Electric Energy Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Attiki, Greece.
School of Electrical and Electronic Engineering, University of Manchester, Manchester, Greater Manchester, UK.
Risk Anal. 2019 Jan;39(1):195-211. doi: 10.1111/risa.13220. Epub 2018 Oct 23.
The increased frequency of extreme events in recent years highlights the emerging need for the development of methods that could contribute to the mitigation of the impact of such events on critical infrastructures, as well as boost their resilience against them. This article proposes an online spatial risk analysis capable of providing an indication of the evolving risk of power systems regions subject to extreme events. A Severity Risk Index (SRI) with the support of real-time monitoring assesses the impact of the extreme events on the power system resilience, with application to the effect of windstorms on transmission networks. The index considers the spatial and temporal evolution of the extreme event, system operating conditions, and the degraded system performance during the event. SRI is based on probabilistic risk by condensing the probability and impact of possible failure scenarios while the event is spatially moving across a power system. Due to the large number of possible failures during an extreme event, a scenario generation and reduction algorithm is applied in order to reduce the computation time. SRI provides the operator with a probabilistic assessment that could lead to effective resilience-based decisions for risk mitigation. The IEEE 24-bus Reliability Test System has been used to demonstrate the effectiveness of the proposed online risk analysis, which was embedded in a sequential Monte Carlo simulation for capturing the spatiotemporal effects of extreme events and evaluating the effectiveness of the proposed method.
近年来，极端事件的频繁发生凸显了开发相关方法的迫切需求，这些方法有助于减轻此类事件对关键基础设施的影响，并提高其抵御此类事件的能力。本文提出了一种在线空间风险分析方法，能够为易受极端事件影响的电力系统区域的风险演变提供指示。利用实时监测，提出了一种严重度风险指数（SRI）来评估极端事件对电力系统弹性的影响，该方法应用于风灾对输电网络的影响。该指数考虑了极端事件的时空演变、系统运行条件以及事件期间系统性能的降级。SRI 基于概率风险，通过在极端事件空间移动时对可能的故障场景的概率和影响进行压缩。由于在极端事件期间可能会发生大量的故障，因此应用了一种场景生成和缩减算法来减少计算时间。SRI 为操作人员提供了一种概率评估，可有助于基于弹性的风险缓解决策。本文采用 IEEE 24 母线可靠性测试系统来验证所提出的在线风险分析的有效性，该分析嵌入在顺序蒙特卡罗模拟中，以捕捉极端事件的时空效应并评估所提出方法的有效性。
