Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9777914/ |
| _version_ | 1818006166892969984 |
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| author | Rakshith Subramanya Seppo A. Sierla Valeriy Vyatkin |
| author_facet | Rakshith Subramanya Seppo A. Sierla Valeriy Vyatkin |
| author_sort | Rakshith Subramanya |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T04:57:41Z |
| format | Article |
| id | doaj.art-2085ef35d6c24384a54f6a2713f45149 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-14T04:57:41Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2085ef35d6c24384a54f6a2713f451492022-12-22T02:11:07ZengIEEEIEEE Access2169-35362022-01-0110544845450610.1109/ACCESS.2022.31764469777914Exploiting Battery Storages With Reinforcement Learning: A Review for Energy ProfessionalsRakshith Subramanya0https://orcid.org/0000-0003-0651-5593Seppo A. Sierla1https://orcid.org/0000-0002-0402-315XValeriy Vyatkin2https://orcid.org/0000-0002-9315-9920Department of Electrical Engineering and Automation, Aalto University, Espoo, FinlandDepartment of Electrical Engineering and Automation, Aalto University, Espoo, FinlandDepartment of Electrical Engineering and Automation, Aalto University, Espoo, FinlandThe 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.https://ieeexplore.ieee.org/document/9777914/Battery degradationbattery storageelectric vehiclemicrogridreinforcement learning |
| spellingShingle | Rakshith Subramanya Seppo A. Sierla Valeriy Vyatkin Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals IEEE Access Battery degradation battery storage electric vehicle microgrid reinforcement learning |
| title | Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals |
| title_full | Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals |
| title_fullStr | Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals |
| title_full_unstemmed | Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals |
| title_short | Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals |
| title_sort | exploiting battery storages with reinforcement learning a review for energy professionals |
| topic | Battery degradation battery storage electric vehicle microgrid reinforcement learning |
| url | https://ieeexplore.ieee.org/document/9777914/ |
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