Safe Learning for Voltage Control (Papers Track)
Wenqi Cui (University of Washington); Jiayi Li (University of Washington); Baosen Zhang (University of Washington)
Inverter-based distributed energy resources provide the possibility for fast time-scale voltage control by quickly adjusting their reactive power. The power-electronic interfaces allow these resources to follow almost arbitrary control law, while designing these decentralized controllers is nontrivial. Reinforcement learning (RL) approaches are becoming increasingly popular to search for policy parameterized by neural networks. But it is difficult to enforce that the learned controllers stabilize the system. This paper proposes a safe learning approach for voltage control with stability guarantees. Using Lyapunov stability theory, we explicitly derive the structure of neural network-based controllers such that they guarantee system stability by design. A decentralized RL framework is constructed to train local neural network controller at each bus in a model-free setting.