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This paper presents a novel AI-based approach for maximizing time-seriesavailable transfer capabilities (ATCs) via autonomous topology controlconsidering various practical constraints and uncertainties. Several AItechniques including supervised learning and deep reinforcement learning (DRL)are adopted and improved to train effective AI agents for achieving the desiredperformance. First, imitation learning (IL) is used to provide a good initialpolicy for the AI agent. Then, the agent is trained by DRL algorithms with anovel guided exploration technique, which significantly improves the trainingefficiency. Finally, an Early Warning (EW) mechanism is designed to help theagent find good topology control strategies for long testing periods, whichhelps 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 toRun a Power Network (L2RPN)" global competition, where the developed AI agentscan continuously and safely control a power grid to maximize ATCs withoutoperator's intervention for up to 1-month's operation data and eventually wonthe first place in both development and final phases of the competition. Thewinning agent has been open-sourced on GitHub.

AI-Based Autonomous Line Flow Control via Topology Adjustment for Maximizing Time-Series ATCs