Controlled Islanding Resilience With High Penetration of Renewable Energy Resources

Abstract

Controlled islanding is a last-resort recovery scheme for preventing cascading failures. Although effective, high levels of uncertainty from renewable energy resources challenge the promise of controlled islanding to prevent additional failures. Such uncertainties can lead to subsequent failures in individual islands, render the overall recovery approach ineffective, and reduce the grid resilience. This paper develops a stochastic cascading failure model to assess the resilience of islands against cascading failures under high penetration of renewables. The stochastic model incorporates physical system constraints and system data to predict the failures. Hence, the most probable cascades in islands with high uncertainties are efficiently identified, which enables evaluating the islands' resilience to cascades. The developed model significantly improves the efficiency and accuracy of the resilience study in the presence of uncertainties. Case studies indicate that the increased penetration of intermittent renewables can improve or degrade the system resilience depending on the level of renewable generation. The case studies also reveal that an islanding strategy to minimize the imbalance of load and generation can lead to less resilient islands. These findings substantially enhance the understanding of controlled islanding under high generation uncertainties and improve resilience-oriented grid planning.