Critical component identification under load uncertainty for cascading failure analysis

As electric power grids are increasingly growing in scale and complexity, modeling and analysis of cascading failures become more critical. In this paper, a new method is proposed for identification of critical components using global and relative indices. These indices are extracted from the data from cascading failure analysis based on distributed slack power flow under load uncertainty. The proposed model considers fast propagation of failures and avoids the problems caused by the common assumption that optimal power flow can be implemented between subsequent failures. It also alleviates the unrealistic assignment of criticality to the transmission lines that are in the vicinity of slack bus in cascading failure models based on power flow solution. The proposed model and analysis are tested on Illinois 200-bus system and results are discussed.