Expert Opinion Fuzzy Model to Assess Energy Systems Resilience in Mexico

Authors

DOI:

https://doi.org/10.61467/2007.1558.2026.v17i1.1080

Keywords:

Fuzzy Logic, energy system resilience, expert opinion, fuzzy knowledge-based modelling

Abstract

Existing energy resilience metrics have primarily focused on the technical performance of systems, with evaluations typically conducted using mathematical algorithms. However, as several authors have noted, energy resilience involves more than the ability to recover from a blackout, as it also encompasses social and economic contexts. In response to this limitation, the present study proposes a metric for assessing energy resilience that incorporates not only technical factors, but also environmental, social, economic, and institutional dimensions. The metric was validated through the judgements of experts with more than 15 years of professional experience in the field.

Given the qualitative nature of the information associated with these dimensions, fuzzy logic is employed as the analytical approach. Two fuzzy logic models were applied: a simplified model comprising five variables and a more detailed model comprising fifteen variables organised into sub-principles. The results indicate a national energy resilience value of 1.55 for the simplified model and 2.0 for the detailed model, suggesting that the inclusion of a broader set of variables yields a more representative assessment. These findings support the view that fuzzy logic is an appropriate tool for this type of evaluation, as it allows expert knowledge to be systematically incorporated into the assessment process. Furthermore, the proposed approach may be adaptable to energy resilience evaluations conducted at both national and local scales.

 

Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1080

Dimensions.
Open Alex.

References

Ahmadi, S., Saboohi, Y., & Vakili, A. (2021). Frameworks, quantitative indicators, characters, and modelling approaches to analysis of energy system resilience: A review. Renewable and Sustainable Energy Reviews, 144, 110988. https://doi.org/10.1016/j.rser.2021.110988 IDEAS/RePEc

Canavese, D., Ortega, N. R. S., & Queirós, M. (2014). The assessment of local sustainability using fuzzy logic: An expert opinion system to evaluate environmental sanitation in the Algarve region, Portugal. Ecological Indicators, 36, 711–718. https://doi.org/10.1016/j.ecolind.2013.09.030 University of Lisbon Research Portal

CFE (Comisión Federal de Electricidad). (2022). Reporte Anual 2021. https://www.cfe.mx/finanzas/reportes-financieros/Informe%20Anual%20Documentos/Informe%20Anual%202021.pdf (consultado el 28 noviembre 2022). cfe.mx+1

Delina, L. L., Ocon, J., & Esparcia, E. (2020). What makes energy systems in climate-vulnerable islands resilient? Insights from the Philippines and Thailand. Energy Research & Social Science, 69, 101703. https://doi.org/10.1016/j.erss.2020.101703 researchportal.hkust.edu.hk

Di Nardo, E., & Simone, R. (2019). A model-based fuzzy analysis of questionnaires. Statistical Methods & Applications, 28(2), 187–215. https://doi.org/10.1007/s10260-018-00443-9 IDEAS/RePEc

Dickson, E., Baker, J. L., Hoornweg, D., & Tiwari, A. (2012). Urban Risk Assessment: Understanding disaster and climate risk in cities. Washington, DC: The World Bank. https://doi.org/10.1596/978-0-8213-8962-1 (ISBN 978-0-8213-8962-1). World Bank+2World Bank+2

Erker, S., Stangl, R., & Stoeglehner, G. (2017). Resilience in the light of energy crises – Part I: A framework to conceptualise regional energy resilience. Journal of Cleaner Production, 164, 420–433. https://doi.org/10.1016/j.jclepro.2017.06.163

Flores Payán, L., & Salas Durazo, I. A. (2015). Las brechas de género en la calidad del empleo en México. Una valoración basada en modelos de lógica difusa. Análisis Económico, 30(75), 89–112. https://www.redalyc.org/articulo.oa?id=41343702004

Gamalath, I., Hewage, K., Ruparathna, R., Karunathilake, H., Prabatha, T., & Sadiq, R. (2018). Energy rating system for climate-conscious operation of multi-unit residential buildings. Clean Technologies and Environmental Policy, 20(4), 785–802. https://doi.org/10.1007/s10098-018-1510-x OUCI+1

Hamborg, S., Meya, J. N., Eisenack, K., & Raabe, T. (2020). Rethinking resilience: A cross-epistemic resilience framework for interdisciplinary energy research. Energy Research & Social Science, 59, 101285. https://doi.org/10.1016/j.erss.2019.101285

Lafuente, R., Page, Á., Sánchez-Lacuesta, J., & Tortosa, L. (1998). Application of fuzzy logic techniques for the qualitative interpretation of preferences in a collective questionnaire for users of wheelchairs. Journal of Rehabilitation Research and Development, 35(1), 91-107. https://pubmed.ncbi.nlm.nih.gov/9505257/ PubMed+2rehab.research.va.gov+2

MathWorks. (2022). Fuzzy Logic Toolbox™. Available online: https://la.mathworks.com/products/fuzzy-logic.html (accessed 28 November 2022). MathWorks+1

Mejía-Montero, A., León-Rodríguez, N. R., Lorenzo-Yera, B., Díaz-Florián, D., Thomson, H., Robles-Bonilla, T., et al. (2022). Energy transitions in Latin America through the lens of vulnerability and resilience: Insights from Colombia, Cuba and Mexico. En K. Araújo (Ed.), Routledge Handbook of Energy Transitions (1ª ed.). Routledge. https://doi.org/10.4324/9781003183020-12

Papas, P. J., Ramsey, D. S., Holmes, J., Frood, D., & Lyon, S. (2022). Integrating data, expert opinion and fuzzy logic in the development of an index of wetland condition. Marine and Freshwater Research, 73, 1184-1195. https://doi.org/10.1071/MF21197

Reinhart, B. J., & Reinhart, A. (2024). Tropical Cyclone Report: Hurricane Otis (EP182023). National Hurricane Center. https://www.nhc.noaa.gov/data/tcr/EP182023_Otis.pdf

Reuters. (2021). Texas freeze hits northern Mexico with $2.7 billion blackout. Reuters. https://www.reuters.com/business/energy/northern-mexico-suffers-second-day-power-outages-after-texas-frost-2021-02-16/

Roege, P. E., Collier, Z. A., Mancillas, J., McDonagh, J. A., & Linkov, I. (2014). Metrics for energy resilience. Energy Policy, 72, 249-256. https://doi.org/10.1016/j.enpol.2014.04.012

SENER (Secretaría de Energía). (2022). Prontuario Estadístico. https://www.gob.mx/cms/uploads/attachment/file/788927/202212_En_elaboraci_n_Formato_-_Accesibilidad.pdf (accessed 28 November 2022).

Seuret-Jimenez, D., Robles-Bonilla, T., & Cedano, K. G. (2020). Measurement of energy access using fuzzy logic. Energies, 13, 3266. https://doi.org/10.3390/en13123266

Sharifi, A., & Yamagata, Y. (2016). Principles and criteria for assessing urban energy resilience: A literature review. Renewable and Sustainable Energy Reviews, 60, 1654-1677. https://doi.org/10.1016/j.rser.2016.03.028

Downloads

Published

2026-01-02

How to Cite

Robles Bonilla, T., & Cedano Villavicencio, K. G. (2026). Expert Opinion Fuzzy Model to Assess Energy Systems Resilience in Mexico. International Journal of Combinatorial Optimization Problems and Informatics, 17(1), 5–19. https://doi.org/10.61467/2007.1558.2026.v17i1.1080

Issue

Section

Articles