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DC Field | Value | Language |
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dc.contributor.author | He, Y | - |
dc.contributor.author | Guo, S | - |
dc.contributor.author | Wang, Y | - |
dc.contributor.author | Zhao, Y | - |
dc.contributor.author | Zhu, W | - |
dc.contributor.author | Xu, F | - |
dc.contributor.author | Lai, CS | - |
dc.contributor.author | Zobaa, AF | - |
dc.date.accessioned | 2023-01-20T16:17:49Z | - |
dc.date.available | 2023-01-20T16:17:49Z | - |
dc.date.issued | 2022-12-30 | - |
dc.identifier | ORCID iDs: Chun Sing Lai https://orcid.org/0000-0002-4169-4438; Ahmed Zobaa https://orcid.org/0000-0001-5398-2384 | - |
dc.identifier.citation | He, Y. et al. (2023) 'An agent-based bidding simulation framework to recognize monopoly behavior in power markets', Energies, 16 (1), 434, pp. 1 - 19. doi: 10.3390/en16010434. | en_US |
dc.identifier.other | 434 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/25834 | - |
dc.description | Data Availability Statement: The data presented in this study are available upon request from the corresponding author. | en_US |
dc.description.abstract | Copyright © 2022 by the authors. Although many countries prefer deregulated power markets as a means of containing power costs, a monopoly may still exist. In this study, an agent-based bidding simulation framework is proposed to detect whether there will be a monopoly in the power market. A security-constrained unit commitment (SCUC) is conducted to clear the power market. Using the characteristics that the agent can fully explore in a certain environment and the Q-learning algorithm, each power producer in the power market is modeled as an agent, and the agent selects a quotation strategy that can improve profits based on historical bidding information. The numerical results show that in a power market with monopoly potential among the power producers, the profits of the power producers will not converge, and the locational marginal price will eventually become unacceptable. Whereas, in a power market without monopoly potential, power producers will maintain competition and the market remains active and healthy. | en_US |
dc.description.sponsorship | Guangdong Basic and Applied Basic Research Foundation (2021A1515010742, 2020A1515011160). | en_US |
dc.format.extent | 1 - 19 | - |
dc.format.medium | Electronic | - |
dc.language.iso | en_US | en_US |
dc.publisher | MDPI AG | en_US |
dc.rights | Copyright © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | security-constrained unit commitment | en_US |
dc.subject | power market | en_US |
dc.subject | locational marginal price | en_US |
dc.subject | monopoly | en_US |
dc.subject | Q-learning | en_US |
dc.title | An agent-based bidding simulation framework to recognize monopoly behavior in power markets | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.3390/en16010434 | - |
dc.relation.isPartOf | Energies | - |
pubs.issue | 1 | - |
pubs.publication-status | Published online | - |
pubs.volume | 16 | - |
dc.identifier.eissn | 1996-1073 | - |
dc.rights.holder | The authors | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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FullText.pdf | Copyright © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | 3.53 MB | Adobe PDF | View/Open |
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