Application of DSAPSO Algorithm in Distribution Network Reconfiguration with Distributed Generation
With the current integration of distributed energy resources into the grid, the structure of distribution networks is becoming more complex. This complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent algorithms. Consequently, tra...
Uložené v:
| Vydané v: | Energy engineering Ročník 121; číslo 1; s. 187 - 201 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Atlanta
Tech Science Press
2024
|
| Predmet: | |
| ISSN: | 1546-0118, 0199-8595, 1546-0118 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | With the current integration of distributed energy resources into the grid, the structure of distribution networks is becoming more complex. This complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent algorithms. Consequently, traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local optima. To tackle this issue, a more advanced particle swarm optimization algorithm is proposed. To address the varying emphases at different stages of the optimization process, a dynamic strategy is implemented to regulate the social and self-learning factors. The Metropolis criterion is introduced into the simulated annealing algorithm to occasionally accept suboptimal solutions, thereby mitigating premature convergence in the population optimization process. The inertia weight is adjusted using the logistic mapping technique to maintain a balance between the algorithm’s global and local search abilities. The incorporation of the Pareto principle involves the consideration of network losses and voltage deviations as objective functions. A fuzzy membership function is employed for selecting the results. Simulation analysis is carried out on the restructuring of the distribution network, using the IEEE-33 node system and the IEEE-69 node system as examples, in conjunction with the integration of distributed energy resources. The findings demonstrate that, in comparison to other intelligent optimization algorithms, the proposed enhanced algorithm demonstrates a shorter convergence time and effectively reduces active power losses within the network. Furthermore, it enhances the amplitude of node voltages, thereby improving the stability of distribution network operations and power supply quality. Additionally, the algorithm exhibits a high level of generality and applicability. |
|---|---|
| AbstractList | With the current integration of distributed energy resources into the grid, the structure of distribution networks is becoming more complex. This complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent algorithms. Consequently, traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local optima. To tackle this issue, a more advanced particle swarm optimization algorithm is proposed. To address the varying emphases at different stages of the optimization process, a dynamic strategy is implemented to regulate the social and self-learning factors. The Metropolis criterion is introduced into the simulated annealing algorithm to occasionally accept suboptimal solutions, thereby mitigating premature convergence in the population optimization process. The inertia weight is adjusted using the logistic mapping technique to maintain a balance between the algorithm’s global and local search abilities. The incorporation of the Pareto principle involves the consideration of network losses and voltage deviations as objective functions. A fuzzy membership function is employed for selecting the results. Simulation analysis is carried out on the restructuring of the distribution network, using the IEEE-33 node system and the IEEE-69 node system as examples, in conjunction with the integration of distributed energy resources. The findings demonstrate that, in comparison to other intelligent optimization algorithms, the proposed enhanced algorithm demonstrates a shorter convergence time and effectively reduces active power losses within the network. Furthermore, it enhances the amplitude of node voltages, thereby improving the stability of distribution network operations and power supply quality. Additionally, the algorithm exhibits a high level of generality and applicability. |
| Author | Yang, Shize Tao, Caixia Li, Taiguo |
| Author_xml | – sequence: 1 givenname: Caixia surname: Tao fullname: Tao, Caixia – sequence: 2 givenname: Shize surname: Yang fullname: Yang, Shize – sequence: 3 givenname: Taiguo surname: Li fullname: Li, Taiguo |
| BookMark | eNp1kE1LAzEQhoMo2FbPXoP3bfO5H8el1SoUK1bPIZtNanS7WZOU4r-37QqK4GkG5nmGmXcITlvXagCuMBpTkiI20XpMEKFjxAgj-AQMMGdpgjDOT3_152AYwhtCiOeoGABVdl1jlYzWtdAZOFuVj6slLJu18za-bqBt4cyG6G21PTIPOu6cf4dPWrnW2PXW9-5uT_-QuoZz3ep-dgHOjGyCvvyuI_Bye_M8vUsWy_n9tFwkihCGk9TwLNsfZojBFWdSKoaoqvMCF8RUMss5qhSXmPK0UDUnVc1lXfGKmkyluMjoCFz3ezvvPrY6ROF153wMguKiyHGK8AGa9JDyLgSvjei83Uj_KTASxyCF1uIQpOiD3Bv8j6FsPD4WvbTNv94Xecl5pQ |
| CitedBy_id | crossref_primary_10_1080_1448837X_2025_2487342 crossref_primary_10_32604_ee_2024_054662 crossref_primary_10_1116_6_0003919 crossref_primary_10_3390_app15126423 crossref_primary_10_1016_j_epsr_2025_112101 crossref_primary_10_1088_1742_6596_2963_1_012009 |
| Cites_doi | 10.7498/aps.70.20202124 10.1016/j.amc.2007.12.053 |
| ContentType | Journal Article |
| Copyright | 2024. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: 2024. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | AAYXX CITATION 3V. 7SP 7TB 7XB 88I 8AF 8FD 8FE 8FG 8FK ABJCF ABUWG AFKRA AZQEC BENPR BGLVJ CCPQU DWQXO FR3 GNUQQ HCIFZ KR7 L6V L7M M2P M7S PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS Q9U S0X |
| DOI | 10.32604/ee.2023.042421 |
| DatabaseName | CrossRef ProQuest Central (Corporate) Electronics & Communications Abstracts Mechanical & Transportation Engineering Abstracts ProQuest Central (purchase pre-March 2016) Science Database (Alumni Edition) STEM Database Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central Technology Collection ProQuest One Community College ProQuest Central Engineering Research Database ProQuest Central Student SciTech Premium Collection Civil Engineering Abstracts ProQuest Engineering Collection Advanced Technologies Database with Aerospace Science Database Engineering Database ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection ProQuest Central Basic SIRS Editorial |
| DatabaseTitle | CrossRef Publicly Available Content Database ProQuest Central Student Technology Collection Technology Research Database ProQuest One Academic Middle East (New) Mechanical & Transportation Engineering Abstracts ProQuest Central Essentials SIRS Editorial ProQuest AP Science ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Engineering Collection ProQuest Central Korea ProQuest Central (New) Advanced Technologies Database with Aerospace Engineering Collection Civil Engineering Abstracts Engineering Database ProQuest Science Journals (Alumni Edition) ProQuest Central Basic ProQuest Science Journals ProQuest One Academic Eastern Edition Electronics & Communications Abstracts ProQuest Technology Collection ProQuest SciTech Collection ProQuest One Academic UKI Edition Materials Science & Engineering Collection Engineering Research Database ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) |
| DatabaseTitleList | Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1546-0118 |
| EndPage | 201 |
| ExternalDocumentID | 10_32604_ee_2023_042421 |
| GroupedDBID | -~X .7F .DC .QJ 29G 2DF 4.4 5VS 88I 8AF 8FE 8FG 8FW 8R4 8R5 AAENE AAFWJ AAIKC AAKQS AAMNW AAYXX ABCCY ABFIM ABHAV ABJCF ABJNI ABPEM ABTAI ABUWG ACGFS ACGOD ACIWK ACTIO ACTTO ADCVX ADDNK AFBWG AFFHD AFION AFKRA AGMYJ AGVKY AGWUF AI. AIJEM ALMA_UNASSIGNED_HOLDINGS ALRRR AQRUH ARCSS AVBZW AZQEC BENPR BGLVJ BLEHA BPHCQ BWMZZ CAG CCCUG CCPQU CE4 CITATION COF CS3 CYRSC DAOYK DGEBU DKSSO DWQXO EBS EJD E~A E~B GNUQQ GTTXZ H13 HCIFZ HF~ HZ~ H~9 H~P J.P KYCEM L6V LJTGL M2P M2Q M4Z M7S MET NA5 NEJ O9- OPCYK P-O P2P PHGZM PHGZT PIMPY PQGLB PQQKQ PROAC PTHSS Q2X RDR RTS RWL RXW S-T S0X TAE TDBHL TFL TFW TN5 TWF UT5 UU3 VH1 WH7 ~S~ 3V. 7SP 7TB 7XB 8FD 8FK FR3 KR7 L7M PKEHL PQEST PQUKI PRINS Q9U |
| ID | FETCH-LOGICAL-c2241-6f577000f2f1b54aac403cd89192fba7850bc5a13569cd52bd5adb5b3f7c61973 |
| IEDL.DBID | M2P |
| ISSN | 1546-0118 0199-8595 |
| IngestDate | Wed Aug 27 18:20:32 EDT 2025 Sat Nov 29 08:16:50 EST 2025 Tue Nov 18 21:51:01 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | false |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c2241-6f577000f2f1b54aac403cd89192fba7850bc5a13569cd52bd5adb5b3f7c61973 |
| OpenAccessLink | https://www.proquest.com/docview/3199816017?pq-origsite=%requestingapplication% |
| PQID | 3199816017 |
| PQPubID | 7125269 |
| PageCount | 15 |
| ParticipantIDs | proquest_reports_3199816017 crossref_primary_10_32604_ee_2023_042421 crossref_citationtrail_10_32604_ee_2023_042421 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-00-00 20240101 |
| PublicationDateYYYYMMDD | 2024-01-01 |
| PublicationDate_xml | – year: 2024 text: 2024-00-00 |
| PublicationDecade | 2020 |
| PublicationPlace | Atlanta |
| PublicationPlace_xml | – name: Atlanta |
| PublicationTitle | Energy engineering |
| PublicationYear | 2024 |
| Publisher | Tech Science Press |
| Publisher_xml | – name: Tech Science Press |
| References | Zhang (ref13) 2021; 43 Liang (ref19) 2017; 29 Wei (ref9) 2023; 35 Pan (ref5) 2020; 48 Chen (ref11) 2021; 38 Si (ref1) 2020 Zhang (ref10) 2022; 50 Wu (ref6) 2023; 23 Zang (ref12) 2021; 70 Olamaei (ref15) 2008; 201 Chen (ref17) 2022; 41 Wang (ref2) 2015; 39 Zhou (ref3) 2021 Li (ref8) 2021; 28 Zheng (ref14) 2023; 51 Xu (ref18) 2017 Arya (ref16) 2011; 34 Li (ref7) 2019; 47 Wang (ref4) 2022; 39 |
| References_xml | – year: 2020 ident: ref1 publication-title: Research on distribution network reconstruction considering distributed power access (Master Thesis) – volume: 39 start-page: 56 year: 2022 ident: ref4 article-title: Research on distribution network reconstruction with distributed power sources based on improved grey wolf algorithm publication-title: Modern Electric Power – volume: 43 start-page: 53 year: 2021 ident: ref13 article-title: Application of improved particle swarm algorithm in power economic dispatch publication-title: Manufacturing Automation – volume: 23 start-page: 626 year: 2023 ident: ref6 article-title: Optimization and reconstruction of distribution network with distributed power sources based on SA-CS algorithm publication-title: Science Technology and Engineering – volume: 70 start-page: 229 year: 2021 ident: ref12 article-title: Distribution of nonuniform combustion field reconstruction based on improved simulated annealing algorithm publication-title: Acta Physica Sinica doi: 10.7498/aps.70.20202124 – volume: 48 start-page: 102 year: 2020 ident: ref5 article-title: Research on active distribution network reconstruction strategy with distributed power sources publication-title: Power System Protection and Control – volume: 29 start-page: 90 year: 2017 ident: ref19 article-title: Application of improved harmony search algorithm in distribution network reconstruction publication-title: Proceedings of the CSEE – volume: 47 start-page: 30 year: 2019 ident: ref7 article-title: Research on multi-objective active reconstruction of distribution network based on genetic algorithm with gated communities publication-title: Power System Protection and Control – volume: 50 start-page: 25 year: 2022 ident: ref10 article-title: Reconstruction method for distribution network with ZIP load considering mixed integer linear programming publication-title: Power System Protection and Control – year: 2021 ident: ref3 publication-title: Research on distribution network reconstruction with distributed generation of renewable energy (Master Thesis) – volume: 51 start-page: 38 year: 2023 ident: ref14 article-title: Transformer fault diagnosis based on multi-strategy ISOA optimized SVM publication-title: Smart Grid – volume: 39 start-page: 1860 year: 2015 ident: ref2 article-title: Multi-objective reactive power optimization considering multiple wind turbines connected to distribution networks publication-title: Power System Technology – volume: 34 start-page: 54 year: 2011 ident: ref16 article-title: Reconfiguration of electric distribution network using modified particle swarm optimization publication-title: International Journal of Computer Applications – volume: 35 start-page: 30 year: 2023 ident: ref9 article-title: Multi-objective distribution network reconstruction method based on discrete monkey algorithm publication-title: Proceedings of the CSEE – volume: 38 start-page: 245 year: 2021 ident: ref11 article-title: Adaptive simulated annealing algorithm for solving traveling salesman problem publication-title: Control Theory and Applications – year: 2017 ident: ref18 publication-title: Research on distribution network reconstruction with distributed power sources (Master Thesis) – volume: 201 start-page: 575 year: 2008 ident: ref15 article-title: Application of particle swarm optimization for distribution feeder reconfiguration considering distributed generators publication-title: Applied Mathematics and Computation doi: 10.1016/j.amc.2007.12.053 – volume: 41 start-page: 111 year: 2022 ident: ref17 article-title: Optimization and reconstruction design of distribution network with distributed power sources based on GA-QPSO algorithm publication-title: Experimental Research and Exploration – volume: 28 start-page: 931 year: 2021 ident: ref8 article-title: Research on optimal reconstruction method for distribution network with new energy sources connected publication-title: Control Engineering |
| SSID | ssj0005809 |
| Score | 2.3040545 |
| Snippet | With the current integration of distributed energy resources into the grid, the structure of distribution networks is becoming more complex. This complexity... |
| SourceID | proquest crossref |
| SourceType | Aggregation Database Enrichment Source Index Database |
| StartPage | 187 |
| SubjectTerms | Algorithms Complexity Convergence Distributed generation Electric power loss Energy resources Energy sources Nodes Optimization Pareto optimum Particle swarm optimization Reconfiguration Simulated annealing Solution space |
| Title | Application of DSAPSO Algorithm in Distribution Network Reconfiguration with Distributed Generation |
| URI | https://www.proquest.com/docview/3199816017 |
| Volume | 121 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 1546-0118 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0005809 issn: 1546-0118 databaseCode: M7S dateStart: 20200101 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1546-0118 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0005809 issn: 1546-0118 databaseCode: BENPR dateStart: 20200101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1546-0118 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0005809 issn: 1546-0118 databaseCode: PIMPY dateStart: 20200101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVPQU databaseName: Science Database customDbUrl: eissn: 1546-0118 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0005809 issn: 1546-0118 databaseCode: M2P dateStart: 20200101 isFulltext: true titleUrlDefault: https://search.proquest.com/sciencejournals providerName: ProQuest – providerCode: PRVAWR databaseName: Taylor and Francis Online Journals customDbUrl: eissn: 1546-0118 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0005809 issn: 1546-0118 databaseCode: TFW dateStart: 19970101 isFulltext: true titleUrlDefault: https://www.tandfonline.com providerName: Taylor & Francis |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV05T8MwFLa4Bhi4Ebc8MLAYGh-xM6FyCSQoEQUJpii241IJUqAtvx-_OKUwwMKSIT4U-T2953fk-xDaYzbhsaGOOGcLwhMWk5xLSoDWQdtI6YarWEuuZKulHh6StE649eu2ypFNrAy17RnIkR8y-Bks8uGDPHp9I8AaBdXVmkJjEk17z8Uh-Lqm6bjFQ4UWD7-UAI5XgPbxF5YGPywAI5OyAyj90einV_pplCtPc77w329cRPP1HRM3g1IsoYmiXEZz35AHV5BpjgvXuOfwabuZtm9w87nj9xs8veBuiU8BU7emw8Kt0C6OIVwtXbczDJqDIY87nllYHHCsYWwV3Z-f3Z1ckJpvgRhw5CR2Qkp_bI66SAue54Y3mLEq8bdAp3OpREMbkUdMxImxgmorcquFZk4aH4dJtoamyl5ZrCOsnIm1oVZaRzlzTlnLEmUEk8IoKcUGOhidd2ZqMHLgxHjOfFBSCSgrigwElAUBbaD9rwWvAYfj96lbI-lkdeElG0tm88_RLTTrd-Ihx7KNpgbvw2IHzZiPQbf_voumj89a6e1upWXwlG3_Lr28Th8_AWna2-U |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LTxsxEB5BqEQ5tJSHSGmpD1TqxZD1Y-09oCpqiogIaSRAgtOyftFIsAESWvVP8Rtrx7tNOZQbh57ttbzrb2c8nvH3AWxTk7FUE4edMxazjKa4YILgIOugTCJVy01VS3qi35dnZ9lgDh7quzChrLK2iVNDbUY6nJHv0nAZLPHhg_h8c4uDalTIrtYSGhEWh_bXTx-yjfe6Hb--HwnZ_3ry5QBXqgJYB3eFU8eF8IbAEZcozopCsxbVRmZ-r-NUISRvKc2LhPI004YTZXhhFFfUCe2jDUH9uPOwwDzYZQMWBt2jwfmsqETGohI_WRyYwyKZkN8itdiuDaychO6EZCNJHvvBx25g6tv2X_9vX2UZXlW7aNSOsH8Dc7ZcgaW_uBVXQbdnqXk0cqhz3B4cf0Ptq0s__8n3azQsUSewBleCX6gfC-JRCMhLN7y8j_8GCifVs57WoMjUHdrW4PRZ3nIdGuWotBuApNOp0sQI4wijzkljaCY1p4JrKQRvwk69vrmu6NaD6sdV7sOuKSBya_MAiDwCogmf_jxwE5lG_t11s0ZDXqWW8hkS3j7Z-gEWD06Oenmv2z_chJd-VBZPlN5BY3J3b9_DC_1jMhzfbVXYRnDx3MD5DRtrNWw |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Application+of+DSAPSO+Algorithm+in+Distribution+Network+Reconfiguration+with+Distributed+Generation&rft.jtitle=Energy+engineering&rft.au=Tao%2C+Caixia&rft.au=Yang%2C+Shize&rft.au=Li%2C+Taiguo&rft.date=2024&rft.issn=1546-0118&rft.eissn=1546-0118&rft.volume=121&rft.issue=1&rft.spage=187&rft.epage=201&rft_id=info:doi/10.32604%2Fee.2023.042421&rft.externalDBID=n%2Fa&rft.externalDocID=10_32604_ee_2023_042421 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1546-0118&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1546-0118&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1546-0118&client=summon |