Evaluation of renewable energy sources in China using an interval type-2 fuzzy large-scale group risk evaluation method

As one of the most effective ways to alleviate energy crisis and environmental pollution, the renewable energy sources (RESs) have received increasing attention. Different RESs enjoy different characteristics and are suitable for different scenarios, thus it is essential to evaluate them before installation. Due to the increasing complexity of reality, the RESs evaluation usually involves various risks and large-scale group decision makers. To manage these risks and decision makers, this paper proposes an interval type-2 fuzzy large-scale group risk evaluation method. First, the interval type-2 fuzzy sets (IT2FSs) are employed to encode the qualitative information provided by the decision makers. Then, a new clustering approach integrating consensus reaching model and risk measurement model is developed to manage the decision makers and enhance the evaluation efficiency. After the clustering process, the selection procedure is activated and an interval type-2 fuzzy centroid-based ranking method is presented to rank the candidate RESs. Finally, a case study in China is provided to illustrate the effectiveness of the proposed method and comparisons are also made to verify the advantages. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes an interval type-2 fuzzy large-scale group risk evaluation method, which encodes qualitative information provided by decision makers using interval type-2 fuzzy sets and develops a new clustering approach to manage decision makers and enhance evaluation efficiency. Through clustering and selection procedures, a centroid-based ranking method for candidate RESs is presented.