The resilience of electric systems is receiving growing attention due to their increased vulnerability to infrastructure damages and widespread outages from frequent extreme climactic conditions attributed to global warming effects. Resilience evaluation methods should recognize the uncertainties and correlations in the performance variations of different types of energy resources, load characteristics, extreme events and their impacts on the grid elements. However, there is a lack of established methods and resilience metrics that are widely accepted. In this context, this paper presents the development of probabilistic extreme event model, impact assessment model, and optimal restoration model for active distribution systems, and integrates the models using a non-sequential Monte Carlo Simulation framework. The inter-dependencies of time-varying demand, renewable energy output, and energy storage characteristics are incorporated in the framework. A set of metrics is proposed to quantify the resilience of the system against extreme events and their outage impacts at the load points. The metrics and their probability distribution thus obtained can be utilized in probabilistic value-based investment planning to select appropriate measures to enhance the system resilience. Selected case studies are conducted on the IEEE 69-bus test system to show the efficacy of the proposed framework.

中文翻译：

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集成了分布式能源的配电系统的弹性评估

电力系统的弹性由于其对基础设施破坏的脆弱性增加以及由于全球变暖效应而导致的频繁极端气候条件而造成的广泛停电越来越受到关注。弹性评估方法应认识到不同类型能源的性能变化，负荷特性，极端事件及其对电网要素的影响中的不确定性和相关性。但是，缺乏已被广泛接受的既定方法和弹性指标。在这种情况下，本文介绍了主动配电系统的概率极端事件模型，影响评估模型和最佳恢复模型的开发，并使用非顺序蒙特卡洛模拟框架对模型进行了集成。框架将时变需求，可再生能源输出和能量存储特性的相互依存关系纳入其中。建议使用一组度量标准来量化系统对极端事件及其在负载点的中断影响的恢复能力。这样获得的度量及其概率分布可用于基于概率价值的投资计划中，以选择适当的度量来增强系统的弹性。在IEEE 69总线测试系统上进行了一些案例研究，以证明所提出框架的有效性。这样获得的度量及其概率分布可用于基于概率价值的投资计划中，以选择适当的度量来增强系统的弹性。在IEEE 69总线测试系统上进行了一些案例研究，以证明所提出框架的有效性。这样获得的度量及其概率分布可用于基于概率价值的投资计划中，以选择适当的度量来增强系统的弹性。在IEEE 69总线测试系统上进行了一些案例研究，以证明所提出框架的有效性。