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Safe Reinforcement Learning for Grid Voltage Control

arXiv 2112.01484 ↗First posted 2 Dec 2021cs.LG cs.SY eess.SYcited by 0

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Abstract (arXiv)

Under voltage load shedding has been considered as a standard approach to recover the voltage stability of the electric power grid under emergency conditions, yet this scheme usually trips a massive amount of load inefficiently. Reinforcement learning (RL) has been adopted as a promising approach to circumvent the issues; however, RL approach usually cannot guarantee the safety of the systems under control. In this paper, we discuss a couple of novel safe RL approaches, namely constrained optimization approach and Barrier function-based approach, that can safely recover voltage under emergency events. This method is general and can be applied to other safety-critical control problems. Numerical simulations on the 39-bus IEEE benchmark are performed to demonstrate the effectiveness of the proposed safe RL emergency control.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 17 references that are in the map. First posted on arXiv 2 Dec 2021.

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10 of 17 references.

References · 10

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2015Continuous control with deep reinforcement learningcited by 3,279
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  4. 2015Trust Region Policy Optimizationcited by 2,503
  5. 2019End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Taskscited by 256
  6. 2020Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier Functionscited by 61
  7. 2019Adaptive Power System Emergency Control using Deep Reinforcement Learningcited by 26
  8. 2020Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Controlcited by 6
  9. 2020Safe Reinforcement Learning for Emergency LoadShedding of Power Systemscited by 4
  10. 2021Barrier Function-based Safe Reinforcement Learning for Emergency Control of Power Systemscited by 3

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