Power system emergency control is generally regarded as the last safety net for grid security and resiliency. Existing emergency control schemes are usually designed offline based on either the conceived “worst” case scenario or a few typical operation scenarios. These schemes are facing significant adaptiveness and robustness issues as increasing uncertainties and variations occur in modern electrical grids. To address these challenges, this paper developed novel adaptive emergency control schemes using deep reinforcement learning (DRL) by leveraging the high-dimensional feature extraction and non-linear generalization capabilities of DRL for complex power systems. Furthermore, an open-source platform named Reinforcement Learning for Grid Control (RLGC) has been designed for the first time to assist the development and benchmarking of DRL algorithms for power system control. Details of the platform and DRL-based emergency control schemes for generator dynamic braking and under-voltage load shedding are presented. Robustness of the developed DRL method to different simulation scenarios, model parameter uncertainty and noise in the observations is investigated. Extensive case studies performed in both the two-area, four-machine system and the IEEE 39-bus system have demonstrated excellent performance and robustness of the proposed schemes.

中文翻译：

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基于深度强化学习的自适应电力系统应急控制

电力系统应急控制通常被认为是电网安全和弹性的最后一个安全网。现有的应急控制方案通常是根据设想的“最坏”情况方案或一些典型的操作方案脱机设计的。随着现代电网中越来越多的不确定性和变化，这些方案面临着重大的适应性和鲁棒性问题。为了解决这些挑战，本文通过利用DRL的高维特征提取和复杂电力系统的非线性泛化能力，利用深度强化学习（DRL）开发了新颖的自适应应急控制方案。此外，首次设计了名为“网格控制强化学习”（RLGC）的开源平台，以协助开发和基准测试用于电力系统控制的DRL算法。介绍了发电机动态制动和欠压减载的平台和基于DRL的应急控制方案的详细信息。研究了已开发的DRL方法在不同模拟情况下的稳健性，模型参数不确定性和观测中的噪声。在两个区域，四个机器的系统和IEEE 39总线系统中进行的大量案例研究表明，所提出的方案具有出色的性能和鲁棒性。研究了已开发的DRL方法在不同模拟情况下的稳健性，模型参数不确定性和观测中的噪声。在两个区域，四个机器的系统和IEEE 39总线系统中进行的大量案例研究表明，所提出的方案具有出色的性能和鲁棒性。研究了已开发的DRL方法在不同模拟情况下的稳健性，模型参数不确定性和观测中的噪声。在两个区域，四个机器的系统和IEEE 39总线系统中进行的大量案例研究表明，所提出的方案具有出色的性能和鲁棒性。