Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/79690
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dc.contributorDepartment of Electrical Engineering-
dc.creatorZhang, XS-
dc.creatorYu, T-
dc.creatorZhang, ZY-
dc.creatorTang, JL-
dc.date.accessioned2018-12-21T07:13:05Z-
dc.date.available2018-12-21T07:13:05Z-
dc.identifier.issn2169-3536en_US
dc.identifier.urihttp://hdl.handle.net/10397/79690-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.en_US
dc.rightsPosted with permission of the publisher.en_US
dc.rightsThe following publication Zhang, X. S., Yu, T., Zhang, Z. Y., & Tang, J. L.(2018). Multi-agent bargaining learning for distributed energy hub economic dispatch. IEEE Access, 6, 39564-39573 is available at https://dx.doi.org/10.1109/ACCESS.2018.2853263en_US
dc.subjectMulti-agent bargaining learningen_US
dc.subjectDistributed energy hub economic dispatchen_US
dc.subjectMultiple energy carrier systemsen_US
dc.subjectKnowledge learningen_US
dc.subjectBargaining gameen_US
dc.titleMulti-agent bargaining learning for distributed energy hub economic dispatchen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage39564en_US
dc.identifier.epage39573en_US
dc.identifier.volume6en_US
dc.identifier.doi10.1109/ACCESS.2018.2853263en_US
dcterms.abstractThis paper proposes a novel multi-agent bargaining learning (MABL) for the distributed energy hub economic dispatch (EHED) of multiple energy carrier systems (MECS). Distributed EHED is developed by extending the conventional economic dispatch (ED) into MECS in a distributed manner, in which each energy hub is regarded as a learning agent for self-scheduling. The classical Q-learning with associative memory is employed for knowledge learning of each agent, while the non-uniform mutation operator is adopted for handling the continuous control variables. To maximize the total payoff of all the energy hubs, the bargaining game is presented for achieving an effective coordination between the buyer agents and a seller agent, where the slack energy hub is designed as the seller agent and the others are the buyer agents. MABL has been thoroughly evaluated for the distributed EHED on a high-complex 39-hub MECS with 29 energy hub structures and 76 energy production units. Case studies verify the superior performance of MABL for the distributed EHED compared with six centralized heuristic optimization algorithms.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE access, 2018, v. 6, p. 39564-39573-
dcterms.isPartOfIEEE access-
dcterms.issued2018-
dc.identifier.isiWOS:000441198100001-
dc.description.validate201812 bcrcen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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