arXiv 2008.075602020-08-17eess.SYcs.SY

电力系统自然灾害和极端事件恢复力建模与评估方法

Modeling of Natural Disasters and Extreme Events in Power System Resilience Enhancement and Evaluation Methods

Narayan Bhusal, Mukesh Gautam, Michael Abdelmalak, Mohammed Benidris

备注：: This paper has been accepted and presented at probabilistic methods applied to power systems (PMAPS 2020) conference
DOI：: 10.1109/PMAPS47429.2020.9183679

摘要：

过去十年，破坏性和新出现的威胁（例如人为攻击--网络和物理攻击;极端自然事件--飓风、地震和洪水）的频率急剧上升。这些事件的影响非常严重，包括长时间停电、主要电力系统设备（如发电厂、输配电线路和变电站）损坏以及完全停电。这些事件的准确建模是至关重要的，因为它们作为数学工具，用于评估和评价各种操作和规划投资策略，以加强电力系统对这些事件的影响。本文提供了一个全面的和严格的审查，目前的做法，在建模的极端事件，系统组件和系统响应的弹性评估和增强，这是一个非常重要的垫脚石发展的完整，准确，计算有吸引力的建模技术。本文首先回顾了现有的技术来模拟极端事件的传播，然后讨论了用于模拟这些事件对电力系统组件和系统响应的影响的方法。本文还讨论了研究差距和相关的挑战，以及现有建模方法的局限性的潜在解决方案。

英文摘要：

The frequency of disruptive and newly emerging threats (e.g. man-made attacks--cyber and physical attacks; extreme natural events--hurricanes, earthquakes, and floods) has escalated dramatically in the last decade. Impacts of these events are very severe ranging from long power outage duration, major power system equipment (e.g. power generation plants, transmission and distribution lines, and substation) destruction, and complete blackout. Accurate modeling of these events is vitally important as they serve as mathematical tools for the assessment and evaluation of various operations and planning investment strategies to harden power systems against these events. This paper provides a comprehensive and critical review of current practices in the modeling of extreme events, system components, and system response for resilience evaluation and enhancement, which is a very important stepping stone toward the development of complete, accurate, and computationally attractive modeling techniques. The paper starts with reviewing existing technologies to model the propagation of extreme events and then discusses the approaches used to model impacts of these events on power system components and system response. This paper also discusses the research gaps and associated challenges, and potential solutions to the limitations of the existing modeling approaches.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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