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Assessment of Nuclear Energy Competiveness Using a Multi-Criteria Fuzzy Approach

Assessment of Nuclear Energy Competiveness Using a Multi-Criteria Fuzzy Approach

Fausto Cavallaro
Copyright: © 2013 |Volume: 2 |Issue: 1 |Pages: 16
ISSN: 2160-9500|EISSN: 2160-9543|EISBN13: 9781466631748|DOI: 10.4018/ijeoe.2013010102
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MLA

Cavallaro, Fausto. "Assessment of Nuclear Energy Competiveness Using a Multi-Criteria Fuzzy Approach." IJEOE vol.2, no.1 2013: pp.21-36. http://doi.org/10.4018/ijeoe.2013010102

APA

Cavallaro, F. (2013). Assessment of Nuclear Energy Competiveness Using a Multi-Criteria Fuzzy Approach. International Journal of Energy Optimization and Engineering (IJEOE), 2(1), 21-36. http://doi.org/10.4018/ijeoe.2013010102

Chicago

Cavallaro, Fausto. "Assessment of Nuclear Energy Competiveness Using a Multi-Criteria Fuzzy Approach," International Journal of Energy Optimization and Engineering (IJEOE) 2, no.1: 21-36. http://doi.org/10.4018/ijeoe.2013010102

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Abstract

In Italy, nuclear power is currently on the agenda of the national government. After abandoning nuclear energy 20 years ago, Italy has not had any direct experience since. Besides its social acceptance and the problem of handling radioactive waste, the economic appraisal of nuclear energy compared to coal and gas is the most controversial item in the current debate on energy resources. Unfortunately, the figures published in the literature comparing the competitiveness of nuclear energy against fossil fuels differ wildly. This inconsistency demonstrates the uncertainties in the evaluation process. This paper develops an approach based on fuzzy-sets to handle the uncertainty of nuclear power costs. The underlying aim is to appraise the economic advantages of nuclear power compared with other traditional energy sources, namely coal and natural gas. Having illustrated the general state of the art of nuclear power in terms of production, installed power and economics, a more detailed evaluation follows to select the most important economic and financial parameters involved in calculating the industrial cost (overnight cost, O&M, fuel, etc.) to then build the fuzzy functions and process them using the fuzzy TOPSIS method in order to obtain a final ranking of alternatives analyzed.

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