A multi-criterion decision making method for renewable energy storage technology selection with incomplete evaluation information

Selecting renewable energy storage technologies (RESTs) requires experts with knowledge in different fields to evaluate RESTs under different criteria. However, specialists are usually proficient in a particular field and thus are difficult to provide complete evaluation information of alternatives on all criteria. Besides, different decision makers (DMs) have different risk attitudes on potential risks and benefits. This study proposes a multi-criterion decision making model that considers the causality between criteria, the incomplete evaluation information from specialists, and different risk attitudes of DMs. First, the DEMATEL is used to analyze the causality between criteria and elicit criteria weights, and the interpretive structural modeling is utilized to construct the hierarchical relationship diagram of criteria. Based on the causality of criteria, a Bayesian network is constructed to infer the missing evaluation information of alternatives under the criteria unfamiliar to specialists. Alternatives are then ranked by combining the improved cumulative prospect theory and the combined compromise solution method. An illustrative example is given to validate the applicability of the proposed model. Sensitivity analysis and simulation experiment are given to explore the influence of DMs' risk attitudes to loss and gain on the ranking of alternatives.