Journal article
Scalable multi-agent reinforcement learning for distributed control of residential energy flexibility
- Abstract:
-
This paper proposes a novel scalable type of multi-agent reinforcement learning-based coordination for distributed residential energy. Cooperating agents learn to control the flexibility offered by electric vehicles, space heating and flexible loads in a partially observable stochastic environment. In the standard independent Q-learning approach, the coordination performance of agents under partial observability drops at scale in stochastic environments. Here, the novel combination of learnin...
Expand abstract
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Version of record, pdf, 1006.2KB)
-
- Publisher copy:
- 10.1016/j.apenergy.2022.118825
Authors
Bibliographic Details
- Publisher:
- Elsevier Publisher's website
- Journal:
- Applied Energy Journal website
- Volume:
- 314
- Article number:
- 118825
- Publication date:
- 2022-03-22
- Acceptance date:
- 2022-02-22
- DOI:
- EISSN:
-
1872-9118
- ISSN:
-
0306-2619
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1249984
- Local pid:
- pubs:1249984
- Deposit date:
- 2022-04-11
Terms of use
- Copyright holder:
- Charbonnier et al.
- Copyright date:
- 2022
- Rights statement:
- © 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
- Licence:
- CC Attribution (CC BY)
Metrics
If you are the owner of this record, you can report an update to it here: Report update to this record