Two-stage site selection of hydrogen refueling stations coupled with gas stations considering cooperative effects based on the CRITIC-ITFAHP-MABAC method: A case study in Beijing

Powering hydrogen-fueled vehicles (HFVs) through hydrogen electrolysis using excess sustainable energy is an effective way to move towards sustainable development. Hydrogen refueling stations (HRSs) coupled with gas stations (GSs) can effectively integrate and utilize the resources in existing GSs, improve operational efficiency, and promote the cooperative income of the two. Aiming at the lack of cooperative effects between gas and hydrogen considered in the current site selection process for HRSs, a general criterion establishment process with refined measurement system are proposed to determine a more comprehensive criterion system, including the cooperative criteria and the precise calculation formula of each criterion. Based on these, a new two-stage site selection framework for HRSs is established. The first stage offers eligible alternatives through veto criteria. Then in the second stage, the Criteria Importance Through Intercriteria Correlation (CRITIC) and Improved Triangular Fuzzy Analytic Hierarchy Process (ITFAHP) methods are used to determine the objective weights and subjective weights of the criteria, and the results are combined according to the minimum deviation estimation (MDE). The rankings of alternatives are finally determined through the Multi-Attributive Border Approximation Area Comparison (MABAC) method. Three scenarios are proposed for further analysis in the case study of Beijing, China. The results show that the two-stage site selection framework with new criterion system makes the evaluation results more differentiated. When the importance coefficient of subjective weights reflecting the importance of the criterion in practice changes from 0 to 1, the score increase of the best alternative A5 (Zhoujiaxiangbei) is 49.14% higher than that of the second-ranked alternative, indicating that this alternative is more in line with people's perception of a good selection. The analysis results also further verify the rationality and robustness of the decision-making framework.