Issue |
E3S Web Conf.
Volume 327, 2021
26th Scientific Conference on Power Engineering and Power Machines (PEPM’2021)
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Article Number | 02004 | |
Number of page(s) | 8 | |
Section | Renewable Energy and Environmental Protection | |
DOI | https://doi.org/10.1051/e3sconf/202132702004 | |
Published online | 22 November 2021 |
Renewable energies projects selection: block criteria systematization with AHP and Entropy-MOORA methods in MCDM
Bulgarian Academy of Sciences, Institute of Information and Communication Technologies, Dep. “Information Processes and Decision Support Systems”, akad. Georgi Bonchev srt., bl. 2, 1113 Sofia, Bulgaria
* Corresponding author: petrovindex@gmail.com
EU countries face two major challenges in the energy sector – the control of carbon emissions and the high dependence on imported primary energy sources. Both European Commission and national governments actively develop, update and upgrade strategies and plans for the energy sector. They aim to ensure the gradual reaching of consecutive targets for increasing the weight of Renewable Energies (RE) in the European primary energy mix. The selection of Renewable Energy investment projects in these sectors involves different groups of experts who have to consider the specifics of different countries, regions, and technologies. The Decision-making process becomes more complicated, as in addition to political goals it has to ensure the efficiency and competitiveness of European economies. Several conflicting objectives have to be balanced with complex social, economic, technological, and environmental factors. Our study explores the application of combined methods and evaluation techniques for improving the assessment and selection of prospective RE projects. We implement an innovative integration of the Analytical Hierarchy Process (AHP) for block criteria systematization combined with objective Shannon Entropy in the MOORA evaluation.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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