Distribution Grid Modeling Using Smart Meter Data
Distribution Grid Modeling Using Smart Meter Data
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DOI：
10.1109/tpwrs.2021.3118004
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发表时间：
2021-02
影响因子：
6.6
通讯作者：
Yifei Guo;Yuxuan Yuan;Zhaoyu Wang
中科院分区：
文献类型：
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作者：
Yifei Guo;Yuxuan Yuan;Zhaoyu Wang
配电网模型的知识，包括拓扑结构和线路阻抗，是必不可少的电网监测，控制和保护。然而，这种信息往往无法获得、不完整或过时。越来越多的智能电表（SM）的部署为解决这一问题提供了独特的机会。本文提出了一个两阶段的框架，配电网建模使用SM数据。在第一阶段中，网络拓扑结构的识别是通过重建的配电网络的加权拉普拉斯矩阵。在第二阶段中，最小绝对偏差（LAD）回归模型被开发用于基于非线性（逆）潮流模型来估计单个分支的线路阻抗，其中利用导体库来缩小解空间。LAD回归模型原本是一个混合整数非线性规划，其连续松弛仍然是非凸的。因此，我们专门讨论了它的凸松弛问题，并讨论了它的精确性。然后，将修改后的回归模型嵌入自底向上的扫描算法中，以分支方式实现跨网络的识别。IEEE 13节点、37节点和69节点试验馈线的数值结果验证了所提方法的有效性。
The knowledge of distribution grid models, including topologies and line impedances, is essential for grid monitoring, control and protection. However, such information is often unavailable, incomplete or outdated. The increasing deployment of smart meters (SMs) provides a unique opportunity to tackle this issue. This paper proposes a two-stage framework for distribution grid modeling using SM data. In the first stage, the network topology is identified by reconstructing a weighted Laplacian matrix of distribution networks. In the second stage, a least absolute deviations (LAD) regression model is developed for estimating line impedance of a single branch based on the nonlinear (inverse) power flow model, wherein a conductor library is leveraged to narrow down the solution space. The LAD regression model is originally a mixed-integer nonlinear program whose continuous relaxation is still non-convex. Thus, we specially address its convex relaxation and discuss the exactness. The modified regression model is then embedded within a bottom-up sweep algorithm to achieve the identification across the network in a branch-wise manner. Numerical results on the IEEE 13-bus, 37-bus and 69-bus test feeders validate the effectiveness of the proposed methods.