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.

本文提出了一种新的范式Grid Mind，使用深度强化学习进行自主电网运行控制。 通过与大规模离线模拟的交互，所提出的人工智能代理可以学习其控制策略，并适应包括负荷/发电变化以及拓扑变化在内的新变化，然后在IEEE 14总线系统上进行了测试，并展示了在运用自主电压控制进行安全电网运行方面的良好表现。

使用深度强化学习进行电网运行的自主电压控制