A Critical Review of Safe Reinforcement Learning Techniques in Smart Grid Applications
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909324400394240 |
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| author | Bui, Van-Hai Das, Srijita Hussain, Akhtar Hollweg, Guilherme Vieira Su, Wencong |
| author_facet | Bui, Van-Hai Das, Srijita Hussain, Akhtar Hollweg, Guilherme Vieira Su, Wencong |
| contents | The high penetration of distributed energy resources (DERs) in modern smart power systems introduces unforeseen uncertainties for the electricity sector, leading to increased complexity and difficulty in the operation and control of power systems. As a cutting-edge machine learning technology, deep reinforcement learning (DRL) has been widely implemented in recent years to handle the uncertainty in power systems. However, in critical infrastructures such as power systems, safety issues always receive top priority, while DRL may not always meet the safety requirements of power system operators. The concept of safe reinforcement learning (safe RL) is emerging as a potential solution to overcome the shortcomings of conventional DRL in the operation and control of power systems. This study provides a rigorous review of the latest research efforts focused on safe RL to derive power system control policies while accounting for the unique safety requirements of power grids. Furthermore, this study highlights various safe RL algorithms applied in diverse applications within the power system sector, from single grid-connected power converters, residential smart homes, and buildings to large power distribution networks. For all methods outlined, a discussion on their bottlenecks, research challenges, and potential opportunities in the operation and control of power system applications is also presented. This review aims to support research in the area of safe RL algorithms, embracing smart power system operation with safety constraints amid high uncertainty from DERs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_16256 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Critical Review of Safe Reinforcement Learning Techniques in Smart Grid Applications Bui, Van-Hai Das, Srijita Hussain, Akhtar Hollweg, Guilherme Vieira Su, Wencong Systems and Control The high penetration of distributed energy resources (DERs) in modern smart power systems introduces unforeseen uncertainties for the electricity sector, leading to increased complexity and difficulty in the operation and control of power systems. As a cutting-edge machine learning technology, deep reinforcement learning (DRL) has been widely implemented in recent years to handle the uncertainty in power systems. However, in critical infrastructures such as power systems, safety issues always receive top priority, while DRL may not always meet the safety requirements of power system operators. The concept of safe reinforcement learning (safe RL) is emerging as a potential solution to overcome the shortcomings of conventional DRL in the operation and control of power systems. This study provides a rigorous review of the latest research efforts focused on safe RL to derive power system control policies while accounting for the unique safety requirements of power grids. Furthermore, this study highlights various safe RL algorithms applied in diverse applications within the power system sector, from single grid-connected power converters, residential smart homes, and buildings to large power distribution networks. For all methods outlined, a discussion on their bottlenecks, research challenges, and potential opportunities in the operation and control of power system applications is also presented. This review aims to support research in the area of safe RL algorithms, embracing smart power system operation with safety constraints amid high uncertainty from DERs. |
| title | A Critical Review of Safe Reinforcement Learning Techniques in Smart Grid Applications |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2409.16256 |