Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals

Loading...
Thumbnail Image
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2022
Major/Subject
Mcode
Degree programme
Language
en
Pages
23
54484-54506
Series
IEEE Access, Volume 10
Abstract
The transition to renewable production and smart grids is driving a massive investment to battery storages, and reinforcement learning (RL) has recently emerged as a potentially disruptive technology for their control and optimization of battery storage systems. A surge of papers has appeared in the last two years applying reinforcement learning to the optimization of battery storages in buildings, energy communities, energy harvesting Internet of Things networks, renewable generation, microgrids, electric vehicles and plug-in hybrid electric vehicles. This article reviews these applications through 4 different perspectives. Firstly, the type of optimization problem is analyzed; the literature can be divided to approaches that optimize either financial targets or energy efficiency. Secondly, the approaches for handling user comfort are analyzed for applications that may impact a human user. Thirdly, this paper discusses the approach to model and reduce battery degradation. Fourthly, the articles are categorized by application context and applications likely to attract a high amount of research are identified. The paper concludes with a list of unresolved challenges.
Description
Keywords
Battery degradation, battery storage, electric vehicle, microgrid, reinforcement learning
Other note
Citation
Subramanya , R , Sierla , S A & Vyatkin , V 2022 , ' Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals ' , IEEE Access , vol. 10 , pp. 54484-54506 . https://doi.org/10.1109/ACCESS.2022.3176446