Assessment of renewable energies based on fuzzy and qualitative multi-criteria decision making

Renewable energies have become a crucial issue in energy sector research due to their increasingly positive impact on sustainability. Current studies focus on the impact of existing sources of renewable energies, as well as their comparison and possible combinations. The assessment and selection of the most suitable types of energy in a geographical area is a complex problem, involving technical, economic, environmental, political, and social criteria. To establish a methodology to select the best option depending on different criteria in each scenario, this paper compares two multi-criteria decision making (MCDM) approaches based on linguistic label assessment for renewable energies. The first approach consists of a modified fuzzy Topsis methodology introduced by Kaya and Kahraman in 2011. The second approach, introduced by Agell et al. in 2012, is based on qualitative reasoning techniques for ranking multi-attribute alternatives in group decision-making with linguistic labels. This second methodology has achieved interesting results in various managerial applications, but has not yet been applied in the energy sector. An example, based on data provided in a paper by Kaya and Kahraman in 2011, is used to illustrate the mechanisms employed in both approaches and analyse their similarities and differences. We consider seven energy alternatives, six renewable (solar, wind, nuclear, biomass, hydraulic, and combined heat and power) and one conventional. Both MCDM methods are performed on the basis of nine criteria: efficiency, energy efficiency, investment cost, operation and maintenance cost, NOX emission, CO2 emission, land use, social acceptability, and job creation { as conveniently weighted by a group of three experts using the analytic hierarchy process (AHP). The obtained results are presented and analysed using both methods in five scenarios.