Safe Reinforcement Learning for Grid Voltage Control

• URL: http://arxiv.org/abs/2112.01484v1
• Date: Thu, 2 Dec 2021 18:34:50 GMT
• Title: Safe Reinforcement Learning for Grid Voltage Control
• Authors: Thanh Long Vu, Sayak Mukherjee, Renke Huang, Qiuhua Huang
• Abstract summary: Under voltage load shedding has been considered as a standard approach to recover the voltage stability of the electric power grid under emergency conditions. In this paper, we discuss a couple of novel safe RL approaches, namely constrained optimization approach and Barrier function-based approach.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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