Transmission expansion planning integrated with wind farms: A review, comparative study, and a novel profound search approach

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorNaderi, Ehsanen_US
dc.contributor.authorPourakbari-Kasmaei, Mahdien_US
dc.contributor.authorLehtonen, Mattien_US
dc.contributor.departmentDepartment of Electrical Engineering and Automationen
dc.contributor.groupauthorPower Systems and High Voltage Engineeringen
dc.contributor.organizationRazi Universityen_US
dc.date.accessioned2019-09-03T13:46:08Z
dc.date.available2019-09-03T13:46:08Z
dc.date.embargoinfo:eu-repo/date/embargoEnd/2021-08-08en_US
dc.date.issued2020-02-01en_US
dc.description.abstractThis paper develops a novel hybrid algorithm for solving transmission expansion planning (TEP) problems in electric power networks. Raising the awareness about immense contaminants produced by fossil fuels as well as depleting these resources have pushed energy companies toward considering more renewable energy resources (RERs). The RESs are beneficial for the society and the power system utility, however, taking into account the uncertainties, which are inherent in RERs, increase the complexity of the optimization problems. In this work, a Monte-Carlo simulation (MCS) is used to address the intermittent nature of wind energy. To handle the resulted model, by modifying and combining three well-known evolutionary algorithms such as shuffled frog leaping algorithm (SFLA), particle swarm optimization (PSO), and teaching learning-based optimization (TLBO), a potent hybrid MSFLA-MPSO-MTLBO, namely combinatorial heuristic-based profound-search algorithm (CHPSA), is proposed. A self-adaptive probabilistic mutation operator (SAPMO) is employed to enhance the effectiveness and computational efficiency of the CHPSA. Ten commonly-used benchmark problems are introduced to corroborate the performance of the CHPSA, while the IEEE RTS 24-bus test system is used to validate the model. Results show that the proposed CHPSA is capable of obtaining better solutions than other algorithms, either implemented in this paper or borrowed from the literature.en
dc.description.versionPeer revieweden
dc.format.extent22
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationNaderi, E, Pourakbari-Kasmaei, M & Lehtonen, M 2020, ' Transmission expansion planning integrated with wind farms : A review, comparative study, and a novel profound search approach ', International Journal of Electrical Power and Energy Systems, vol. 115, 105460 . https://doi.org/10.1016/j.ijepes.2019.105460en
dc.identifier.doi10.1016/j.ijepes.2019.105460en_US
dc.identifier.issn0142-0615
dc.identifier.issn1879-3517
dc.identifier.otherPURE UUID: 7656a06d-9701-4193-8f6a-9306a84dc9ffen_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/7656a06d-9701-4193-8f6a-9306a84dc9ffen_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85070216112&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/36428220/ELEC_Naderi_etal_Transmission_Expansion_Planning_IJEPES_115_acceptedauthormanuscript.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/40068
dc.identifier.urnURN:NBN:fi:aalto-201909035110
dc.language.isoenen
dc.publisherElsevier Limited
dc.relation.ispartofseriesInternational Journal of Electrical Power and Energy Systemsen
dc.relation.ispartofseriesVolume 115en
dc.rightsopenAccessen
dc.subject.keywordMonte-Carlo simulation (MCS)en_US
dc.subject.keywordRenewable energy resources (RERs)en_US
dc.subject.keywordSelf-adaptive probabilistic mutation operator (SAPMO)en_US
dc.subject.keywordTransmission expansion planning (TEP)en_US
dc.titleTransmission expansion planning integrated with wind farms: A review, comparative study, and a novel profound search approachen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
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