Framework for probabilistic simulation of power transmission network performance under hurricanes
Framework for probabilistic simulation of power transmission network performance under hurricanes
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DOI：
10.1016/j.ress.2021.108072
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发表时间：
2022-01
期刊：
影响因子：
--
通讯作者：
Liyang Ma;Vasileios Christou;P. Bocchini
中科院分区：
文献类型：
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作者：
Liyang Ma;Vasileios Christou;P. Bocchini
输电网络中的结构，如塔和导体，容易受到飓风的影响。这些结构的故障会使输电线路跳闸，通常会导致该地区大规模停电。本文提出了一种技术的概率仿真下的飓风事件的电力传输系统，并提供了基本的见解建模和量化的电力系统的性能和弹性。该研究模型的电力传输系统作为一个网络连接的各个组件，这是受到风致机械故障和功率流的限制。一个现实的电力传输网络的研究区域。基于数据收集和图像处理活动获得网络中所有组件的地理数据，以反映服务于宾夕法尼亚州利哈伊谷的网络的现实属性。利用飓风模拟器生成飓风情景，为组件故障分析提供随时间变化的风强度和风向。飓风的时空影响进行了研究：一个池的组件的脆弱性，有效地将结构能力的不确定性纳入分析;结构之间的空间相关性建模有效地通过随机场为基础的技术。在系统级，采用蒙特卡罗模拟方法确定输电线路的失效概率。基于交流最优潮流分析、系统容量约束和切负荷过程，以概率方式计算系统的未满足需求。仿真结果可用于量化和可视化电网性能，并帮助决策者识别网络中的关键组件，以优化即将到来的飓风的短期事前准备和长期改造策略，以提高系统的弹性。
Structures in a power transmission network, such as towers and conductors, are vulnerable to hurricanes. Failures of these structures trip transmission lines and usually result in large scale power outages in the region. This paper presents a technique for the probabilistic simulation of power transmission systems under hurricane events and provides fundamental insights on the modeling and quantification of power system performance and resilience. The study models the power transmission system as a network of connected individual components, which are subjected to wind-induced mechanical failure and power flow constraints. A realistic power transmission network is developed for the study region. The geographical data are obtained for all components in the network based on a data collection and image processing campaign to reflect the realistic properties of the network serving the Lehigh Valley, PA. A hurricane simulator is utilized to generate a hurricane scenario providing time-varying wind intensities and wind directions for the component failure analysis. The spatio-temporal impact of the hurricane is investigated: a pool of component fragilities is generated to effectively incorporate the uncertainties in structural capacities into the analysis; the spatial correlation among structures is modeled efficiently by a random field based technique. At the system level, Monte Carlo simulation is adopted to determine the failure probability of transmission lines. The unmet demand of the system is computed probabilistically, based on the alternate current optimal power flow analysis, capacity constraints and load shedding process of the system. The simulation results can be used to quantify and visualize the power network performance, and help decision makers to identify critical components in the network to optimize the short-term pre-event preparation for an approaching hurricane and long-term retrofit strategy to enhance system resilience.