Quantitative analysis of power systems resilience: Standardization, categorizations, and challenges

Data‐driven spatio‐temporal analysis of wildfire risk to power systems operation

“…In addition, the framework aids improve the resilience of the system by spatio‐temporally informing the disaster progression phase of the resilience trapezoid as illustrated in Figure 13, hence reducing the “dip” in the resilience curve [47].…”

Resilience Framework, Methods, and Metrics for the Prioritization of Critical Electrical Grid Customers

“…One thing to keep in mind is the fact that there is no one-size-fits-all approach for all power systems, and that the assignment of such weights to customers can vary significantly from one operator to another based on their unique systems and the current and expected states of their critical customers. It should also be mentioned that, although the factors influencing the electrical resilience of modern electric power systems are numerous and constantly changing [79], the viability of power flow is the most influential factor in the resilience of electric power systems [40]. Following the definition provided by Raoufi et al, the research presented here assumes that the power system resilience is "the ability of this system to withstand disasters (low-frequency high-impact incidents) efficiently while ensuring the least possible interruption in the supply of electricity, sustain critical social services, and enabling a quick recovery and restoration to the normal operation state" [80].…”

“…However, quantification of the social consequences of not powering critical loads during a blackout has not received the same attention, and a study to address them is needed. Some authors, such as Mishra et al [38], Poulin et al [39], Umunnakwe et al [40], Shi et al [41], Hossain et al [42], Plotnek et al [43], and Wang et al [44], provided a review on metrics and strategies for grid resilience and reliability; other authors, such as Sun et al [45], Souto et al [46], Gorham et al [47], and Rocchetta [48], provided statistical analysis on resilience metrics; some modeled engineered system and infrastructure availability, such as Cheng et al [49], Azimian et al [50] and Senkel et al [51]; others proposed multi-stage frameworks for resilience evaluation, such as Mahzarnia et al [ Alkhaleel et al [58]; and Younesi et al [59] developed multi-objective resilience-economic stochastic scheduling models. such as As a consequence, from this deeper survey of the updated literature related to the topic addressed here, it was possible to find that-even though there are plenty of different approaches-this paper contributes to the pool of existing knowledge by:…”

A Risk-Driven Probabilistic Approach to Quantify Resilience in Power Distribution Systems

“…The existing metrics for resilience can be broadly categorized as: a) attribute-based metrics that identify power system attributes such as robustness, resourcefulness, adaptivity, recoverability, and situational awareness [3] and b) performance-based metrics that describes the system's ability to maintain supply (i.e., the system's availability [4]) and often measured using the conceptual resilience curve [5]. Different resilience indicators that are widely used in literature are based on the optimal repair time of critical components [6], energy not served after an extreme event [7], total critical loads supplied during the aftermath of a disaster [8], and in terms of infrastructure recovery [9]. An exhaustive list of examples of resilience indicators for consideration in developing a suitable metric can be found in [10].…”