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

Camargo, Federico Gabriel

doi:10.1590/0103-6513.20210132

Federico Gabriel Camargo ^* * camargof@frcu.utn.edu.ar

Universidad Tecnológica Nacional – UTN, Electromechanical department, Facultad Regional de Concepción del Uruguay – FRCU, Concepción del Uruguay, Entre Ríos, Argentina

http://orcid.org/0000-0002-8685-1900

* camargof@frcu.utn.edu.ar

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Figure 1
Life Cycle Analysis (LCA) model applied in the present work. Source: The Author.

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Figure 2
Dynamic growth model and its interaction with fuzzy modeling and the metaheuristic EPSO. Source: The Author.

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Figure 3
Electrical matrix (%) vs. time in years for the Optimal Scenario. Source: The author.

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Figure 4
Energy Returned On Investment (EROI) vs. time [years] for the Optimal Scenario. Source: The Authors.

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Figure 5
Emission

{CO}_{2} [{t CO}_{2} EQ / MW]

vs. time

[years]

, for the Optimal Scenario. Source: The Author.

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Figure 6
Investment Cost [USD/MW] vs. time in years, for the Optimal Scenario. Source: The Author.

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Figure 7
Investment Cost [USD/MW] vs. time in years, for the Optimal Scenario. Source: The Author.

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Figure 8
Fuzzy Function associated with the attributes to be optimized and their confluence. Source: The Author.

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Figure 9
Confluence of the fuzzy functions for each scenario. Source: The Author.

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Figure 10
Confluence of the fuzzy functions for each analyzed scenario. Source: The Author.

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Figure A1
Electrical matrix, for the Business as usual (BAU). Source: The Authors.

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Figure A2
Electrical matrix, for the Business as usual (BAU). Source: The Authors.

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Figure A3
Electrical matrix for the Business as usual (BAU) and Collective Responsibility (CR). Source: The Authors.

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Table 1
Search results in the Scopus database (conducted in November 2021).

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Table 2
Parameters associated with each type of generation used (Wind, Solar photovoltaic, Hydraulic, Biomass, Fossil and Nuclear).

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Table 3
Composition of the Argentine electricity matrix, according to the scenarios proposed.

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Author/Year	Country of study	Source	Modeling	Indices	Objective and Constraints	Resolution method	Limitation
Ruggeri & Garrido (2021)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. http://dx.doi.org/10.1016/j.erss.2021.102161. http://dx.doi.org/10.1016/j.erss.2021.10...	Argentina	All types	Deterministic	Socio-economic Indices	Linear	Statistic	Analysis from statistical records
Hwangbo et al. (2021)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. http://dx.doi.org/10.1016/j.energy.2021.122006. http://dx.doi.org/10.1016/j.energy.2021....	South Korea	Fossil	Mixed: deterministic and stochastic	Economic cost, emissions and energy	Linear	Mathematical Programming	Linearization removes accuracy from the solution
Wang et al. (2018)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. http://dx.doi.org/10.1016/j.apenergy.2017.08.114. http://dx.doi.org/10.1016/j.apenergy.201...	Beijing	Fossil			Linear	MILP model based on graph theory	Linearization removes accuracy from the solution
Zhao & You (2021)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. http://dx.doi.org/10.1016/j.adapen.2021.100019. http://dx.doi.org/10.1016/j.adapen.2021....	New York	All types			Multiple and non Linear	Machine Learning	Supervised learning is needed
Kokkinos et al. (2020)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. http://dx.doi.org/10.1016/j.scitotenv.2020.137754. PMid:32172116. http://dx.doi.org/10.1016/j.scitotenv.20...	Greek	Biofuel	Fuzzy Cognitive Map (FCM)	Environmental social, economic and political impacts	Non Linear	Heuristics	It requires interactive methodologies
Capellán-Pérez et al. (2019)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. http://dx.doi.org/10.1016/j.esr.2019.100399. http://dx.doi.org/10.1016/j.esr.2019.100...	Global	All types	Deterministic	EROI and material requirements	Non Linear	Heuristics	Economic costs or emissions are not considered
Navas-Anguita et al. (2019)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. http://dx.doi.org/10.1016/j.fuel.2018.08.147. http://dx.doi.org/10.1016/j.fuel.2018.08...	Spain	Biogas and fossil		Energy and emissions	Linear	life cycle assessment	Economic costs or emissions are not considered
Bogdanov et al. (2021)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. http://dx.doi.org/10.1016/j.energy.2021.120467. http://dx.doi.org/10.1016/j.energy.2021....	Global	All types		Economic cost, emissions and energy		life cycle assessment	Does not consider uncertainty
Adesanya et al. (2020)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. http://dx.doi.org/10.1016/j.erss.2020.101579. http://dx.doi.org/10.1016/j.erss.2020.10...	United States	All types		Economic cost, emissions and energy		Statistic	Analysis from statistical records
Ligus & Peternek (2018)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. http://dx.doi.org/10.1016/j.egypro.2018.10.046. http://dx.doi.org/10.1016/j.egypro.2018....	Poland	All types	Fuzzy	Environmental socioeconomic and political impacts		AHP-TOPSIS method

Power Source	EROI [u]	Useful life [y]	Load factor [u]	Efficiency [u]	Emissions factor $[\frac{t C O_{2} E Q}{M W}]$
Wind Energy	4.11	25	0.37	0.8	0.162
Photovoltaic Energy	3.59	25	0.24	0.65	0.3807
Hydraulic Energy	6.63	60	0.69	0.8	0.0405
Biomass Energy	4.54	15	0.71	0.32	0.081
Fossil Energy	4.30	15	0.89	0.35	0.81
Nuclear Energy	9.61	40	0.72	0.3	0.0405

Power Source	Initial Scenario	Wind Scenario	Photovoltaic Scenario	Hydraulic Scenario	Biomass Scenario	Fossil Scenario	Nuclear Scenario	Optimal Scenario
Wind Energy	4%	14%	3%	10%	9%	12%	9%	9%
Photovoltaic Energy	1%	1%	9%	2%	1%	2%	2%	2%
Hydraulic Energy	31%	26%	26%	27%	26%	18%	18%	18%
Biomass Energy	1%	6%	7%	9%	14%	3%	8%	9%
Fossil Energy	58%	30%	30%	30%	30%	36%	31%	31%
Nuclear Energy	6%	22%	17%	15%	17%	20%	31%	30%
Total Energy	100%	100%	100%	100%	100%	100%	100%	100%

E R O I_{T}^{[s]} = \frac{\sum (P_{g i}^{[s]} \cdot U L_{g i} \cdot η_{g i} \cdot L F_{g i})}{\sum (\frac{P_{g i}^{[s]} \cdot U L_{g i} \cdot η_{g i} \cdot L F_{g i}}{E R O I_{g i}^{[s]}})}

E R O I_{T}^{[s]} = \frac{\frac{\sum (P_{g i}^{[s]} \cdot U L_{g i} \cdot η_{g i} \cdot L F_{g i})}{P_{T}^{[s]}}}{\sum (\frac{P_{g i}^{[s]}}{P_{T}^{[s]}} \cdot \frac{U L_{g i}}{E R O I_{g i}^{[s]}} \cdot η_{g i} \cdot L F_{g i})} = \frac{\sum (A P_{g i}^{[s]} \cdot U L_{g i} \cdot η_{g i} \cdot L F_{g i})}{\sum (A P_{g i}^{[s]} \cdot R T_{g i}^{[s]} \cdot η_{g i} \cdot L F_{g i})}

C O_{2}_{T}^{[s]} [\frac{C O_{2} E Q}{M W}] = 720 \cdot \sum_{g i = 1}^{g i = 6} (e f_{g i} \cdot η_{g i} \cdot L F_{g i} \cdot A P_{g i}^{[s]} \cdot (1 + \frac{1}{E R O I_{g i}^{[S]}}))

C^{[s]} [U S D] = \frac{C a s h F l o w^{[s]}}{{(t_{d} + 1)}^{S}} = \frac{2.3 \cdot 10^{5}}{{(1 + t_{d})}^{s}} \cdot \sum_{1}^{6} (C R F_{g i} \cdot \frac{P_{g i}^{[s]} \cdot U L_{g i}^{[s]} \cdot η_{g i} \cdot L F_{g i}}{E R O I_{g i}})

C^{[s]} [\frac{U S D}{M W}] = \frac{2.3 \cdot 10^{5}}{{(1 + t_{d})}^{s}} \cdot \sum_{1}^{6} (C R F_{g i} \cdot \frac{P_{g i}^{[s]} \cdot U L_{g i}^{[s]} \cdot η_{g i} \cdot L F_{g i}}{E R O I_{g i} \cdot P_{T}^{[s]}})

C^{[s]} [\frac{U S D}{M W}] = \frac{2.3 \cdot 10^{5}}{{(1 + t_{d})}^{s}} \cdot \sum_{1}^{6} (C R F_{g i} \cdot \frac{A P_{g i}^{[k]} \cdot U L_{g i}^{[s]} \cdot η_{g i} \cdot L F_{g i}}{E R O I_{g i}})

\frac{A Δ P_{g i}^{[s]}}{A P_{g i}^{[s]}} = \frac{Δ P_{g i}^{[s]} / Δ P^{[s]}}{P_{g i}^{[s]} / P_{T}^{[s]}} = \frac{Δ P_{g i}^{[s]}}{Δ P^{[s]}} \cdot \frac{P_{T}^{[s]}}{P_{g i}^{[s]}}

f_{1} (s, s') = \{\begin{matrix} 0, \\ E_{1}, \\ 0, \end{matrix} \begin{matrix} s \int [0, s' + T_{i n v} + T_{p r}] \\ s \int [s' + T_{i n v} + T_{p r}, s' + T_{i n v} + T_{p r} + U L] \\ s \int [s' + T_{i n v} + T_{p r} + U L, 180] \end{matrix}

f_{2} (s, s') = \{\begin{matrix} 0 \\ E_{2} \cdot N (s' + \frac{T i n v}{2} + T_{p r}, \frac{T_{i n v}}{6}) \\ 0 \end{matrix} \begin{matrix} s \int [0, s' + T_{p r}] \\ s \int [s' + T_{p r}, s' + T_{p r} + T_{i n v}] \\ s \int [s' + T_{p r} + T_{i n v}, 180] \end{matrix}

f_{3} (s, s') = \{\begin{matrix} 0, \\ E_{3} \cdot e^{- r \cdot (s - s')} \\ 0, \end{matrix} \begin{matrix} s \int [0, s'] \\ s \int [s', s' + T_{p r}] \\ s \int [s' + T_{p r}, 180] \end{matrix}

U_{1}^{[s]} = E R O I_{T}^{[s]} = \frac{\sum (A_{P_{g i}}^{[s]} \cdot U L_{g i}^{[s]} \cdot η_{g i}^{[s]} \cdot L F_{g i}^{[s]})}{\sum (A_{P_{g i}}^{[s]} \cdot R T_{g i}^{[s]} \cdot η_{g i}^{[s]} \cdot L F_{g i})}

U_{2}^{[s]} = C O_{2}_{T}^{[s]} [\frac{t C O_{2} E Q}{M W}] = 720 \cdot \sum_{g i = 1}^{g n} (e f_{g i} \cdot A P_{g i}^{[k]} \cdot η_{g i} \cdot L F_{g i} \cdot (1 + \frac{1}{E R O I_{g i}^{[k]}}))

U_{3}^{[s]} = C_{s} [\frac{U S D}{M W}] = \frac{2.3 \cdot 10^{5}}{{(1 + t_{d})}^{s}} \cdot \sum_{1}^{g n} (F R C \cdot \frac{A P_{g i}^{[k]} \cdot U L_{g i}^{[k]} \cdot η_{g i} \cdot L F_{g i}}{E R O I_{g i}})

μ_{m}^{[s]} = \{\begin{matrix} a, U_{m}^{L o w} \geq U_{m} \\ {(\frac{U_{m}^{U p} - U_{m}}{U_{m}^{U p} - U_{m}^{L o w}})}^{E W_{m}}, U_{m}^{L o w} \leq U_{m} \leq U_{m}^{U p} \\ 1 - a, U_{m}^{U p} \leq U_{m} \end{matrix}

[1] * camargof@frcu.utn.edu.ar

Brasil

Brasil

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

Abstract

Paper aims

Originality

Research method

Main findings

Implications for theory and practice