Spatial Risk Analysis of Power Systems Resilience During Extreme Events

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 spat...

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Bibliographic Details
Published in:Risk analysis 2019-01, Vol.39 (1), p.195-211
Main Authors: Trakas, Dimitris N., Panteli, Mathaios, Hatziargyriou, Nikos D., Mancarella, Pierluigi
Format: Article
Language:English
Subjects:
Computer simulation
Extreme weather event
Internet
Mitigation
Monte Carlo simulation
Performance degradation
Power
power system
Reliability
Resilience
resiliency
Risk analysis
Risk assessment
Risk reduction
Simulation
Spatial analysis
spatial risk
Statistical analysis
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Summary: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.
ISSN:0272-4332
1539-6924
DOI:10.1111/risa.13220