An integrated fuzzy multi-energy transaction evaluation approach for energy internet markets considering judgement credibility and variable rough precision

Energy internet is an effective way to fulfill carbon neutrality, since it has the advantages of high-efficiency energy utilization. As an important part in energy internet market, multi-energy transaction reflects complementarity of multi-energy sources and provides guidance for future development. However, there still lacks an effective index system for multi-energy transaction evaluation and a reliable method to flexibly handle the subjectivity and vagueness during the evaluation process. In this paper, an effective index system and its extraction method and a comprehensive evaluation approach are proposed to reliably assess the multi-energy transaction performance of the energy internet market. Firstly, an interval type-2 fuzzy decision-making trial and evaluation laboratory (IT2F DEMATEL) method and λ-adversarial interpretive structural modelling (λAISM) approach are combined to extract a 14-indicator index system, the proposed method not only investigates the interrelationship between the indicators but also calculates the influence degree between them. Secondly, to control the vagueness and uncertainty involved in expert judgement, a novel non-deterministic method, combining fuzzy comprehensive evaluation and credibility-based analytic hierarchy process (AHP) with variable rough precision theory, is developed to evaluate the multi-energy transaction performance. Finally, two real-world demonstration projects in northern and southern China are investigated for case studies. The evaluation score of the multi-energy transaction in DZZ town has improved by 25.77 from 2018 to 2021, reaching a “good” performance of 78.09; while the score of XB town has improved by 19.34, reaching an “excellent” performance of 84.32 in 2021. Comparative analysis shows that the extracted index system contains the minimum required information, and the proposed evaluation method can effectively handle the linguistic imprecision and cognition vagueness caused by expert judgements.