Adaptive Power System Emergency Control Using Deep Reinforcement Learning
Adaptive Power System Emergency Control Using Deep Reinforcement Learning
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DOI：
10.1109/tsg.2019.2933191
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发表时间：
2019-03
影响因子：
9.6
通讯作者：
Qiuhua Huang;Renke Huang;Weituo Hao;Jie Tan;Rui Fan;Zhenyu Huang
中科院分区：
文献类型：
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作者：
Qiuhua Huang;Renke Huang;Weituo Hao;Jie Tan;Rui Fan;Zhenyu Huang
电力系统紧急控制被普遍认为是电网安全和恢复的最后一道安全网。现有的紧急控制方案通常是离线设计的基础上设想的“最坏”的情况下，或一些典型的操作场景。随着现代电网中不确定性和变化的增加，这些方案面临着显著的适应性和鲁棒性问题。为了应对这些挑战，本文利用深度强化学习（DRL）的高维特征提取和非线性泛化能力，为复杂电力系统开发了新的自适应紧急控制方案。此外，还首次设计了一个名为电网控制强化学习（RLGC）的开源平台，以协助电力系统控制DRL算法的开发和基准测试。详细介绍了该平台和基于DRL的发电机动态制动和低压减载紧急控制方案。不同的模拟场景，模型参数的不确定性和噪声的观测的DRL方法的鲁棒性进行了研究。在两个区域，四机系统和IEEE 39节点系统进行了广泛的案例研究表明，所提出的计划具有良好的性能和鲁棒性。
Power system emergency control is generally regarded as the last safety net for grid security and resiliency. Existing emergency control schemes are usually designed off-line 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.