Our paper Literature Review on Modeling and Simulation of Energy Infrastructures from a Resilience Perspective led by PhD student Jing has been accepted by Reliability Engineering & System Safety. This is a recognition to her work by the resilience community. Congratulations to her and keep up!
J. Wang, W. Zuo, L. R.-B., X. Lu, J. Wang, Y. Lin 2019. "Literature Review on Modeling and Simulation of Energy Infrastructures from a Resilience Perspective." Reliability Engineering and System Safety, 183, pp. 360-373. DOI: https://doi.org/10.1016/j.ress.2018.11.029.
Highlights
• Comprehensively review 30 state-of-the-art energy infrastructure models using the emerging concept of resilience.
• Summarize common topics and typical implementation approaches for the modeling and simulation of energy infrastructures.
• Propose five resilience indicators for the evaluation of energy infrastructure models.
• Offer insights and future trends for model developers and model users.
Abstract
Recent years have witnessed an increasing frequency of disasters, both natural and human-induced. This applies pressure to critical infrastructures (CIs). Among all the CI sectors, the energy infrastructure plays a critical role, as almost all other CIs depend on it. In this paper, 30 energy infrastructure models dedicated for the modeling and simulation of power or natural gas networks are collected and reviewed using the emerging concept of resilience. Based on the review, typical modeling approaches for energy infrastructure resilience problems are summarized and compared. The authors, then, propose five indicators for evaluating a resilience model; namely, catering to different stakeholders, intervening in development phases, dedicating to certain stressor and failure, taking into account different interdependencies, and involving socio-economic characteristics. As a supplement, other modeling features such as data needs and time scale are further discussed. Finally, the paper offers observations of existing energy infrastructure models as well as future trends for energy infrastructure modeling.