Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/93376
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electrical Engineering | en_US |
dc.creator | Zhu, Z | en_US |
dc.creator | Chan, KW | en_US |
dc.creator | Bu, S | en_US |
dc.creator | Or, SW | en_US |
dc.creator | Gao, X | en_US |
dc.creator | Xia, S | en_US |
dc.date.accessioned | 2022-06-21T08:23:16Z | - |
dc.date.available | 2022-06-21T08:23:16Z | - |
dc.identifier.issn | 0885-8950 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/93376 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication Z. Zhu, K. W. Chan, S. Bu, S. W. Or, X. Gao and S. Xia, "Analysis of Evolutionary Dynamics for Bidding Strategy Driven by Multi-Agent Reinforcement Learning," in IEEE Transactions on Power Systems, vol. 36, no. 6, pp. 5975-5978, Nov. 2021 is available at https://doi.org/10.1109/TPWRS.2021.3099693. | en_US |
dc.subject | Bidding strategy | en_US |
dc.subject | Evolutionary game theory | en_US |
dc.subject | Market power | en_US |
dc.subject | Multi-agent reinforcement learning | en_US |
dc.title | Analysis of evolutionary dynamics for bidding strategy driven by multi-agent reinforcement learning | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 5975 | en_US |
dc.identifier.epage | 5978 | en_US |
dc.identifier.volume | 36 | en_US |
dc.identifier.issue | 6 | en_US |
dc.identifier.doi | 10.1109/TPWRS.2021.3099693 | en_US |
dcterms.abstract | In this letter, the evolutionary game theory (EGT) with replication dynamic equations (RDEs) is adopted to explicitly determine the factors affecting energy providers' (EPs) willingness of using the market power to uplift the price in the bidding procedure, which could be simulated using the win-or-learn-fast policy hill climbing (WoLF-PHC) algorithm as a multi-agent reinforcement learning (MARL) method. Firstly, empirical and numerical connections between WoLF-PHC and RDEs is proved. Then, by formulating RDEs of the bidding procedure, three factors affecting the bidding strategy preference are revealed, including the load demand, severity of congestion, and the price cap. Finally, the impact of these factors on the converged bidding price is demonstrated in case studies, by simulating the bidding procedure driven by WoLF-PHC. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE transactions on power systems, Nov. 2021, v. 36, no. 6, p. 5975-5978 | en_US |
dcterms.isPartOf | IEEE transactions on power systems | en_US |
dcterms.issued | 2021-11 | - |
dc.identifier.scopus | 2-s2.0-85111609440 | - |
dc.identifier.eissn | 1558-0679 | en_US |
dc.description.validate | 202206 bchy | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | EE-0004 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Innovation and Technology Commission; Hong Kong Branch of National Rail Transit Electrification and Automation Engineering Technology Research Center; National Natural Science Foundation of China | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 54281244 | - |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Zhu_Analysis_Evolutionary_Dynamics.pdf | Pre-Published version | 1.38 MB | Adobe PDF | View/Open |
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