Deep Reinforcement Leaming for Short-term Voltage Control by Dynamic Load Shedding in China Southem Power Grid

We propose a novel load shedding (LS) scheme against voltage instability using deep reinforcement learning (DRL). Both spatial and temporal information of a large power grid are used in the DRL control scheme. Specifically, both dynamic state variables and grid topology information are inputs to the learning controller. Within the DRL scheme, a deep learning neural network is designed and implemented to automatically extract translation-invariance information about voltage instability. The DRL load shedding controller interacts with system dynamics through a sequence of observations, actions and rewards to determine load shedding amounts in a manner that maximizes cumulative future reward, accomplishing coordination within the region rapidly to meet the online application requirements. The DRL based distributed LS scheme is performed to control the China Southern Power Grid (CSG) system. Our results show improved voltage recovery performance by load shedding using the proposed scheme under different unknown test scenarios.