Sustainability prioritization of energy systems under hybrid information and missing information based on the improved grey relational analysis

Abstract Life cycle sustainability of energy systems has received more and more attentions recently. In order to make an accurate comparison of the sustainability performance of different energy systems and promote the decision-making process, various prioritization methods of energy systems were developed. However, the lack of enough data for decision-making usually limits the accuracy of the prioritization. On the one hand, the decision-makers can only collect multiple types of data resources with hybrid information, for example, data in the formats of crisp numbers, interval numbers, and fuzzy numbers. On the other hand, some information for certain alternatives with respect to certain criteria is hard to be obtained. Therefore, it is of vital importance to achieve sustainability-oriented prioritization of energy systems under hybrid information and missing information. This study aims at developing a prioritization framework for energy systems ranking with missing information and hybrid information. An improved Grey Relational Analysis (GRA) is extended from the classical GRA method to handle hybrid information in this study. An innovative method to quantify linguistic expressions is proposed to deal with missing information. A hypothetical case study regarding electricity generation scenarios selection was used to evaluate the feasibility of this proposed framework. Sensitivity analysis was also conducted, and the results showed that the iGRA-MH is feasible in handling hybrid and missing information and it performs more stable than other multi-criteria decision-making models.