  Modern power grids are experiencing grand challenges caused by the stochastic
and dynamic nature of growing renewable energy and demand response. Traditional
theoretical assumptions and operational rules may be violated, which are
difficult to be adapted by existing control systems due to the lack of
computational power and accurate grid models for use in real time, leading to
growing concerns in the secure and economic operation of the power grid.
Existing operational control actions are typically determined offline, which
are less optimized. This paper presents a novel paradigm, Grid Mind, for
autonomous grid operational controls using deep reinforcement learning. The
proposed AI agent for voltage control can learn its control policy through
interactions with massive offline simulations, and adapts its behavior to new
changes including not only load/generation variations but also topological
changes. A properly trained agent is tested on the IEEE 14-bus system with tens
of thousands of scenarios, and promising performance is demonstrated in
applying autonomous voltage controls for secure grid operation.

Systems and Control;Machine Learning