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Power System Resilience to Extreme Weather: Fragility Modelling, Probabilistic Impact Assessment, and Adaptation Measures

机译:电力系统对极端天气的适应能力:脆弱性建模,概率影响评估和适应措施

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摘要

Historical electrical disturbances highlight the impact of extreme weather on power system resilience. Even though the occurrence of such events is rare, the severity of their potential impact calls for (i) developing suitable resilience assessment techniques to capture their impacts and (ii) assessing relevant strategies to mitigate them. This paper aims to provide fundamentals insights on the modelling and quantification of power systems resilience. Specifically, a fragility model of individual components and then of the whole transmission system is built for mapping the real-time impact of severe weather, with focus on wind events, on their failure probabilities. A probabilistic multi-temporal and multi-regional resilience assessment methodology, based on optimal power flow and sequential Monte Carlo simulation, is then introduced, allowing the assessment of the spatiotemporal impact of a windstorm moving across a transmission network. Different risk-based resilience enhancement (or “adaptation”) measures are evaluated, which are driven by the resilience achievement worth (RAW) index of the individual transmission components. The methodology is demonstrated using a test version of the Great Britain’s system. As key outputs, the results demonstrate how, by using a mix of infrastructure and operational indices, it is possible to effectively quantify system resilience to extreme weather, identify and prioritize critical network sections, whose criticality depends on the weather intensity, and assess the technical benefits of different adaptation measures to enhance resilience.
机译:历史上的电气干扰突显了极端天气对电力系统弹性的影响。尽管此类事件很少发生,但其潜在影响的严重性要求(i)开发适当的弹性评估技术以捕获其影响,以及(ii)评估相关策略以减轻影响。本文旨在提供有关电力系统弹性建模和量化的基础知识。具体而言,构建各个组件的脆弱性模型,然后构建整个传输系统的脆弱性模型,以针对恶劣天气的实时影响(以风事件为重点)及其故障概率进行映射。然后,引入了基于最佳潮流和顺序蒙特卡洛模拟的概率多时间和多区域弹性评估方法,从而可以评估暴风雨在传输网络中传播的时空影响。评估了不同的基于风险的弹性增强(或“适应”)措施,这些措施受各个传动组件的弹性成就价值(RAW)指数的驱动。使用英国系统的测试版本演示了该方法。作为主要产出,结果证明了如何通过结合使用基础架构和运营指标,来有效地量化系统对极端天气的适应能力,确定和确定关键网络部分的优先级,其关键程度取决于天气强度,并评估技术各种适应措施增强弹性的好处。

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