A Quantitative Resilience Measure Framework for Power Systems Against Wide-Area Extreme Events

Younesi, Abdollah; Shayeghi, Hossein; Safari, Amin; Siano, Pierluigi

doi:10.1109/jsyst.2020.3001222

Cited by 18 publications

(10 citation statements)

References 29 publications

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“…[27][28][29] Under this context, the Markov chain has been widely used to describe the time-variant characteristics of the recovery process. 28,[30][31][32][33] Next, the relationship between R s,T,i and R s (0, T) is investigated. According to Equation ( 6), one has,…”

Section: A Special Case With Independence Assumptionsmentioning

confidence: 99%

Role of recovery profile dependency in time‐dependent resilience

Wang

2023

Engineering Reports

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Resilience assessment of civil structures and infrastructure systems plays a vital role in quantitatively measuring the object's ability to withstand and recover from disruptive events (e.g., natural hazards). The performance of the object (structure or system) may degrade with time, and the external loads often display nonstationary characteristics with time‐varying occurrence rate and/or magnitude. In this context, the resilience would be dependent on the duration of the reference period of interest, known as time‐dependent resilience. This article investigates the effect of recovery profile dependency on the time‐dependent resilience of structures and systems. The term “recovery profile dependency” refers to the association of the post‐hazard recovery processes in the aftermath of different load events within the object's service life, and is reflected through the following three aspects: (1) the temporal correlation between the remaining functionalities associated with different load events; (2) the load–recovery interaction (i.e., the scenario that a load event occurs before the full recovery of the degraded functionality due to the previous load event), and (3) the resource allocation strategy in the presence of limited resources, which affects the expeditiousness of each recovery process. In particular, for (2), the concept of average ratio of interaction is proposed to measure the frequency of load‐recovery interaction. The evaluation of time‐dependent resilience is demonstrated through two examples. It is shown that, the time‐dependent resilience is sensitive to the impact of load–recovery interaction and the selection of resource allocation strategy, but is insensitive to the temporal correlation of the remaining functionalities.

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Section: A Special Case With Independence Assumptionsmentioning

confidence: 99%

Role of recovery profile dependency in time‐dependent resilience

Wang

2023

Engineering Reports

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“…Power system resilience is therefore broadly understood as the ability to withstand high impact‐low probability events, rapidly recover from these disruptive events and prevent the impacts of similar future events (Panteli & Mancarella, 2015b). However, developing a comprehensive mathematical resilience measure has become a recent research challenge (Younesi et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Younesi et al. (2020)'s evaluation framework proposed five metrics with the system loss of load probability and the expected demand not supplied also being measured. However, although these studies provided valuable information on measuring the resilient power system performances, understanding what specific elements make a system more resilient remains a challenge.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Talukder et al (2020) quantified power system resilience by measuring stability and performance levels, for which six measures were selected and load loss and repair time integrated as the system performance indicators. Younesi et al (2020)'s evaluation framework proposed five metrics with the system loss of load probability and the expected demand not supplied also being measured. However, although these studies provided valuable information on measuring the resilient power system performances, understanding what specific elements make a system more resilient remains a challenge.…”

Section: Introductionmentioning

confidence: 99%

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Physical–cyber–human framework‐based resilience evaluation toward urban power system: Case study from China

Wang

Yan

et al. 2022

Risk Analysis

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Because the increased frequency, intensity, and duration of extreme weather events have significantly challenged power systems, there has been an increased interest in resilient power systems. This article establishes a multicriteria resilience evaluation framework for urban power systems from a physical-cyber-human system perspective, in which the two principal elements responsible for power system function degradation are described, the three major domains comprising urban power systems are explained, four core capacities that positively contribute to power system resilience are proposed, and 15 (11 objective and four subjective) power system resilience evaluation indicators are identified. Fuzzy hesitant judgment and a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) aggregation method are employed to minimize the expert divergence and maximize the group consensus. A validation method is designed and a comparison with commonly applied performance-based and attributes-based evaluation methods is conducted. The applicability of the evaluation framework is verified using data from four Chinese municipalities: Shanghai, Beijing, Chongqing, and Tianjin. It was found that Shanghai's resilience was the best, and Chongqing's physical resistance disadvantages would result in the greatest difficulties in coping with extreme event disturbances. Physical, cyber, and human domain resilience enhancement strategies are given for different cities separately. This study provides a practical tool to evaluate, compare, and enhance power system resilience for governments and public utilities.

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“…The proposed method difference with the other previously methods is that it integrates the resilience index directly in formulations and improve resilience while optimizing the operating costs. The fragility index (FI), recovery efficiency index (REI), MG voltage index (MVI), and the lost load index (LLI), are used to assess MG resilience, which are emphasized by major resilience definitions [18].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Resilience of Operational Microgrids Through a Two-Stage Scheduling Strategy Considering the Impact of Uncertainties

et al. 2021

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This paper deals with the stochastic scheduling of a microgrid (MG) to balance the economical and resilience metrics. In the proposed model, the MG resilience indices are integrated into the economic criteria to ensure the resilience of MG operation alongside the main MG actors' profit/loss. The MG fragility index, recovery efficiency index, MG voltage index, and lost load index are considered in the proposed planning model. Further, to make the results more realistic, the uncertainties associated with energy price and wind production, alongside with planning of energy storage systems and electric vehicles parking lots are considered. To achieve a better solution for the security-constraint operation of MG, AC network equations are included in the system modelling. Finally, a large-scale MG based on the IEEE-33 bus testbed is utilized to evaluate the effectiveness of the proposed stochastic scheduling program.

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A Quantitative Resilience Measure Framework for Power Systems Against Wide-Area Extreme Events

Cited by 18 publications

References 29 publications

Role of recovery profile dependency in time‐dependent resilience

Role of recovery profile dependency in time‐dependent resilience

Physical–cyber–human framework‐based resilience evaluation toward urban power system: Case study from China

Enhancing the Resilience of Operational Microgrids Through a Two-Stage Scheduling Strategy Considering the Impact of Uncertainties

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