Selecting the Optimal Renewable Energy Using Multi Criteria Decision Making

Article excerpt

1. Introduction

Renewable energy is recognized as a key resource for future life and plays a significant role in supplying energy and reducing air pollutants and greenhouse gas emissions. Main renewable energy resources are (Kaltschmitt et al. 2007): (i) solar radiation, (ii) wind energy, (iii) hydropower, (iv) photosynthetically fixed energy, and (v) geothermal energy. In 2009, about 16% of global final energy consumption comes from renewable energies, with 10% coming from traditional biomass, 3.4% from hydropower, and 2.6% from all other renewable energies (REN21 2011). This is due to the negative effect of fossil fuels on the environment, the precarious nature of dependency on fossil fuel imports, and the advent of renewable energy alternatives (Cristobal et al. 2011). These are environment-friendly and capable of replacing conventional sources in a variety of applications at competitive prices (Haralambopoulos, Polatidis 2003; Aras et al. 2004).

The selection of different energy investment projects is a multi criteria decision making (MCDM) problem, because various criteria should be analyzed and considered that are often in conflicting with each other. These criteria affect the success of a renewable energy project. For instance, two criteria that could be employed in renewable energy selection might be power and operation and maintenance costs. There are two conflicting criteria because an attempt in order to enhance power possibly causes a growth in operation and maintenance costs. According to the capability and effectively of MCDM and the need to incorporate social, economic, technological, and environmental considerations in energy issues, there is a vast MCDM literature on energy problems.

Beccali et al. (2003) applied the ELECTERE (ELimination Et Choix Traduisant la Realite or Elimination and Choice Translating Reality) method to determine regional level for the diffusion of renewable energy technology. Heo et al. (2010) used fuzzy analytical hierarchy process (FAHP) to analyze the assessment factors for renewable energy dissemination program evaluation. Kahraman et al. (2010) applied a comparative analysis for multi attribute selection among renewable energy alternatives using fuzzy axiomatic design and FAHP.

Evans et al. (2009) employed sustainability indicators to assess renewable energy technologies. They indicators include price of generated electricity, greenhouse gas emissions during the full life cycle of the technology, availability of renewable sources, efficiency of energy conversion, land requirements, water consumption and social impacts. In this study, each indicator was assumed to have equal importance to sustainable development and utilized to rank the renewable energy technologies against their impacts.

Lee et al. (2009) utilized the FAHP technique in order to prioritize energy technologies against high oil prices. The results show that building technology is the most preferred technology in the sector of energy technologies against high oil prices, and the coal technology and transportation technology are located in the second and third place, respectively.

Cavallaro (2005) set out the application of PROMETHEE to assess sustainable energy options. Oberschmidt et al. (2010) developed the modified PROMETHEE approach for assessing energy technologies. Sola et al. (2011) proposed a multi-criteria model using the PROMETHEE II method, with the aim of ranking alternatives for induction motors replacement. Lee et al. (2011) used a fuzzy AHP approach to prioritize the weights of hydrogen energy technologies in the sector of the hydrogen economy. Virtanen (2011) developed the PROMETHEE II method to select the optimal energy system for buildings and districts. In order to achieve the renewable energy policy goals, Shen et al. (2011) showed how different policy goals lead to corresponding renewable energy sources. In this paper, the relative importance of each goal was evaluated by using AHP. …