Resilience based optimization for western US transmission grid against cascading failures

TL;DR

This paper introduces a framework for optimizing restoration strategies in the western US power grid to enhance resilience against cascading failures, aiming to reduce failure extent and improve system robustness.

Contribution

It presents a novel optimization framework for system restoration that accounts for system dependencies and resilience loss during cascading failures.

Findings

Restoration strategies can significantly reduce cascading failure severity.

System dependency influences the effectiveness of restoration actions.

The framework improves resilience in the western US transmission grid case study.

Abstract

Real-world network systems, for example, power grids, are critical to modern economies. Due to the increasing system scale and complex dependencies inside of these networks, system failures can widely spread and cause severe damage. We have experienced massive cascading failures in power grids, such as major U.S. western grid failures in 1996 and the great Northeast blackout of 2003. Therefore, analyzing cascading failures and defense strategies, in response to system catastrophic breakdown, is crucial. Although many efforts have been performed to prevent failure propagation throughout systems, optimal system restoration considering system dependency against cascading failures is rarely studied. In this paper, we present a framework to optimize restoration strategies to improve system resiliency regarding cascading failures. The effects of restoration strategies are evaluated by system resilience loss during the cascading process. Furthermore, how system dependency influences the effects of the system restoration actions against cascading failures are investigated. By performing a case study on the western U.S. transmission grid, we demonstrate that our framework of system restoration optimization can enhance system resiliency by reducing the intensity and extent of cascading failures. Our proposed framework provides insights regarding optimal system restoration from cascading failures to enhance the resiliency of real-life network systems.