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Register a new userQuantifying the Influence of Component Failure Probability on Cascading Blackout Risk
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Jinpeng Guo
Publication date
2017
fields
Mathematical Statistics
and research's language is
English
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The risk of cascading blackouts greatly relies on failure probabilities of individual components in power grids. To quantify how component failure probabilities (CFP) influences blackout risk (BR), this paper proposes a sample-induced semi-analytic approach to characterize the relationship between CFP and BR. To this end, we first give a generic component failure probability function (CoFPF) to describe CFP with varying parameters or forms. Then the exact relationship between BR and CoFPFs is built on the abstract Markov-sequence model of cascading outages. Leveraging a set of samples generated by blackout simulations, we further establish a sample-induced semi-analytic mapping between the unbiased estimation of BR and CoFPFs. Finally, we derive an efficient algorithm that can directly calculate the unbiased estimation of BR when the CoFPFs change. Since no additional simulations are required, the algorithm is computationally scalable and efficient. Numerical experiments well confirm the theory and the algorithm.
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Whereas maintenance has been recognized as an important and effective means for risk management in power systems, it turns out to be intractable if cascading blackout risk is considered due to the extremely high computational complexity. In this paper, based on the inference from the blackout simulation data, we propose a methodology to efficiently identify the most influential component(s) for mitigating cascading blackout risk in a large power system. To this end, we first establish an analytic relationship between maintenance strategies and blackout risk estimation by inferring from the data of cascading outage simulations. Then we formulate the component maintenance decision-making problem as a nonlinear 0-1 programming. Afterwards, we quantify the credibility of blackout risk estimation, leading to an adaptive method to determine the least required number of simulations, which servers as a crucial parameter of the optimization model. Finally, we devise two heuristic algorithms to find approximate optimal solutions to the model with very high efficiency. Numerical experiments well manifest the efficacy and high efficiency of our methodology.
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