An advanced Grey Wolf Optimization Algorithm and its application to planning problem in smart grids

dc.authorid ozdemir, aydogan/0000-0003-1331-2647
dc.authorid Younesi, Soheil/0000-0003-2170-857X
dc.authorid Ahmadi, Bahman/0000-0002-1745-2228
dc.authorwosid ozdemir, aydogan/A-2223-2016
dc.authorwosid Ahmadi, Bahman/GSD-7380-2022
dc.contributor.author Ceylan, Oğuzhan
dc.contributor.author Younesi, Soheil
dc.contributor.author Ceylan, Oguzhan
dc.contributor.author Ozdemir, Aydogan
dc.contributor.other Management Information Systems
dc.date.accessioned 2023-10-19T15:12:35Z
dc.date.available 2023-10-19T15:12:35Z
dc.date.issued 2022
dc.department-temp [Ahmadi, Bahman; Younesi, Soheil; Ozdemir, Aydogan] Istanbul Tech Univ, Dept Elect Engn, Istanbul, Turkey; [Ceylan, Oguzhan] Kadir Has Univ, Management & Informat Syst Dept, Istanbul, Turkey; [Ceylan, Oguzhan] Marmara Univ, Dept Elect & Elect Engn, Istanbul, Turkey en_US
dc.description.abstract Due to the complex mathematical structures of the models in engineering, heuristic methods which do not require derivative are developed. This paper improves recently developed Grey Wolf Optimization Algorithm by extending it with three new features: namely presenting a new formulation for evaluating the positions of search agents, applying mirroring distance to the variables violating the limits, and proposing a dynamic decision approach for each agent either in exploration or exploitation phases. The performance of Advanced Grey Wolf Optimization (AGWO) method is tested using several optimization test functions and compared to several heuristic algorithms. Moreover, a planning problem in smart grids is solved by considering different objective functions using 33 and 141 bus distribution test systems. From the numerical simulation results, we observe that, AGWO is able to find the best results compared to other methods from 10 and 9 out of 13 test functions for 30 and 60 variables, respectively. Similar to this, it finds best function values for 5 out of 10 fixed number of variable test functions. Also, the result of the CEC-C06 2019 benchmark functions shows that AGWO outperforms 8 for optimization problems from 10. In power distribution system planning problem, better objective function values were determined by using AGWO, resulting a better voltage profile, less losses, and less emission costs compared to solutions obtained by Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms. en_US
dc.description.sponsorship Scientific and Technological Research Council of Turkey TUBITAK [117E773] en_US
dc.description.sponsorship This research is funded as a part of 117E773 Advanced Evolutionary Computation for Smart Grid and Smart Community project under the framework of 1001 Project organized by The Scientific and Technological Research Council of Turkey TUBITAK. en_US
dc.identifier.citationcount 11
dc.identifier.doi 10.1007/s00500-022-06767-9 en_US
dc.identifier.endpage 3808 en_US
dc.identifier.issn 1432-7643
dc.identifier.issn 1433-7479
dc.identifier.issue 8 en_US
dc.identifier.scopus 2-s2.0-85123995376 en_US
dc.identifier.scopusquality Q2
dc.identifier.startpage 3789 en_US
dc.identifier.uri https://doi.org/10.1007/s00500-022-06767-9
dc.identifier.uri https://hdl.handle.net/20.500.12469/5486
dc.identifier.volume 26 en_US
dc.identifier.wos WOS:000749389800001 en_US
dc.identifier.wosquality Q2
dc.khas 20231019-WoS en_US
dc.language.iso en en_US
dc.publisher Springer en_US
dc.relation.ispartof Soft Computing en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 30
dc.subject Distributed Generation En_Us
dc.subject Distribution-Systems En_Us
dc.subject Optimal Allocation En_Us
dc.subject Optimal Placement En_Us
dc.subject Bat Algorithm En_Us
dc.subject Reanalysis En_Us
dc.subject Capacitors En_Us
dc.subject Distributed Generation
dc.subject Distribution-Systems
dc.subject Optimal Allocation
dc.subject Optimal Placement
dc.subject Optimization algorithm en_US
dc.subject Bat Algorithm
dc.subject Evolutionary computation en_US
dc.subject Reanalysis
dc.subject Smart grid applications en_US
dc.subject Capacitors
dc.subject Renewable energy integration en_US
dc.title An advanced Grey Wolf Optimization Algorithm and its application to planning problem in smart grids en_US
dc.type Article en_US
dc.wos.citedbyCount 19
dspace.entity.type Publication
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