Resilient Distribution System Reconfiguration Based on Genetic Algorithms Considering Load Margin and Contingencies.
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| Title: | Resilient Distribution System Reconfiguration Based on Genetic Algorithms Considering Load Margin and Contingencies. |
|---|---|
| Authors: | Muñoz, Jorge1 (AUTHOR) jmunoz@ups.edu.ec, Tipán, Luis1 (AUTHOR), Cuji, Cristian1 (AUTHOR), Jaramillo, Manuel1 (AUTHOR) |
| Source: | Energies (19961073). Jun2025, Vol. 18 Issue 11, p2889. 22p. |
| Subjects: | Genetic load, Test systems, Genetic algorithms, Topology, Voltage |
| Abstract: | This paper addresses the challenge of restoring electrical service in distribution systems (DS) under contingency scenarios using a genetic algorithm (GA) implemented in MATLAB. The proposed methodology seeks to maximize restored load, considering operational constraints such as line loadability, voltage limits, and radial topology preservation. It is evaluated with simulations on the IEEE 34-bus test system under four contingency scenarios that consider the disconnection of specific branches. The algorithm's ability to restore service is demonstrated by identifying optimal auxiliary line reconnections. The method maximizes restored load, achieving between 97% and 99% load reconnection, with an average of 98.8% across the four cases analyzed. Bus voltages remain above 0.95 pu and below the upper limit. Furthermore, test feeder results demonstrate that line loadability is mostly below 60% of the post-reconfiguration loadability. [ABSTRACT FROM AUTHOR] |
| Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 185868062 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Resilient Distribution System Reconfiguration Based on Genetic Algorithms Considering Load Margin and Contingencies. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Muñoz%2C+Jorge%22">Muñoz, Jorge</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jmunoz@ups.edu.ec</i><br /><searchLink fieldCode="AR" term="%22Tipán%2C+Luis%22">Tipán, Luis</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cuji%2C+Cristian%22">Cuji, Cristian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jaramillo%2C+Manuel%22">Jaramillo, Manuel</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jun2025, Vol. 18 Issue 11, p2889. 22p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Genetic+load%22">Genetic load</searchLink><br /><searchLink fieldCode="DE" term="%22Test+systems%22">Test systems</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Topology%22">Topology</searchLink><br /><searchLink fieldCode="DE" term="%22Voltage%22">Voltage</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper addresses the challenge of restoring electrical service in distribution systems (DS) under contingency scenarios using a genetic algorithm (GA) implemented in MATLAB. The proposed methodology seeks to maximize restored load, considering operational constraints such as line loadability, voltage limits, and radial topology preservation. It is evaluated with simulations on the IEEE 34-bus test system under four contingency scenarios that consider the disconnection of specific branches. The algorithm's ability to restore service is demonstrated by identifying optimal auxiliary line reconnections. The method maximizes restored load, achieving between 97% and 99% load reconnection, with an average of 98.8% across the four cases analyzed. Bus voltages remain above 0.95 pu and below the upper limit. Furthermore, test feeder results demonstrate that line loadability is mostly below 60% of the post-reconfiguration loadability. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=185868062 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en18112889 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 2889 Subjects: – SubjectFull: Genetic load Type: general – SubjectFull: Test systems Type: general – SubjectFull: Genetic algorithms Type: general – SubjectFull: Topology Type: general – SubjectFull: Voltage Type: general Titles: – TitleFull: Resilient Distribution System Reconfiguration Based on Genetic Algorithms Considering Load Margin and Contingencies. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Muñoz, Jorge – PersonEntity: Name: NameFull: Tipán, Luis – PersonEntity: Name: NameFull: Cuji, Cristian – PersonEntity: Name: NameFull: Jaramillo, Manuel IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 18 – Type: issue Value: 11 Titles: – TitleFull: Energies (19961073) Type: main |
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