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 |
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 |
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 |
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 |
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 |
|