Spatial-Temporal Deep Learning for Hosting Capacity Analysis in Distribution Grids
Spatial-Temporal Deep Learning for Hosting Capacity Analysis in Distribution Grids
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DOI：
10.1109/tsg.2022.3196943
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发表时间：
2023-01
影响因子：
9.6
通讯作者：
Jiaqi Wu;Jingyi Yuan;Yang Weng;Raja Ayyanar
中科院分区：
文献类型：
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作者：
Jiaqi Wu;Jingyi Yuan;Yang Weng;Raja Ayyanar
分布式能源（DER）的广泛使用对电力系统的设计，规划和运行提出了重大挑战，导致托管容量分析（HCA）工具的广泛适应。传统的HCA方法进行广泛的潮流分析。由于计算负担，这些耗时的方法无法提供在线托管容量（HC）在大型分销系统。为了解决这个问题，我们首先提出了一个基于深度学习的HCA问题公式，它进行离线训练并真实的时间确定HC。所使用的学习模型，长短期记忆（LSTM），实现了历史时间序列数据，以捕获配电系统中的周期性模式。然而，直接应用LSTM由于缺乏对空间信息的考虑而遭受低精度，其中像馈线拓扑的位置信息在节点HCA中是至关重要的。因此，我们将遗忘门函数修改为双遗忘门，以捕获网格内的空间相关性。这样的设计将LSTM转变为时空LSTM（ST-LSTM）。此外，由于电压违规是HCA中最重要的约束，我们设计了一个电压灵敏度门，以进一步提高精度。LSTM和ST-LSTM的馈线，如IEEE 34，123总线馈线，和公用事业馈线的结果，验证我们的设计。
The widespread use of distributed energy sources (DERs) raises significant challenges for power system design, planning, and operation, leading to wide adaptation of tools on hosting capacity analysis (HCA). Traditional HCA methods conduct extensive power flow analysis. Due to the computation burden, these time-consuming methods fail to provide online hosting capacity (HC) in large distribution systems. To solve the problem, we first propose a deep learning-based problem formulation for HCA, which conducts offline training and determines HC in real time. The used learning model, long short-term memory (LSTM), implements historical time-series data to capture periodical patterns in distribution systems. However, directly applying LSTMs suffers from low accuracy due to the lack of consideration on spatial information, where location information like feeder topology is critical in nodal HCA. Therefore, we modify the forget gate function to dual forget gates, to capture the spatial correlation within the grid. Such a design turns the LSTM into the Spatial-Temporal LSTM (ST-LSTM). Moreover, as voltage violations are the most vital constraints in HCA, we design a voltage sensitivity gate to increase accuracy further. The results of LSTMs and ST-LSTMs on feeders, such as IEEE 34-, 123-bus feeders, and utility feeders, validate our designs.