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arXiv 1912.03344eess.SYcs.SY
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基于风险的配电系统运行弹性概率量化方法

Risk-based Probabilistic Quantification of Power Distribution System Operational Resilience

Shiva Poudel, Anamika Dubey, Anjan Bose

AI总结:

本文针对配电系统应对高影响低概率事件的运行弹性量化需求,提出基于VaR和CVaR的概率度量指标,并搭建仿真框架结合IEEE测试系统验证,可评估不同规划措施的弹性提升效果。

摘要:

如何借助恰当的加固措施和新型运行流程保障电力基础设施系统抵御极端天气事件的弹性,正受到越来越多的关注。有效的缓解策略需要一套弹性量化指标,该指标不仅能够建模复杂配电网络中未预见的灾难性事件的影响,还能评估不同规划措施带来的潜在提升效果。本文提出了用于量化配电系统应对高影响低概率(HILP)事件运行弹性的概率指标。具体而言,我们定义了两种基于风险的度量:风险价值($VaR_α$)和条件风险价值($CVaR_α$),二者分别将弹性度量为超出预设风险阈值$α$的事件对应的最大电能损失和电能损失的条件期望。随后,我们提出了一个基于仿真的框架,借助改进的IEEE 37节点和IEEE 123节点系统,针对不同天气场景评估所提出的弹性指标。该仿真方法还被拓展用于评估不同规划措施对所提出弹性指标的影响。

英文摘要:

It is of growing concern to ensure the resilience in electricity infrastructure systems to extreme weather events with the help of appropriate hardening measures and new operational procedures. An effective mitigation strategy requires a quantitative metric for resilience that can not only model the impacts of the unseen catastrophic events for complex electric power distribution networks but also evaluate the potential improvements offered by different planning measures. In this paper, we propose probabilistic metrics to quantify the operational resilience of the electric power distribution systems to high-impact low-probability (HILP) events. Specifically, we define two risk-based measures: Value-at-Risk ($VaR_α$) and Conditional Value-at-Risk ($CVaR_α$) that measure resilience as the maximum loss of energy and conditional expectation of a loss of energy, respectively for the events beyond a prespecified risk threshold, $α$. Next, we present a simulation-based framework to evaluate the proposed resilience metrics for different weather scenarios with the help of modified IEEE 37-bus and IEEE 123-bus system. The simulation approach is also extended to evaluate the impacts of different planning measures on the proposed resilience metrics.

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