Bottlenecks Identification and Resilience Improvement of Power Networks in Extreme Events

Abstract

Extreme events are always accompanied with extensive failures and sharp performance degradation in the power network. This study aims to derive an effective scheme to identify the transmission bottlenecks and improve the power network’s resilience under extreme events. A greedy search scheme is designed for the quick and slow restoration stage to obtain the largest power supply (LPS), which is a significant engineering indicator of the power network. In the quick restoration stage, we use interior point optimization to adjust the operating parameters of undamaged components and maximize the LPS with limited resources. It is worth pointing out that the LPS cannot be further improved, even by increasing the capacities of most transmission links. This phenomenon is due to the existence of transmission bottlenecks, which operate at their capacity limits. Thus, in the slow restoration stage, we identify these transmission bottlenecks and further improve the LPS by expanding the capacities of these links. Case studies show that the proposed greedy search scheme can not only greatly improve the LPS available to the post-disaster network but can also accurately identify the transmission bottlenecks. This work provides practical insights for building resilient infrastructures, although the power network is the object of study.

Bottleneck identification and transfer prediction for automated production lines based on FNN

“…Constraint (4) denotes that one machine can only process one job at most. Constraint (5) represents that the job is processed at most once in one process in the same stage. Constraint (6) ensures that the job needs to be processed once in each process in the first stage.…”

“…One of the primary issues within production systems is the identification and resolution of bottlenecks. Focusing on improving bottleneck performance is pivotal for optimizing production systems, and accurately pinpointing production bottlenecks within the workshop is crucial for effective production management [4][5][6][7] .…”