In this article, we investigate the local and nonlocal failure propagation patterns in power systems and propose effective mitigation strategies. It is widely acknowledged that the sequence of failure events of component overloading is determined by power flow redistribution. To understand the power flow redistribution process, we analyze the prefault power flow condition using network flow theory and by clustering the system into several regions with balanced local power flow. We observe that faults triggering local power imbalance would lead to remote power transfer and faults causing no local power imbalance would lead to local power redistribution. The improved understanding of the failure process permits us to develop effective mitigating strategies consisting of adaptive power balance restoration and selective edge protection for cascading failure propagation. In addition, we propose a set of indexes to measure the local and nonlocal propagation risks of cascading failures. Numerical simulations on the IEEE 118- and 300-bus systems demonstrate that the proposed mitigation strategies can significantly reduce the blackout sizes and are tolerant to incorrect transmission line tripping.

This paper studies cascading failure propagation in power systems and presents methods for the quantification of important properties of failure propagation. First, the topological properties of cascading failure propagation are examined. This includes an analysis of the electrical distance between consecutive failures, shedding light on the spatial spread of failures. Additionally, the formation of islands in cascading failure processes is explored to understand their topological characteristics. Second, the paper measures the evolution of a power system during cascading failure processes, considering both the topological changes of the overall system and the propagation rates of system loss. This analysis provides a measurable comprehension of how the system evolves and adapts as failures propagate. Third, this study investigates system loss and analyzes the contributions of various failure mechanisms to the overall system loss. Numerical experiments yield valuable insights into the propagation of cascading failures, leading to several significant conclusions. The findings from this research can inform the development of effective strategies for resilience enhancement and risk mitigation.

In this paper, we investigate the influence of the locations of inverter-based resources (IBRs) on the synchronization performance of power networks. We propose two indexes to measure the distribution of inertia in power networks, considering the distribution of control parameters and the topological factors jointly. The first index is the inertia clustering coefficient, which measures how densely the neighbors of each node are connected in a power network. The second index is the inertia centrality coefficient, which captures whether the distribution of inertia in a power network is centralized or peripheral. We characterize synchronous generators (SGs), grid-following inverters (GFLs), and grid-forming inverters (GFMs) by their damping and inertial properties. We evaluate the synchronization performance of the system after disturbances by adopting the settling time and hertz-sec metric. Both the frequency response and the trajectories of eigenvalues show that the location of IBRs has a significant impact on the synchronization performance of the system. Monte Carlo simulations are conducted on two test networks, the results of the IEEE 30-, 57-, and 118-bus systems demonstrate a strong correlation between synchronization performance and the two inertia distribution indexes.

In this study, we evaluate the impact of frequency control and the penetration of inverter-based resources (IBRs) on cascading failures in power systems. The modeling of controllers for IBRs requires obtaining realistic state information during cascading failure processes. However, cascading failure events can alter system topologies and the power flow Jacobian, leading to unsolvable power flow even when an equilibrium point exists. To address this, we propose a model that tracks the steady-state voltage profiles across successive cascade generations. This model enables the incorporation of droop controllers for IBRs into the cascading failure model. Additionally, it allows for capturing the effects of the interaction between local primary frequency control and system-level frequency control on cascading failures. Numerical experiments conducted on the IEEE 118- and 300-bus systems demonstrate the effectiveness of increasing primary frequency control reserve in mitigating power outages. However, increasing the droop control coefficients, which aim to enhance system robustness, results in larger power outage sizes.

This study proposes a cascading failure model for power systems that addresses event-triggered power flow divergence. After a cascading failure event, voltage instability is a major cause of event-triggered power flow divergence. To detect voltage instability events, we track the steady-state voltage profiles across successive cascade generations by adopting continuation power flow (CPF). Various cascading failure events are incorporated into the CPF computation. Hence, the proposed model can identify voltage instability by monitoring the emergence of buses that hit a saddle-node bifurcation (SNB) point. In this way, the proposed cascading failure model is able to derive equilibrium points, if they exist, under all conditions. Furthermore, the model incorporates primary frequency control instead of slack buses to account for power losses, thereby improving the accuracy of the results. In addition, we propose a set of metrics to measure the cascading failure propagation rate and the influence of voltage instability events on cascading failure outcomes. Experimental results indicate that power system configurations exert a significant influence on voltage instability events.

Power systems around the world exhibit diverse topological characteristics depending on historical and geographical factors. In this paper, we aim to acquire insights into the relationship between the topological characteristics of power systems and their robustness through controlled experiments. To achieve this, we generate synthetic networks that incorporate the generation and load settings of practical power systems. We test various aspects, such as topological properties of regional power grids, interconnections among regional power grids, network sizes, and power balance of regional power grids. We derive inspiring results by performing cascading failure trials in test networks. Specifically, a wide and compact power grid can be more robust than a narrow and elongated power grid. Moreover, increasing the capacity of interconnection links among regional power grids may result in a less robust power system concerning cascading failures.