Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/79690
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electrical Engineering | - |
dc.creator | Zhang, XS | - |
dc.creator | Yu, T | - |
dc.creator | Zhang, ZY | - |
dc.creator | Tang, JL | - |
dc.date.accessioned | 2018-12-21T07:13:05Z | - |
dc.date.available | 2018-12-21T07:13:05Z | - |
dc.identifier.issn | 2169-3536 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/79690 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_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.rights | Posted with permission of the publisher. | en_US |
dc.rights | The 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.2018.2853263 | en_US |
dc.subject | Multi-agent bargaining learning | en_US |
dc.subject | Distributed energy hub economic dispatch | en_US |
dc.subject | Multiple energy carrier systems | en_US |
dc.subject | Knowledge learning | en_US |
dc.subject | Bargaining game | en_US |
dc.title | Multi-agent bargaining learning for distributed energy hub economic dispatch | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 39564 | en_US |
dc.identifier.epage | 39573 | en_US |
dc.identifier.volume | 6 | en_US |
dc.identifier.doi | 10.1109/ACCESS.2018.2853263 | en_US |
dcterms.abstract | This 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.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE access, 2018, v. 6, p. 39564-39573 | - |
dcterms.isPartOf | IEEE access | - |
dcterms.issued | 2018 | - |
dc.identifier.isi | WOS:000441198100001 | - |
dc.description.validate | 201812 bcrc | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Zhang_Multi-agent_Bargaining_Learning.pdf | 2.96 MB | Adobe PDF | View/Open |
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