Journal article
Deep Reinforcement Learning Based Energy Storage Arbitrage With Accurate Lithium-ion Battery Degradation Model
- Abstract:
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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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- Files:
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(Accepted manuscript, 2.7MB)
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- Publisher copy:
- 10.1109/tsg.2020.2986333
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Authors
Funding
Bibliographic Details
- 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:
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1949-3053
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1100724
- Local pid:
- pubs:1100724
- Deposit date:
- 2020-04-22
Terms of use
- Copyright holder:
- Institute of Electrical and Electronics Engineers
- Copyright date:
- 2020
- Rights statement:
- © 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission
- Notes:
- This is the accepted manuscript version of the article. The publisher's version is available online
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