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Safe Reinforcement Learning for Grid Voltage Control
Thanh Long Vu · Sayak Mukherjee · Renke Huang · Qiuhua Huang

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, the 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.

Author Information

Thanh Long Vu (Pacific Northwest National Lab)
Sayak Mukherjee (Pacific Northwest National Laboratory)
Renke Huang (Pacific Northwest National Laboratory)
Qiuhua Huang (PNNL)

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