  Under voltage load shedding (UVLS) for power grid emergency control builds
the last defensive perimeter to prevent cascade outages and blackouts in case
of contingencies. This letter proposes a novel cooperative multi-agent deep
reinforcement learning (MADRL)-based UVLS algorithm in an adaptive
decentralized way. With well-designed input signals reflecting the voltage
deviation, newly structured neural networks are developed as intelligent agents
to obtain control actions and their probabilities to accommodate high
uncertainties in volatile power system operations. Moreover, the interaction
among the agents for coordinated control is implemented and refined by a
state-of-the-art attention mechanism, which helps agents concentratively learn
effective interacted information. The proposed method realizes decentralized
coordinated control, adapting to extremely high uncertainties. Case studies on
an IEEE benchmark system indicate the superior performance of the proposed
algorithm.
