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Sustainable renewable energy system selection for self-sufficient households using integrated fermatean neutrosophic fuzzy stratified AHP-MARCOS approach

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  • Manirathinam, Thangaraj
  • Narayanamoorthy, Samayan
  • Geetha, Selvaraj
  • Othman, Mohd Fairuz Iskandar
  • Alotaibi, Badr Saad
  • Ahmadian, Ali
  • Kang, Daekook

Abstract

Future focus ought to be on developing sustainable cities by cutting down on energy waste, and constructing environments that are carbon neutral. To achieve this objective, it is necessary to develop smart homes with self-sufficient power generation, storage, and consumption based on distributed renewable energy resources with good infrastructure. This energy system should be environmentally, technically, commercially, and socially sustainable. However, none of the alternative energy sources can completely meet these demands, even though wind and solar energy are often the most practical forms of renewable energy. Currently, many studies focused on fusing these two to benefit from their complimentary characteristics. Deploying optimal system of renewable energy on each house is very difficult due to various socio-economical barriers in selection and implementation of such technology leads to formulate respective decision-making problems. The fermatean neutrosophic fuzzy preferences handles problems uncertainty and the stratified target concept approves its ranking sustainability. Since, eight plausible targets were identified for ideal criteria weights which determined hybrid electric system as the best choice out of four possible options for future smart houses. The economical benefits of hybrid system is convincing enough to be taken into account for increased power generating capacity in developing nations. Furthermore, the proposed hybrid fuzzy decision-making technique is quite encouraging for governing authorities or specialists to make forward-thinking judgments on numerous real-world challenges due to its outcome sustainability, accuracy, and consistency.

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  • Manirathinam, Thangaraj & Narayanamoorthy, Samayan & Geetha, Selvaraj & Othman, Mohd Fairuz Iskandar & Alotaibi, Badr Saad & Ahmadian, Ali & Kang, Daekook, 2023. "Sustainable renewable energy system selection for self-sufficient households using integrated fermatean neutrosophic fuzzy stratified AHP-MARCOS approach," Renewable Energy, Elsevier, vol. 218(C).
    • Handle: RePEc:eee:renene:v:218:y:2023:i:c:s0960148123012077
    DOI: 10.1016/j.renene.2023.119292
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    1. Elkadeem, M.R. & Younes, Ali & Sharshir, Swellam W. & Campana, Pietro Elia & Wang, Shaorong, 2021. "Sustainable siting and design optimization of hybrid renewable energy system: A geospatial multi-criteria analysis," Applied Energy, Elsevier, vol. 295(C).
    2. Kumar, Abhishek & Sah, Bikash & Singh, Arvind R. & Deng, Yan & He, Xiangning & Kumar, Praveen & Bansal, R.C., 2017. "A review of multi criteria decision making (MCDM) towards sustainable renewable energy development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 596-609.
    3. Khan, Khalid & Su, Chi Wei & Rehman, Ashfaq U. & Ullah, Rahman, 2022. "Is technological innovation a driver of renewable energy?," Technology in Society, Elsevier, vol. 70(C).
    4. Salim, Ruhul A. & Shafiei, Sahar, 2014. "Urbanization and renewable and non-renewable energy consumption in OECD countries: An empirical analysis," Economic Modelling, Elsevier, vol. 38(C), pages 581-591.
    5. Nigim, K. & Munier, N. & Green, J., 2004. "Pre-feasibility MCDM tools to aid communities in prioritizing local viable renewable energy sources," Renewable Energy, Elsevier, vol. 29(11), pages 1775-1791.
    6. Saraswat, S.K. & Digalwar, Abhijeet K. & Yadav, S.S. & Kumar, Gaurav, 2021. "MCDM and GIS based modelling technique for assessment of solar and wind farm locations in India," Renewable Energy, Elsevier, vol. 169(C), pages 865-884.
    7. Nema, Pragya & Nema, R.K. & Rangnekar, Saroj, 2009. "A current and future state of art development of hybrid energy system using wind and PV-solar: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 2096-2103, October.
    8. Magazzino, Cosimo & Mele, Marco & Schneider, Nicolas, 2021. "A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions," Renewable Energy, Elsevier, vol. 167(C), pages 99-115.
    9. Dilek Temiz Dinç & Ece C. Akdoğan, 2019. "Renewable Energy Production, Energy Consumption and Sustainable Economic Growth in Turkey: A VECM Approach," Sustainability, MDPI, vol. 11(5), pages 1-14, February.
    10. Guo, Jiacheng & Liu, Zhijian & Li, Ying & Wu, Di & Liu, Xuan & Zhang, Shicong & Yang, Xinyan & Ge, Hua & Zhang, Peiwen, 2022. "Thermodynamic performance analyses and optimization design method of a novel distributed energy system coupled with hybrid-energy storage," Renewable Energy, Elsevier, vol. 182(C), pages 1182-1200.
    11. Speidel, Stuart & Bräunl, Thomas, 2016. "Leaving the grid—The effect of combining home energy storage with renewable energy generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1213-1224.
    12. Sadorsky, Perry, 2009. "Renewable energy consumption, CO2 emissions and oil prices in the G7 countries," Energy Economics, Elsevier, vol. 31(3), pages 456-462, May.
    13. Bobinaite, Viktorija & Tarvydas, Dalius, 2014. "Financing instruments and channels for the increasing production and consumption of renewable energy: Lithuanian case," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 259-276.
    14. Lee, Hsing-Chen & Chang, Ching-Ter, 2018. "Comparative analysis of MCDM methods for ranking renewable energy sources in Taiwan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 92(C), pages 883-896.
    15. Alizadeh, Reza & Soltanisehat, Leili & Lund, Peter D. & Zamanisabzi, Hamed, 2020. "Improving renewable energy policy planning and decision-making through a hybrid MCDM method," Energy Policy, Elsevier, vol. 137(C).
    16. Indre Siksnelyte-Butkiene & Edmundas Kazimieras Zavadskas & Dalia Streimikiene, 2020. "Multi-Criteria Decision-Making (MCDM) for the Assessment of Renewable Energy Technologies in a Household: A Review," Energies, MDPI, vol. 13(5), pages 1-22, March.
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    2. Mota, Bruno & Faria, Pedro & Vale, Zita, 2024. "Energy cost optimization through load shifting in a photovoltaic energy-sharing household community," Renewable Energy, Elsevier, vol. 221(C).

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