Multi-phase assessment and adaptation of power systems resilience to natural hazards
Multi-phase assessment and adaptation of power systems resilience to natural hazards
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DOI：
10.1016/j.epsr.2016.03.019
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发表时间：
2016-07-01
影响因子：
3.9
通讯作者：
Rudnick, Hugh
中科院分区：
文献类型：
--
作者：
Espinoza, Sebastian;Panteli, Mathaios;Rudnick, Hugh
极端天气灾害作为高影响低概率事件，对关键基础设施产生灾难性后果。作为气候变化的直接影响，其中一些事件的频率和严重程度预计在未来会增加，这突出表明有必要评估其影响，并调查系统如何能够承受重大破坏，同时减少退化并迅速恢复。本文首先提出了一个多阶段的弹性评估框架，可用于分析任何自然威胁，可能有一个严重的单一，多重和/或持续的影响，对关键基础设施，如电力系统。即，这些阶段是（i）威胁特征描述，（ii）系统组件的脆弱性评估，（iii）系统的反应和（iv）系统的恢复。其次，多阶段的适应情况下，即使系统更强大，冗余和响应解释讨论不同的策略，以提高电力网络的弹性。为了说明上述情况，这个时间依赖性的框架被应用于评估潜在的未来风暴和洪水对英国的电力网络的简化版本的影响。最后，对适应案例进行评估，以得出结论，在不确定的未来中，在什么情况下更强大、更大或更智能的电网是首选。(C)2016爱思唯尔B. V.保留所有权利。
Extreme weather hazards, as high-impact low-probability events, have catastrophic consequences on critical infrastructures. As a direct impact of climate change, the frequency and severity of some of these events is expected to increase in the future, which highlights the necessity of evaluating their impact and investigating how can systems withstand a major disruption with limited degradation and recover rapidly. This paper first presents a multi-phase resilience assessment framework that can be used to analyze any natural threat that may have a severe single, multiple and/or continuous impact on critical infrastructures, such as electric power systems. Namely, these phases are (i) threat characterization, (ii) vulnerability assessment of the system's components, (iii) system's reaction and (iv) system's restoration. Second, multi-phase adaptation cases, i.e. making the system more robust, redundant and responsive are explained to discuss different strategies to enhance the resilience of the electricity network. To illustrate the above, this time-dependent framework is applied to assess the impact of potential future windstorms and floods on a reduced version of the Great Britain's power network. Finally, the adaptation cases are evaluated to conclude in what situations a stronger, bigger or smarter grid is preferred against the uncertain future. (C) 2016 Elsevier B.V. All rights reserved.