Deep Reinforcement Learning-Based Energy Storage Arbitrage With Accurate Lithium-Ion Battery Degradation Model
- Submitting institution
-
University of Keele
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 347
- Type
- D - Journal article
- DOI
-
10.1109/TSG.2020.2986333
- Title of journal
- IEEE Transactions on Smart Grid
- Article number
- -
- First page
- 4513
- Volume
- 11
- Issue
- 5
- ISSN
- 1949-3053
- Open access status
- Compliant
- Month of publication
- April
- Year of publication
- 2020
- URL
-
https://ieeexplore.ieee.org/document/9061038
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
5
- Research group(s)
-
-
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This novel application of machine leaning to the optimisation of energy markets proposes tool support which is currently being implemented as an industrial platform by Qbots Energy Ltd as part of the UrbanX project (Innovate UK: 106187, PI Fan). The research illustrate the scale and developing influence of the Keele Smart Energy Network Demonstrator research. Cao's contribution is funded by a Royal Society Research Grant (REF. RGS/R1/191395); Harrold is a SEND doctoral Fellow, supervised nu Cao and Fan.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -