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

Abstract

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.

A hybrid novel method to economically evaluate the carbon dioxide emissions in the productive chain of Argentina

“…Most of the important published works are carried out using deterministic models, or in the best of cases, mixed with stochastic models. That is, they use classical models of mathematical programming, which have limitations in their formulation and quality of the results obtained (Camargo et al, 2018;Camargo, 2022b). It is observed that these proposals present complex methods, which require a large amount of data, in some cases require supervised learning and most do not consider the presence of uncertainty, but rather are statistical analyses.…”

“…• There is imperfect knowledge/rationality (Camargo et al, 2018;Camargo et al, 2019;Camargo, 2022b), since the preferences (weights) associated with each affected country and individual are unknown (Cavallaro et al, 2018;Jeanne & Korinek, 2019). Firstly, there is uncertainty about the economic impact of the externality on society and a tax according to the social damage caused by emissions.…”

“…These proposals do not take into account hierarchy criteria either or, in the best of cases, they make linear weightings. The latter is an inconvenience due to metric compatibility (same measurement units) and the presence of subjectivity and uncertainty in the assessment of each index (Camargo et al, 2018;Camargo et al, 2019;Camargo, 2019Camargo, , 2021Camargo, , 2022aCamargo, , 2022b.…”

“…• These methodologies have problems of metric compatibility between indices (Camargo et al, 2018(Camargo et al, , 2019Camargo, 2019Camargo, , 2022b. This is because these indices are required to be translated through a subjective economic cost (a priori).…”

“…• The uncertainty (called fundamental uncertainty) allows the problem to be modelled using possibilistic models (fuzzy models). An acceptance level associated with each index is introduced according to the preferences determined by the decision maker (Shahzadi et al, 2020;Camargo, 2019Camargo, , 2022b. The presence of fundamental uncertainty is contemplated, it is associated with the partial/total ignorance of this problem, due to the factors mentioned (ignorance of functional relationships of the impact of the externalities of 2 CO emissions, the elasticities of demand, social impact and equity).…”