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Energy systems are regularly subject to major disruptions affecting economic activities, operation of infrastructure and the society as a whole. Resilience assessment comprises the pre-event oriented classical risk assessment as a central element, but it goes beyond that because it also includes and evaluates post-event strategies to improve the functioning of the system during its future operation. First, an overview of resilience definitions used across various scientific disciplines is presented, followed by an in-depth analysis of resilience assessment and quantification for energy systems. The relevant literature is classified by approach and according to four key functions of resilience: resist, restabilize, rebuild, and reconfigure. Findings show that irrespective of the research field, a resilient system always operates with an aim to minimize the potential consequences resulting from a disruptive event and to efficiently recover from a potential system performance loss. [ABSTRACT FROM AUTHOR]

Composite Indicators (CIs, a.k.a. indices) are increasingly used as they can simplify interpretation of results by condensing the information of a plurality of underlying indicators in a single measure. This paper demonstrates that the strength of the correlations between the indicators is directly linked with their capacity to transfer information to the CI. A measure of information transfer from each indicator is proposed along with two weight-optimization methods, which allow the weights to be adjusted to achieve either a targeted or maximized information transfer. The tools presented in this paper are applied to a case study for resilience assessment of energy systems, demonstrating how they can support the tailored development of CIs. These findings enable analysts bridging the statistical properties of the index with the weighting preferences from the stakeholders. They can thus choose a weighting scheme and possibly modify the index while achieving a more consistent (by correlation) index. • Composite indicator development is enhanced with advanced statistical analysis. • Tools to assess the information transferred from indicators to the index are proposed. • Two weight-optimization methods allow adjusting information transfer to the index. • The approaches are applied to a real case study on resilience of electricity supply. [ABSTRACT FROM AUTHOR]

• A structured approach for robustness assessment of rankings is proposed. • Resilience of electricity supply security is analyzed for 140 countries. • 12 resilience-related indicators are quantified using a structured methodology. • 38 combinations of normalization methods and aggregation functions are applied. • General ranking trends and different levels of compensation are identified. Indicator-based approaches are suitable to assess multi-dimensional problems. In order to compare a set of alternatives, one strategy is to normalize individual indicators to a common scale and aggregate them into a comprehensive score. This study proposes the Electricity Supply Resilience Index (ESRI), which is a measure of a nation's electricity supply resilience. Starting from an initial set of individual indicators derived through a structured selection process, the ESRI is calculated for 140 countries worldwide. To account for robustness of the resulting resilience index, 38 combinations of eight normalization methods and six aggregation functions were considered. Results show a clear country ranking trend, with robust top- and low-performing countries across all combinations. However, the ranking disparity becomes large for average performing countries, especially if their indicators show high variability. Furthermore, the differences of the rankings are quantified through the Rank Difference Measure (RDM), which identifies the categorical scales and the minimum aggregator as the most different ones. Finally, the effects of different compensation levels of the aggregation functions are discussed. The findings of the present study aim to provide recommendations for policymakers on how composite indexes results depend on assumptions and chosen approaches. [ABSTRACT FROM AUTHOR]