  Timely and effective load shedding in power systems is critical for
maintaining supply-demand balance and preventing cascading blackouts. To
eliminate load shedding bias against specific regions in the system,
optimization-based methods are uniquely positioned to help balance between
economical and equity considerations. However, the resulting optimization
problem involves complex constraints, which can be time-consuming to solve and
thus cannot meet the real-time requirements of load shedding. To tackle this
challenge, in this paper we present an efficient machine learning algorithm to
enable millisecond-level computation for the optimization-based load shedding
problem. Numerical studies on both a 3-bus toy example and a realistic RTS-GMLC
system have demonstrated the validity and efficiency of the proposed algorithm
for delivering equitable and real-time load shedding decisions.
