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Spatial Risk Analysis of Power Systems Resilience During Extreme Events
Author(s) -
Trakas Dimitris N.,
Panteli Mathaios,
Hatziargyriou Nikos D.,
Mancarella Pierluigi
Publication year - 2019
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/risa.13220
Subject(s) - probabilistic logic , resilience (materials science) , reliability engineering , computer science , event (particle physics) , risk analysis (engineering) , electric power system , reliability (semiconductor) , probabilistic risk assessment , vulnerability (computing) , risk assessment , data mining , real time computing , power (physics) , engineering , computer security , artificial intelligence , medicine , physics , quantum mechanics , thermodynamics
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.