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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...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
Pembroke College
Role:
Author
ORCID:
0000-0003-3174-0362
More by this author
Role:
Author
ORCID:
0000-0003-2781-9588
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
Language:
English
Keywords:
Pubs id:
1249984
Local pid:
pubs:1249984
Deposit date:
2022-04-11

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