Quantitative Methods for Data-Driven Next-Generation Resilience of Energy Systems and Their Supply Chains

Natasha J. Chrisandina (), Shivam Vedant (), Mahmoud M. El-Halwagi (), Efstratios N. Pistikopoulos () and Eleftherios Iakovou ()
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Natasha J. Chrisandina: Texas A&M University
Shivam Vedant: Texas A&M University
Mahmoud M. El-Halwagi: Texas A&M University
Efstratios N. Pistikopoulos: Texas A&M University
Eleftherios Iakovou: Texas A&M University

A chapter in Handbook of Smart Energy Systems, 2023, pp 409-427 from Springer

Abstract: Abstract In order to meet the growing global energy demand in the new era of increased volatility, uncertainty, complexity, and ambiguity (VUCA), next-generation energy systems need to be designed with considerations for increased resilience along with cost efficiency. This need is further demonstrated by an increase in recent publications highlighting the advantages of resilience-aware design and management of energy supply chains. In this chapter, we first present the evolution of resilience, the key motivators, and the antecedents to supply chain resilience. We then provide a taxonomy of performance and structural metrics for quantifying the resilience of energy systems. Building on that, we propose a conceptual framework for next-generation data-driven cost-competitive resilience specifically for energy systems by integrating multiscale modeling approaches. Finally, future research directions for the continued enhancement of the proposed framework towards next-generation energy supply chains are discussed.

Keywords: Supply chain resilience; Resilience metrics; Multiscale modeling; Data-driven approach; Energy systems (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-97940-9_182

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DOI: 10.1007/978-3-030-97940-9_182

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