Abstract
The degradation property of an energy storage system (ESS) has a decisive impact on the economic benefits of ESS operation. However, existing degradation models either do not fully reflect the effects of battery usage or require a full operation history of the battery. This study proposes an accurate Markovian model for battery degradation that reflects battery usage and requires only the current state. We demonstrate the use of the model by establishing a Markov decision process based on the proposed Markovian degradation model and solving it with deep reinforcement learning algorithms. Our intelligent agent, which aims to minimize the sum of electricity cost and degradation cost, generates cost savings of 5%–29% compared to baseline strategies. This framework offers an optimal ESS operation strategy considering battery degradation in a tractable and practical way.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.