Research Article

Fuzzy multi-objective optimization of the energy transition towards renewable energies with a mixed methodology

Federico Gabriel Camargo

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Paper aims: the combination of the quality indices, a novel model called “Dynamic Growth Allocation Model (DGAM)”, Fuzzy Decision Making Theory (FDM), Analytical Hierarchy Process (AHP) and the Evolutive Particle Swarm Optimization (EPSO) is proposed.

Originality: the multi-objective optimization (with uncertainty) of the Argentine energy transition is not sufficiently studied. This combined methodology in this problem was not published and it had good, relatively easy and fast results.

Research method: the optimization indices (EROI, CO2, IC and RP), the methodology used (DGAM, FDM, AHP and EPSO) and its results are analyzed.

Main findings: (i) the nuclear energy allowed the renewable transition; (ii) the fossil dismantling and the investment in biomass and wind are needed; (iii) the EROI depends on the good load factor, useful life and performance.

Implications for theory and practice: It is sought a minimum Renewable Participation (RP) of 20% of Argentina with a sustainable energy matrix.


Multiobjective Optimization with Energy Scenarios, Energy Return Returned On Investment (EROI), Investment Cost and Emissions, Fuzzy Decision Making Theory, Evolutive Particle Swarm Optimization (EPSO)


Adesanya, A. A., Sidortsov, R. V., & Schelly, C. (2020). Act locally, transition globally: Grassroots resilience, local politics, and five municipalities in the United States with 100% renewable electricity. Energy Research & Social Science, 67, 101579.

Argentina. Cámara Argentina de Energías Renovables. (2021a). Informes. Ministerio de Energía y Minería. Retrieved in 2021, January 25, from

Argentina. Ministerio de Economia. Secretaría de Energía. (2021b). Datos Energía. Ministerio de Energía y Minería. Retrieved in 2021, January 25, from

Arvesen, A., & Hertwich, E. G. (2015). More caution is needed when using life cycle assessment to determine energy return on investment (EROI). Energy Policy, 76, 1-6.

Bogdanov, D., Ram, M., Aghahosseini, A., Gulagi, A., Oyewo, A. S., Child, M., Caldera, U., Sadovskaia, K., Farfan, J., De Souza Noel Simas Barbosa, L., Fasihi, M., Khalili, S., Traber, T., & Breyer, C. (2021). Low-cost renewable electricity as the key driver of the global energy transition towards sustainability. Energy, 227, 120467.

Brundage, M. P., Lechevalier, D., & Morris, K. C. (2019). Toward standards-based generation of reusable life cycle inventory data models for manufacturing processes. Journal of Manufacturing Science and Engineering, 141(2), 021017.

Camargo, F. G., Schweickardt, G. A., & Casanova, C. A. (2018). Maps of Intrinsic Cost (IC) in reliability problems of medium voltage power distribution systems through a Fuzzy multi-objective model. Dyna (Bilbao), 85(204), 334-343.

Capellán-Pérez, I., de Castro, C., & Miguel González, L. J. (2019). Dynamic Energy Return on Energy Investment (EROI) and material requirements in scenarios of global transition to renewable energies. Energy Strategy Reviews, 26, 100399.

Falcone, P. M., Lopolito, A., & Sica, E. (2019). Instrument mix for energy transition: a method for policy formulation. Technological Forecasting and Social Change, 148, 119706.

Hwang, C. L., Lai, Y. J., & Liu, T. Y. (1993). A new approach for multiple objective decision making. Computers & Operations Research, 20(8), 889-899.

Hwangbo, S., Heo, S., & Yoo, C. (2021). Development of deterministic-stochastic model to integrate variable renewable energy-driven electricity and large-scale utility networks: towards decarbonization petrochemical industry. Energy, 238, (Pt C), 122006.

Isoaho, K., & Karhunmaa, K. (2019). A critical review of discursive approaches in energy transitions. Energy Policy, 128, 930-942.

Kadkol, A. A. (2021). Mathematical model of particle swarm optimization: numerical optimization problems. In B. A. Mercangöz (Eds.),Applying particle Swarm optimization (pp. 73-95). Cham: Springer.

Kokkinos, K., Karayannis, V., & Moustakas, K. (2020). Circular bio-economy via energy transition supported by Fuzzy Cognitive Map modeling towards sustainable low-carbon environment. The Science of the Total Environment, 721, 137754. PMid:32172116.

Kosheleva, O., & Kreinovich, V. (2018). Why Bellman-Zadeh approach to fuzzy optimization. Applied Mathematical Sciences.

Ligus, M., & Peternek, P. (2018). Determination of most suitable low-emission energy technologies development in Poland using integrated fuzzy AHP-TOPSIS method. Energy Procedia, 153, 101-106.

Lovins, A. (2017). Energy efficiency. Energy Economics, 1, 234-258.

Miranda, V., Vigo, J., Carvalho, L., Marcelino, C., & Wanner, E. (2019, December). Epso enhanced by adaptive scaling and sub-swarms. In Proceedings of the 20th International Conference on Intelligent System Application to Power Systems (ISAP) (pp. 1-6). IEEE.

Navas-Anguita, Z., Cruz, P. L., Martin-Gamboa, M., Iribarren, D., & Dufour, J. (2019). Simulation and life cycle assessment of synthetic fuels produced via biogas dry reforming and Fischer-Tropsch synthesis. Fuel, 235, 1492-1500.

Ruggeri, E., & Garrido, S. (2021). More renewable power, same old problems? Scope and limitations of renewable energy programs in Argentina. Energy Research & Social Science, 79, 102161.

Shahzadi, G., Akram, M., & Al-Kenani, A. N. (2020). Decision-making approach under Pythagorean fuzzy Yager weighted operators. Mathematics, 8(1), 70.

Wang, Y., Zhang, N., Zhuo, Z., Kang, C., & Kirschen, D. (2018). Mixed-integer linear programming-based optimal configuration planning for energy hub: starting from scratch. Applied Energy, 210, 1141-1150.

Zhao, N., & You, F. (2021). New York State’s 100% renewable electricity transition planning under uncertainty using a data-driven multistage adaptive robust optimization approach with machine-learning. Advances in Applied Energy, 2, 100019.

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