Development of Metrics for Resilience Quantification in Energy Systems

Silva, Fellipe Sartori da; Matelli, José Alexandre

doi:10.36001/phmconf.2019.v11i1.822

Cited by 2 publications

References 3 publications

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How Uncertain Is Too Uncertain? Validity Tests for Early Resilient and Risk-Based Design Processes

Hulse

Hoyle

Tumer

et al. 2020

Journal of Mechanical Design

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A variety of risk and resilience-based design methods have been put forward over the years that seek to provide designers the tools to reduce the effects of potential hazards in the early design phase. However, because of the high level of uncertainty and low-fidelity design representations, one might justifiably wonder if using a resilient design process in the early design phase will reliably produce useful results that would improve the realized design. This paper provides tests that can be performed on a design process to determine whether it gives meaningful results when applied to an uncertain problem. These tests check three metrics: the change in the design when uncertainty is considered, the increase in the expected value of the design, and the cost of choice-related uncertainty: to see if they stay within desirable bounds. This approach is illustrated using two case studies to demonstrate how both discrete and continuous parametric uncertainty can be considered in the testing procedure. These case studies show that early design process validity is sensitive to the level of uncertainty and magnitude of design changes, suggesting that while there is a justifiable decision-theoretic case to consider high-level high-impact design changes during the early design phase, there is less of a case to choose between relatively similar design options because the cost of making the choice under high uncertainty is greater than the expected improvement in value of choosing the better design.

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How Uncertain Is Too Uncertain? Validity Tests for Early Resilient and Risk-Based Design Processes

Hulse

Hoyle

Tumer

et al. 2020

Journal of Mechanical Design

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Understanding Resilience Optimization Architectures With an Optimization Problem Repository

Hulse

Zhang

Hoyle

2021

Volume 3A: 47th Design Automation Conference (DAC)

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Optimizing a system’s resilience can be challenging, especially when it involves considering both the inherent resilience of a robust design and the active resilience of a health management system to a set of computationally-expensive hazard simulations. While prior work has developed specialized architectures to effectively and efficiently solve combined design and resilience optimization problems, the comparison of these architectures has been limited to a single case study. To further study resilience optimization formulations, this work develops a problem repository which includes previously-developed resilience optimization problems and additional problems presented in this work: a notional system resilience model, a pandemic response model, and a cooling tank hazard prevention model. This work then uses models in the repository at large to understand the characteristics of resilience optimization problems and study the applicability of optimization architectures and decomposition strategies. Based on the comparisons in the repository, applying an optimization architecture effectively requires understanding the alignment and coupling relationships between the design and resilience models, as well as the efficiency characteristics of the algorithms. While alignment determines the necessity of a surrogate of resilience cost in the upper-level design problem, coupling determines the overall applicability of a sequential, alternating, or bilevel structure. Additionally, the application of decomposition strategies is dependent on there being limited interactions between variable sets, which often does not hold when a resilience policy is parameterized in terms of actions to take in hazardous model states rather than specific given scenarios.

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Development of Metrics for Resilience Quantification in Energy Systems

Cited by 2 publications

References 3 publications

How Uncertain Is Too Uncertain? Validity Tests for Early Resilient and Risk-Based Design Processes

How Uncertain Is Too Uncertain? Validity Tests for Early Resilient and Risk-Based Design Processes

Understanding Resilience Optimization Architectures With an Optimization Problem Repository

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