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http://bura.brunel.ac.uk/handle/2438/24227Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Wei, X | - |
| dc.contributor.author | Xiang, Y | - |
| dc.contributor.author | Li, J | - |
| dc.contributor.author | Zhang, X | - |
| dc.date.accessioned | 2022-03-11T07:35:06Z | - |
| dc.date.available | 2022-03-11T07:35:06Z | - |
| dc.date.issued | 2022-03-07 | - |
| dc.identifier.citation | Wei, X., Xiang, Y., Li, J. and Zhang, X. (2022) 'Self-Dispatch of Wind-Storage Integrated System: A Deep Reinforcement Learning Approach', IEEE Transactions on Sustainable Energy 13 (3), pp. 1861 - 1864. doi: 10.1109/tste.2022.3156426. | en_US |
| dc.identifier.issn | 1949-3029 | - |
| dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/24227 | - |
| dc.identifier.uri | ORCID iDs: Xiangyu Wei https://orcid.org/0000-0003-1436-9303; Yue Xiang https://orcid.org/0000-0001-8456-1195; Xin Zhang https://orcid.org/0000-0002-6063-959X. | - |
| dc.description.sponsorship | 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 52177103 and U2166211). | en_US |
| dc.format.extent | 1 - 4 (4) | - |
| dc.language.iso | en_US | en_US |
| dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
| dc.rights | Copyright © 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. | - |
| dc.rights.uri | https://www.ieee.org/publications/rights/rights-policies.html | - |
| dc.subject | wind farm | en_US |
| dc.subject | energy storage system | en_US |
| dc.subject | electricity market | en_US |
| dc.subject | deep reinforcement learning | en_US |
| dc.subject | distributed prioritized experience replay | en_US |
| dc.subject | maximum entropy | en_US |
| dc.title | Self-Dispatch of Wind-Storage Integrated System: A Deep Reinforcement Learning Approach | en_US |
| dc.type | Article | en_US |
| dc.identifier.doi | https://doi.org/10.1109/tste.2022.3156426 | - |
| dc.relation.isPartOf | IEEE Transactions on Sustainable Energy | - |
| pubs.publication-status | Published | - |
| pubs.volume | 0 | - |
| dc.identifier.eissn | 1949-3037 | - |
| Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| FullText.pdf | Copyright © 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. | 989.33 kB | Adobe PDF | View/Open |
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