Pythagorean fuzzy engineering economic analysis of solar power plants

Çoban V., Çevik Onar S.

SOFT COMPUTING, vol.22, no.15, pp.5007-5020, 2018 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 22 Issue: 15
  • Publication Date: 2018
  • Doi Number: 10.1007/s00500-018-3234-6
  • Journal Name: SOFT COMPUTING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.5007-5020
  • Istanbul Technical University Affiliated: Yes


The total world energy consumption is rising, and the alternative energy sources are sought to meet this demand. Renewable energy sources have distinctive features that make these sources environmental friendly and increase their share in total energy supply. Renewable energies, which are inexhaustible and renew themselves, are predicted to be the primary energy source for the future. The sun, which is the most important renewable energy source and the source of other energies, is also used for direct and indirect energy generation. In order to realize investments in solar energy systems that require high initial investment, their economic suitability must be assessed appropriately. Life cycle cost (LCC) and levelized cost of energy (LCOE) methods are widely used in economic evaluation and comparison of the large-scale solar energy system. Yet, solar energy investment decisions involve uncertainty and imprecision due to the vagueness in production levels and energy prices. An ample economic analysis should be able to evaluate the uncertainty and consider the dynamic costs and benefits. Pythagorean fuzzy sets are excellent tools for dealing with uncertainty and imprecision inherent in a system. In this study, the Pythagorean fuzzy set theory is applied so that the uncertainties and the opinions of the decision makers are more realistically incorporated into the economic analysis. The proposed Pythagorean LCC and LCOE methods enable dealing with the solar energy investments with fuzzy parameters. Alternative energy systems with different technological features and economic conditions can be more accurately compared using the proposed method.