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Deep Reinforcement Learning Based Energy Storage Arbitrage With Accurate Lithium-ion Battery Degradation Model

Abstract:

Accurate estimation of battery degradation cost is one of the main barriers for battery participating on the energy arbitrage market. This paper addresses this problem by using a model-free deep reinforcement learning (DRL) method to optimize the battery energy arbitrage considering an accurate battery degradation model. Firstly, the control problem is formulated as a Markov Decision Process (MDP). Then a noisy network based deep reinforcement learning approach is proposed to learn an optimiz...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/tsg.2020.2986333

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0003-2781-9588
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Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Journal:
IEEE Transactions on Smart Grid Journal website
Volume:
14
Issue:
8
Publication date:
2020-04-08
Acceptance date:
2020-04-03
DOI:
EISSN:
1949-3061
ISSN:
1949-3053
Language:
English
Keywords:
Pubs id:
1100724
Local pid:
pubs:1100724
Deposit date:
2020-04-22

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