APPROACHES TO RESILIENCE ASSESSMENT OF ENERGY SYSTEMS: LITERATURE REVIEW

Authors

DOI:

https://doi.org/10.32782/ecovis/2026-1-11

Keywords:

resilience, assessment, quality-quantitative and deterministic methods, optimization models

Abstract

The resilience approach has been actively developing since the early 2000s as an alternative to risk management due to the growing number of various threats, on one side, and the increase in requirements for the reliability of critical infrastructure, on the other side. The purpose of this investigation is to summarize the existing foreign literature to fill the methodological gap in Ukrainian science on the assessment of infrastructure resilience, in particular, energy and heating. It has been established that of the entire available list of approaches to assessing critical infrastructure resilience, the most widespread and interesting for application in the energy sector are qualitative-quantitative and deterministic methods and optimization models. The investigation revealed a wide range of proposed deterministic methods, based on single or a couple of indicators, to assess system resilience. They are simpler but require precise quantitative data and are less well aligned with the conceptual framework of the resilience approach. The last drawback of deterministic methods is partially or completely eliminated by qualitative-quantitative methods, which makes them more suitable for assessing the resilience of energy systems. Optimization models require a higher quality of the data used, which imposes certain restrictions on indicators for assessing resilience. In optimization models for assessing the resilience of critical infrastructure systems, in particular, energy, economic indicators embedded in the models are often used. However, in most cases, new functions that describe the resilience of the system are integrated into existing models. It has been found that along with this, more complex solutions have become widespread, which propose integrating into models maximization functions that describe not only the economic aspects of resilience, but also technical, environmental and socio-organizational ones. This approach has a number of advantages that characterize the resilience of the system from the standpoint of its life cycle, while covering all key dimensions of resilience (technical, economic, social, organizational).

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https://doi.org/10.3390/buildings11100464

Kozine I., Petrenj B., Trucco P. (2018). Resilience capacities assessment for critical infrastructures disruption: the READ framework (part 1). International Journal of Critical Infrastructures, vol. 14(3), pp. 199-220.

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Cong H. et al. (2018). Robust optimization for improving resilience of integrated energy systems with electricity and natural gas infrastructures. J. Mod. Power Syst. Clean Energy. DOI: https://doi.org/10.1007/s40565-018-0377-5

Almoghathawi Y, Barker K, Albert LA. (2019). Resilience-driven restoration model for interdependent infrastructure networks. Reliab Eng Syst Saf, 185, pp. 12–23. DOI: https://doi.org/10.1016/j.ress.2018.12.006

Hammer L., Veith E. M. (2021). Hybrid Renewable Energy System Optimization is Lacking Consideration of System Resilience and Robustness: An Overview. ENERGY 2021: The Eleventh International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies. Available at: https://personales.upv.es/thinkmind/dl/conferences/energy/energy_2021/energy_2021_3_20_30015.pdf (accessed March 10, 2026)

Eriksson E.L., Gray E.M. (2019). Optimization of renewable hybrid energy systems– A multi-objective approach. Renewable Energy, vol. 133, pp. 971–999. DOI: https://doi.org/10.1016/j.renene.2018.10.053

Yazdanie М. (2023). Resilient energy system analysis and planning using optimization models. Energy and Climate Change, vol. 4, 100097. DOI: https://doi.org/10.1016/j.egycc.2023.100097

Published

31.03.2026

How to Cite

Kyryziuk, S. (2026). APPROACHES TO RESILIENCE ASSESSMENT OF ENERGY SYSTEMS: LITERATURE REVIEW. Scientific Journal of Yuriy Fedkovich Chernivtsi National University. Economics, (1), 75–81. https://doi.org/10.32782/ecovis/2026-1-11