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Multi-source energy systems analysis using a multi-criteria decision aid methodology

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  • Catalina, Tiberiu
  • Virgone, Joseph
  • Blanco, Eric

Abstract

In this article, an original multi-criteria approach is applied to multi-source systems used for the design and the choice of the optimal alternative. The high number of alternatives and potential solutions when dealing with multi-source systems require a decision support method to be implemented and easy to use. Information data on the economic variables, energy performance and impact on the environment of the systems are presently data which analysis and quantification is difficult. To deal with this high level of complexity and uncertainty, an evaluation approach is needed. The multi-criteria decision support methodology concept is described (ELECTRE III) and then applied for a case study. The decision support algorithm has its bases on the developed models and makes the outranking of possible solutions. It is also shown that multi-criteria analysis can provide a technical–scientific decision-making support that is capable to justify the clearly rank of the alternatives in the renewable energy sector. The use of multi-criteria decision aid for assessing the multi-source systems showed encouraging results and interesting insights.

Suggested Citation

  • Catalina, Tiberiu & Virgone, Joseph & Blanco, Eric, 2011. "Multi-source energy systems analysis using a multi-criteria decision aid methodology," Renewable Energy, Elsevier, vol. 36(8), pages 2245-2252.
    • Handle: RePEc:eee:renene:v:36:y:2011:i:8:p:2245-2252
    DOI: 10.1016/j.renene.2011.01.011
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    References listed on IDEAS

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    13. Govindan, Kannan & Jepsen, Martin Brandt, 2016. "ELECTRE: A comprehensive literature review on methodologies and applications," European Journal of Operational Research, Elsevier, vol. 250(1), pages 1-29.
    14. Seddiki, Mohammed & Bennadji, Amar, 2019. "Multi-criteria evaluation of renewable energy alternatives for electricity generation in a residential building," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 101-117.
    15. Strantzali, Eleni & Aravossis, Konstantinos, 2016. "Decision making in renewable energy investments: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 885-898.
    16. Agudelo, Andrés & Valero, Antonio & Usón, Sergio, 2013. "The fossil trace of CO2 emissions in multi-fuel energy systems," Energy, Elsevier, vol. 58(C), pages 236-246.
    17. Garmabdari, R. & Moghimi, M. & Yang, F. & Lu, J., 2020. "Multi-objective optimisation and planning of grid-connected cogeneration systems in presence of grid power fluctuations and energy storage dynamics," Energy, Elsevier, vol. 212(C).
    18. Jiyeon Kim & Eui-Jong Kim, 2016. "Simplified Method of Optimal Sizing of a Renewable Energy Hybrid System for Schools," Sustainability, MDPI, vol. 8(11), pages 1-16, November.
    19. Mardani, Abbas & Zavadskas, Edmundas Kazimieras & Khalifah, Zainab & Zakuan, Norhayati & Jusoh, Ahmad & Nor, Khalil Md & Khoshnoudi, Masoumeh, 2017. "A review of multi-criteria decision-making applications to solve energy management problems: Two decades from 1995 to 2015," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 216-256.
    20. Štreimikienė, Dalia & Šliogerienė, Jūratė & Turskis, Zenonas, 2016. "Multi-criteria analysis of electricity generation technologies in Lithuania," Renewable Energy, Elsevier, vol. 85(C), pages 148-156.
    21. Chiranjib Bhowmik & Sumit Bhowmik & Amitava Ray, 2021. "Selection of optimum green energy sources by considering environmental constructs and their technical criteria: a case study," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(9), pages 13890-13918, September.
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