Power system resilience assessment considering critical infrastructure resilience approaches and government policymaker criteria

Raoufi, Habibollah; Vahidinasab, Vahid

doi:10.1049/gtd2.12218

Cited by 7 publications

(4 citation statements)

References 89 publications

(171 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Equation ( 47) is utilized to calculate the failure probability of the transmission line at any wind speed. The probability of transmission line conductor and each single tower failure FIGURE 4 Fragility curves of the different types of tower and conductor [39] is obtained directly from Figure 4. Equation (48) has been used to calculate the probability of tower failure throughout the route.…”

Section: Second Stage Formulationmentioning

confidence: 99%

“…By considering the resilience index, generators G4 to G6 and lines L7, L21, L22, L23, and L24 are justified. There is a list of this case's features and results in Table (4), and the planning results with and without RI are compared.…”

Section: Static Expansion Planning Frameworkmentioning

confidence: 99%

“…High‐impact and low‐probability (HILP) events are not likely to happen because they have a low probability, but they will have significant consequences of failure for the power systems [3]. Given that resilience is defined in the face of various events and the extent of the resulting perturbations, assessing the level of resilience of a power system is a complex challenge [4]. Events such as storms, earthquakes, and floods cause many socioeconomic losses and disrupt many people's everyday lives for a long time [5].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A two‐stage simulation‐based framework for optimal resilient generation and transmission expansion planning

Firoozjaee

Sheikh‐El‐Eslami

2022

IET Generation Trans & Dist

View full text Add to dashboard Cite

This paper presents a two‐stage resilient framework for generation and transmission expansion planning that profits static and dynamic models. The first stage problem finds the generation and transmission expansion results without considering extreme events. Given the planned power system, in the second stage, a simulation‐based method is employed to accurately model the effects of the severe events and measure the expected demand not served. The proposed framework updates the formulation of the planning problem to efficiently search for a new expansion plan resulting in higher resilience levels. This paper uses the maximizing social welfare in the planning model to find the optimal result. Also, the effect of maximizing social welfare in updating the formulation of the planning problem is investigated. In this case, the planning problem is a bi‐level problem that using the primal‐dual formulation leads to a single‐level problem called a mathematical program with equilibrium constraints. After the linearization, the planning problem becomes a mixed‐integer linear programming problem. The planning model profits from reinforced transmission lines to improve power system resilience. The proposed framework is evaluated on 4‐bus, 6‐bus, and 118‐bus systems. According to the results, the proposed method with a low additional cost boosts power system resilience.

show abstract

Section: Second Stage Formulationmentioning

confidence: 99%

Section: Static Expansion Planning Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A two‐stage simulation‐based framework for optimal resilient generation and transmission expansion planning

Firoozjaee

Sheikh‐El‐Eslami

2022

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…As critical infrastructures of modern energy network, power systems play an essential role in economic growth, individual's life and national security [1]. Given that power transmission and distribution systems are located across a wide geographical area and it is difficult to provide comprehensive protection, various kinds of electrical components comprising these systems are likely to be attractive targets of malicious attackers [2].…”

Section: Introductionmentioning

confidence: 99%

A tri‐level optimization model for power systems defense considering cyber‐physical interdependence

Guo

Yang³

2023

IET Generation Trans & Dist

View full text Add to dashboard Cite

To seek the optimal defense strategy against malicious attacks, this paper proposes a novel defender-attacker-operator model applicable to the cyber-physical power system. The proposed model considers the interdependence in functionality and topology of the cyber network and the power network as well as the cyber network's constraints. The defender aims to minimize the load curtailment caused by the attacker, while the latter intends to maximize the power loss. The operator in the bottom level takes corrective action to minimize the attack consequence. This tri-level model is decomposed into a master problem and a subproblem, based on which the column-and-constraint generation algorithm is implemented to obtain the optimal solution. Comparative case studies based on IEEE RTS-79 system are carried out to demonstrate the advantage of the proposed method, and to investigate the impact of the energy coupling strength, the topologically independent link and the defensive resource. The effectiveness of this model is validated by the sensitivity analysis.

show abstract

A Comparative Analysis of Performance-Based Resilience Metrics via a Quantitative-Qualitative Combined Approach: Are We Measuring the Same Thing?

Tang,

Han,

Wang

et al. 2023

Int J Disaster Risk Sci

View full text Add to dashboard Cite

Since the proposal of the pioneering “resilience triangle” paradigm, various time-series performance-based metrics have been devised for resilience quantification. The numerous choices diversify the toolbox for measuring this compound system concept; however, this multiplicity causes intractable questions for applications, including “Do these metrics measure the same resilience?” and “Which one to pick under what circumstance?” In this study, we attempted to address these two fundamental issues using a comprehensive comparative investigation. Through a quantitative-qualitative combined approach, 12 popular performance-based resilience metrics are compared using empirical data from China’s aviation system under the disturbance of COVID-19. Quantitative results indicate that only 12 of the 66 metric pairs are strongly positively correlated and with no significant differences in quantification outcomes; qualitative results indicate that the majority of the metrics are based on different definition interpretations, basic components, and expression forms, and thus essentially measure different resilience. The advantages and disadvantages of each metric are comparatively discussed, and a “how to choose” guideline for metric users is proposed. This study is an introspective investigation of resilience quantification studies, aiming to offer a new perspective to scrutinize those benchmarking metrics.

show abstract

Power system resilience assessment considering critical infrastructure resilience approaches and government policymaker criteria

Cited by 7 publications

References 89 publications

A two‐stage simulation‐based framework for optimal resilient generation and transmission expansion planning

A two‐stage simulation‐based framework for optimal resilient generation and transmission expansion planning

A tri‐level optimization model for power systems defense considering cyber‐physical interdependence

A Comparative Analysis of Performance-Based Resilience Metrics via a Quantitative-Qualitative Combined Approach: Are We Measuring the Same Thing?

Contact Info

Product

Resources

About