Improved Multiobjective Genetic Algorithm for Partitioning Distributed Photovoltaic Clusters: Balancing Spatial Distance and Power Similarity
The prediction of power output from photovoltaic generation clusters is crucial for optimizing the dispatch of regional photovoltaic generation. Enhancing the accuracy of power prediction for photovoltaic power plant clusters requires the segmentation of distributed photovoltaic systems into cluster...
Uloženo v:
| Vydáno v: | Journal of computing and information technology Ročník 32; číslo 4; s. 251 - 264 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Journal Article Paper |
| Jazyk: | angličtina |
| Vydáno: |
Sveuciliste U Zagrebu
01.12.2024
Sveučilište u Zagrebu Fakultet elektrotehnike i računarstva University of Zagreb Faculty of Electrical Engineering and Computing |
| Témata: | |
| ISSN: | 1330-1136, 1846-3908 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | The prediction of power output from photovoltaic generation clusters is crucial for optimizing the dispatch of regional photovoltaic generation. Enhancing the accuracy of power prediction for photovoltaic power plant clusters requires the segmentation of distributed photovoltaic systems into clusters. This paper proposes a method for partitioning distributed photovoltaic clusters using a multiobjective genetic algorithm NSGA2, with spatial distance modularity and electricity similarity as optimization objectives to determine the optimal cluster partitioning scheme. The numerical examples and experimental results of the case analysis demonstrate a significant improvement in the convergence speed of the prediction system when employing the clustering partitioning method. This cluster segmentation algorithm significantly reduces the complexity and investment cost of the prediction system. |
|---|---|
| AbstractList | The prediction of power output from photovoltaic generation clusters is crucial for optimizing the dispatch of regional photovoltaic generation. Enhancing the accuracy of power prediction for photovoltaic power plant clusters requires the segmentation of distributed photovoltaic systems into clusters. This paper proposes a method for partitioning distributed photovoltaic clusters using a multiobjective genetic algorithm NSGA2, with spatial distance modularity and electricity similarity as optimization objectives to determine the optimal cluster partitioning scheme. The numerical examples and experimental results of the case analysis demonstrate a significant improvement in the convergence speed of the prediction system when employing the clustering partitioning method. This cluster segmentation algorithm significantly reduces the complexity and investment cost of the prediction system. The prediction of power output from photovoltaic generation clusters is crucial for optimizing the dispatch of regional photovoltaic generation. Enhancing the accuracy of power prediction for photovoltaic power plant clusters requires the segmentation of distributed photovoltaic systems into clusters. This paper proposes a method for partitioning distributed photovoltaic clusters using a multiobjective genetic algorithm NSGA2, with spatial distance modularity and electricity similarity as optimization objectives to determine the optimal cluster partitioning scheme. The numerical examples and experimental results of the case analysis demonstrate a significant improvement in the convergence speed of the prediction system when employing the clustering partitioning method. This cluster segmentation algorithm significantly reduces the complexity and investment cost of the prediction system. ACM CCS (2012) Classification: Computing methodologies [right arrow] Artificial Intelligence [right arrow] Planning and Scheduling Keywords: multi-objective genetic algorithm, distributed photovoltaic, cluster partitioning |
| Audience | Academic |
| Author | Chen, Yansen Hu, Xudong Cheng, Kai Li, Zhuohuan Pan, Shixian |
| Author_xml | – sequence: 1 givenname: Yansen surname: Chen fullname: Chen, Yansen organization: China Southern Grid Digital Grid Research Institute Co. Ltd., Guangzhou, China – sequence: 2 givenname: Kai surname: Cheng fullname: Cheng, Kai organization: China Southern Grid Digital Grid Research Institute Co. Ltd., Guangzhou, China – sequence: 3 givenname: Zhuohuan surname: Li fullname: Li, Zhuohuan organization: China Southern Grid Digital Grid Research Institute Co. Ltd., Guangzhou, China – sequence: 4 givenname: Shixian surname: Pan fullname: Pan, Shixian organization: China Southern Grid Digital Grid Research Institute Co. Ltd., Guangzhou, China – sequence: 5 givenname: Xudong surname: Hu fullname: Hu, Xudong organization: China Southern Grid Digital Grid Research Institute Co. Ltd., Guangzhou, China |
| BookMark | eNptkt9u0zAUxiM0JMbYA3AXiSsu0vlfHIe70sGoNMRE4do6cZzWJYkr2y3sIfbOnDYTUiXsCx99-n2ffeTzOrsY_Wiz7C0lM0ZKzm6MS1gxMaOElKoiL7JLqoQseE3UBdack4JSLl9l1zFuCS5eSynoZfa0HHbBH2ybf933yflma01yB5vf2dEmZ_J5v_bBpc2Qdz7kDxCSQ2x04zq_dTEF1-wTuh82PvmD7xOgZ9HvY7Ihfsg_Qg-jOcKrHSQH_cmEks1hRJf_bUO-coPrAS95fJO97KCP9vr5vMp-fv70Y_GluP92t1zM7wvDJSGFqVpWNoIpYQgzopWNbOoSFLYoWFVaI3llLeOKKymgpAyYsm2nRNeptjI1v8qWU27rYat3wQ0QHrUHp0-CD2t9bNT0VleUCtaYrqaSiwaEMnWN11BFKyIq1mJWMWVtgoFfZ2GTEoOxWGrOZCkI8u8mfg0Y78bOpwBmcNHouWLYE8ZKpGb_oXC3dnAGv79zqJ8Z3p8ZkEn2T1rDPka9XH0_Z-nEmuBjDLb792pK9GmiNE6UPk6Ufp4o_hcnDr_1 |
| CODEN | CJCTEM |
| ContentType | Journal Article Paper |
| Copyright | COPYRIGHT 2024 Sveuciliste U Zagrebu |
| Copyright_xml | – notice: COPYRIGHT 2024 Sveuciliste U Zagrebu |
| DBID | AAYXX CITATION ISR VP8 DOA |
| DOI | 10.20532/cit.2024.1005870 |
| DatabaseName | CrossRef Gale In Context: Science Portal of Croatian Scientific and Professional Journals – HRČAK DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1846-3908 |
| EndPage | 264 |
| ExternalDocumentID | oai_doaj_org_article_71142bcf91634ba48c9913418170472d oai_hrcak_srce_hr_326540 A822840476 10_20532_cit_2024_1005870 |
| GeographicLocations | China |
| GeographicLocations_xml | – name: China |
| GroupedDBID | .4S .DC 29B 29K 2WC 5GY 5VS 77I AAYXX ADMLS ALMA_UNASSIGNED_HOLDINGS ARCSS BAIFH BBTPI CITATION CS3 D-I DU5 E3Z EBS EDO EJD EN8 EOJEC GROUPED_DOAJ I-F IAO ICD ISR ITC IVC KQ8 KWQ MK~ ML~ M~E OBODZ OK1 OVT P2P PV9 RZL TR2 TUS VP8 XH6 ICW IPNFZ RIG |
| ID | FETCH-LOGICAL-c3600-c7d25b4284c02c4d6b6b95a81134275ec637ee2383864a512a28edf84ff8d7c93 |
| IEDL.DBID | DOA |
| ISSN | 1330-1136 |
| IngestDate | Fri Oct 03 12:45:26 EDT 2025 Fri Jan 10 04:27:59 EST 2025 Sat Nov 29 13:52:41 EST 2025 Sat Nov 29 10:35:35 EST 2025 Thu Nov 13 16:04:34 EST 2025 Sat Nov 29 04:13:47 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Language | English |
| License | cc-by-nd: openAccess |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c3600-c7d25b4284c02c4d6b6b95a81134275ec637ee2383864a512a28edf84ff8d7c93 |
| Notes | 326540 |
| OpenAccessLink | https://doaj.org/article/71142bcf91634ba48c9913418170472d |
| PageCount | 14 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_71142bcf91634ba48c9913418170472d hrcak_primary_oai_hrcak_srce_hr_326540 gale_infotracmisc_A822840476 gale_infotracacademiconefile_A822840476 gale_incontextgauss_ISR_A822840476 crossref_primary_10_20532_cit_2024_1005870 |
| PublicationCentury | 2000 |
| PublicationDate | 20241201 |
| PublicationDateYYYYMMDD | 2024-12-01 |
| PublicationDate_xml | – month: 12 year: 2024 text: 20241201 day: 01 |
| PublicationDecade | 2020 |
| PublicationTitle | Journal of computing and information technology |
| PublicationYear | 2024 |
| Publisher | Sveuciliste U Zagrebu Sveučilište u Zagrebu Fakultet elektrotehnike i računarstva University of Zagreb Faculty of Electrical Engineering and Computing |
| Publisher_xml | – name: Sveuciliste U Zagrebu – name: Sveučilište u Zagrebu Fakultet elektrotehnike i računarstva – name: University of Zagreb Faculty of Electrical Engineering and Computing |
| SSID | ssj0000396641 |
| Score | 2.289305 |
| Snippet | The prediction of power output from photovoltaic generation clusters is crucial for optimizing the dispatch of regional photovoltaic generation. Enhancing the... |
| SourceID | doaj hrcak gale crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database |
| StartPage | 251 |
| SubjectTerms | Algorithms Analysis Artificial intelligence cluster partitioning Distributed generation (Electric power) distributed photovoltaic Genetic algorithms multi-objective genetic algorithm Photovoltaic power generation Solar energy Solar power plants |
| Title | Improved Multiobjective Genetic Algorithm for Partitioning Distributed Photovoltaic Clusters: Balancing Spatial Distance and Power Similarity |
| URI | https://hrcak.srce.hr/326540 https://doaj.org/article/71142bcf91634ba48c9913418170472d |
| Volume | 32 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1846-3908 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000396641 issn: 1330-1136 databaseCode: DOA dateStart: 20160101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1846-3908 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000396641 issn: 1330-1136 databaseCode: M~E dateStart: 20000101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Nb9QwELVQ4cCFb8RCW1kIgYQUdZM4sc1tW1oVCaoVBdSbZU-c7pY2QUmWI_-A_8yMnVa7p164rFbJjBR7xuM3yfgNY2-mGUiLnpRUmJwkooA6Uc7pxOY2hRKmqQ_dGn58licn6uxMz9dafVFNWKQHjhO3J-mwp4MaYUwunBUKNH0sTolYTsisoug7lXotmQoxOEcYL9L4GTOj7gd7sKTSyUxQXUChqDnx2kYU-PpvovLdRQf259o-c_SIPRgBIp_FB3vM7vjmCXt43XyBj2vxKfsbXwf4iocztK27iKGLE5E06vLZ5XmLmf_iiiMu5XMa6fjylX8ktlxqdIXa80U7tBijBos6B5crIk7oP_B9KnkEEqamxeikQYlchNsGtai5Gj9dXi0xM0Yg_4x9Pzr8dnCcjL0VEsgR4yQgq6xwmHsIQHOJqnSl04VVKc5tJgsPZS69x_08V6WwiApspnxVK1HXqpKg8-dsq2kb_4JxrV0BonaaMlwrtK0lWNRCNACFc9MJe3890eZXpNAwmHoEqxi0iiGrmNEqE7ZPprgRJPbrcAF9wow-YW7ziQl7TYY0xG_RUAHNuV31vfl0-tXMEBBhTitkOWHvRqG6HToLdjyPgIMiSqwNye0NSVyAsHH7bfCXjWeOV_oOPP41iJERGb_8H2N7xe7TfMWKmm22NXQrv8Puwe9h2Xe7YRXg75c_h_8AuQULpg |
| linkProvider | Directory of Open Access Journals |
| 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=Improved+Multiobjective+Genetic+Algorithm+for+Partitioning+Distributed+Photovoltaic+Clusters%3A+Balancing+Spatial+Distance+and+Power+Similarity&rft.jtitle=Journal+of+computing+and+information+technology&rft.au=Yansen+Chen&rft.au=Kai+Cheng&rft.au=Zhuohuan+Li&rft.au=Shixian+Pan&rft.date=2024-12-01&rft.pub=University+of+Zagreb+Faculty+of+Electrical+Engineering+and+Computing&rft.eissn=1846-3908&rft.volume=32&rft.issue=4&rft.spage=251&rft.epage=264&rft_id=info:doi/10.20532%2Fcit.2024.1005870&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_71142bcf91634ba48c9913418170472d |
| thumbnail_s | http://cvtisr.summon.serialssolutions.com/2.0.0/image/custom?url=https%3A%2F%2Fhrcak.srce.hr%2Flogo_broj%2F25287.jpg |