  This paper presents a novel AI-based approach for maximizing time-series
available transfer capabilities (ATCs) via autonomous topology control
considering various practical constraints and uncertainties. Several AI
techniques including supervised learning and deep reinforcement learning (DRL)
are adopted and improved to train effective AI agents for achieving the desired
performance. First, imitation learning (IL) is used to provide a good initial
policy for the AI agent. Then, the agent is trained by DRL algorithms with a
novel guided exploration technique, which significantly improves the training
efficiency. Finally, an Early Warning (EW) mechanism is designed to help the
agent find good topology control strategies for long testing periods, which
helps the agent to determine action timing using power system domain knowledge;
thus, effectively increases the system error-tolerance and robustness.
Effectiveness of the proposed approach is demonstrated in the "2019 Learn to
Run a Power Network (L2RPN)" global competition, where the developed AI agents
can continuously and safely control a power grid to maximize ATCs without
operator's intervention for up to 1-month's operation data and eventually won
the first place in both development and final phases of the competition. The
winning agent has been open-sourced on GitHub.
