Ranking and aggregation-based multiple attributes decision making method for sustainable energy planning

Abstract

In sustainable energy planning, the selection of a suitable Renewable Energy Sources (RES) for energy supply and evaluation of different RES technologies is a complex decision-making process. This is because there are many conflicting criteria that need to be considered. It becomes more complicated when qualitative data is involved in addition to quantitative data. Previous studies use Multiple Attribute Decision Making (MADM) methods for decision making, which work well with quantitative data but not with qualitative data. There are some MADM methods that can handle with both qualitative and quantitative data but suffer from complex computation burden. It becomes more difficult when more than one MADM method or more than one Decision Maker (DM) need to be considered. Different results will be obtained since different MADM methods or different DMs provide different results. This thesis proposes a new MADM method to overcome the limitations of previous methods. It consists of two parts which are ranking and aggregation techniques. The proposed ranking technique able to deal with quantitative and qualitative data through sorting process according to beneficial and non-beneficial criteria without normalizing the data. Then the proposed aggregation technique able to overcome the problem of different rankings due to different MADM methods or different DMs. The idea is to modify the preference ranking organization method for enrichment evaluations, where a preference index is assigned when comparing two alternatives at one time with respect to their ranking position instead of the criteria. Four case studies are examined to illustrate the effectiveness of the proposed ranking method while three case studies are evaluated to demonstrate the applications of the proposed aggregation method. For verification, Spearman’s rank correlation coefficient is utilized to determine an agreement of the proposed method with the existing MADM methods. The results show the strength of the proposed method as it yields a correlation coefficient of more than 0.87 in all case studies. The results show an excellent correlation with those obtained by past researchers, which specifically prove the applicability of the proposed method for solving sustainable energy planning decision problem.