549. 27p.
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  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Hybrid+power+systems%22">Hybrid power systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+power+system+reliability%22">Electric power system reliability</searchLink><br />*<searchLink fieldCode="DE" term="%22Dimensional+reduction+algorithms%22">Dimensional reduction algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+power+systems%22">Electric power systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Reliability+in+engineering%22">Reliability in engineering</searchLink><br />*<searchLink fieldCode="DE" term="%22Clustering+algorithms%22">Clustering algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22K-means+clustering%22">K-means clustering</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: With the advancement of carbon peaking and carbon neutrality goals, the increasing penetration of renewable energy sources such as wind and photovoltaic power poses severe challenges to the power supply reliability of AC/DC hybrid distribution networks due to their fluctuating, intermittent, and stochastic outputs. This paper proposes a reliability assessment method for AC/DC hybrid distribution networks under large-scale renewable energy integration based on clustering of typical operating scenarios. The net load duration curve is adopted as the feature variable to characterize typical operating scenarios. An improved t-distributed Stochastic Neighbor Embedding (t-SNE) nonlinear dimensionality reduction method with Kullback–Leibler (KL) divergence elbow correction is proposed for effective reduction of high-dimensional time-series data. An adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) parameter optimization method based on the k-nearest-neighbor curve and a secondary K-means clustering method based on entropy-weighted multi-objective optimization are further developed, forming a hybrid t-SNE-DBSCAN–K-means clustering algorithm. The power supply reliability is then assessed based on the clustered typical operating scenarios. A typical AC/DC hybrid distribution network is used as the test system. Results show that the DB index of the proposed clustering method improves by at least 22% compared with conventional methods, the maximum relative error between the typical-day-based and full time-series simulation results is less than 6%, and the computational efficiency improves by about 8.8 times, achieving a good balance between accuracy and efficiency. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.3390/en19112549
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 27
        StartPage: 2549
    Subjects:
      – SubjectFull: Hybrid power systems
        Type: general
      – SubjectFull: Electric power system reliability
        Type: general
      – SubjectFull: Dimensional reduction algorithms
        Type: general
      – SubjectFull: Electric power systems
        Type: general
      – SubjectFull: Reliability in engineering
        Type: general
      – SubjectFull: Clustering algorithms
        Type: general
      – SubjectFull: K-means clustering
        Type: general
    Titles:
      – TitleFull: Reliability Assessment of AC/DC Hybrid Distribution Networks with Large-Scale Renewable Energy Integration.
        Type: main
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          Name:
            NameFull: Fan, Chuanguang
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            NameFull: Shi, Nian
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            NameFull: Zhao, Lu
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            NameFull: Cheng, Jie
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            NameFull: Liu, Xiaozhu
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            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
          Identifiers:
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              Value: 19961073
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              Value: 19
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              Value: 11
          Titles:
            – TitleFull: Energies (19961073)
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