Two-Timescale Voltage Control in Distribution Grids Using Deep Reinforcement Learning
Two-Timescale Voltage Control in Distribution Grids Using Deep Reinforcement Learning
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DOI：
10.1109/tsg.2019.2951769
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发表时间：
2019-04
影响因子：
9.6
通讯作者：
Qiuling Yang;Gang Wang;A. Sadeghi;G. Giannakis;Jian Sun-
中科院分区：
文献类型：
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作者：
Qiuling Yang;Gang Wang;A. Sadeghi;G. Giannakis;Jian Sun-
现代配电网目前正面临着频繁和大规模的电压波动的挑战，这主要是由于电动汽车和可再生发电机的部署越来越多。将总线电压幅值维持在期望区域内的现有方法可以科普传统的公用事业拥有的设备（例如，并联电容器），或者与分布式发电单元一起出现的当代智能逆变器（例如，光伏电站）。电容器单元的离散开-关承诺通常按小时或每天配置，但智能逆变器可以在毫秒内控制，因此对这两种类型的资产的联合控制提出了挑战。在此背景下，一种新的双时标电压调节方案开发的配电网明智地耦合数据驱动与基于物理的优化。在更快的时间尺度上，比如每秒，智能逆变器的最佳设定点是通过基于精确的交流功率流模型或其线性近似最小化瞬时总线电压与其标称值的偏差来获得的;而在更慢的时间尺度上（例如，每小时），并联电容器被配置为使用深度强化学习算法来最小化长期折扣电压偏差。在实际47节点配电网和IEEE 123节点测试馈线上进行的大量数值试验验证了该方法的有效性。
Modern distribution grids are currently being challenged by frequent and sizable voltage fluctuations, due mainly to the increasing deployment of electric vehicles and renewable generators. Existing approaches to maintaining bus voltage magnitudes within the desired region can cope with either traditional utility-owned devices (e.g., shunt capacitors), or contemporary smart inverters that come with distributed generation units (e.g., photovoltaic plants). The discrete on-off commitment of capacitor units is often configured on an hourly or daily basis, yet smart inverters can be controlled within milliseconds, thus challenging joint control of these two types of assets. In this context, a novel two-timescale voltage regulation scheme is developed for distribution grids by judiciously coupling data-driven with physics-based optimization. On a faster timescale, say every second, the optimal setpoints of smart inverters are obtained by minimizing instantaneous bus voltage deviations from their nominal values, based on either the exact alternating current power flow model or a linear approximant of it; whereas, on the slower timescale (e.g., every hour), shunt capacitors are configured to minimize the long-term discounted voltage deviations using a deep reinforcement learning algorithm. Extensive numerical tests on a real-world 47-bus distribution network as well as the IEEE 123-bus test feeder using real data corroborate the effectiveness of the novel scheme.