  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.
