A Game Theoretic Optimization Framework for Home Demand Management Incorporating Local Energy Resources
- Submitting institution
-
University of Keele
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 339
- Type
- D - Journal article
- DOI
-
10.1109/TII.2015.2390035
- Title of journal
- IEEE Transactions on Industrial Informatics
- Article number
- -
- First page
- 353
- Volume
- 11
- Issue
- 2
- ISSN
- 1551-3203
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- Year of publication
- 2015
- URL
-
https://ieeexplore.ieee.org/document/7004832
- 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
-
3
- Research group(s)
-
-
- Citation count
- 52
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- One of the first applications of game theory to smart grid demand management, the paper provides new insights into home energy demand optimization, which have been widely used (e.g. https://doi.org/fgdf, https://doi.org/gc24h4). The research was supported by EPSRC CASE Award (Toshiba/Loughborough, 2011-14), and the findings underpinned the subsequent EU project, SUNSEED (2015-17: see https://sunseed-fp7.eu/), for which Fan was Toshiba PI. The work has been further developed by Fan et al, as a mean field game theoretic approach to EV charging (https://doi.org/fgdg).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -