Rapid development in energy systems results in a choice question for decision-makers. Since many conflicting criteria are involved in the decision process, multi-criteria decision-making (MCDM) could be used. To make the decision process more suitable and reliable in reality, this paper introduces a sustainability prioritization framework with hybrid-data consideration via the integration of fuzzy group Delphi (FGD), fuzzy best-worst method (FBWM), and fuzzy best-worst projection (FBWP). In which, the triangular fuzzy number is incorporated into the framework to address the hybrid-data issues under uncertainty; the FGD rationally identifies the key criteria by implementing group consensus decisions, the FBWM accurately assigns the weights to the criteria by preserving the consistency in cognitive processes under uncertainty, and the FBWP rigorously ranks the alternatives by incorporating the absolute and relative performances regarding the multi-criteria. An illustrative case regarding ammonia production systems was studied, while the effectiveness and advantages of the framework were validated by results comparisons. In summary, this work makes methodological contributions to the sustainability prioritization of energy systems, including properly considering the uncertain hybrid data in decision-making, rationally selecting the key criteria and accurately determining their weights, and reliably ranking the alternative systems in the context of sustainability.

能源系统的快速发展给决策者带来了选择问题。由于决策过程涉及许多相互矛盾的标准，因此可以使用多标准决策（MCDM）。为了使决策过程在现实中更合适，更可靠，本文通过集成模糊群德尔菲（FGD），模糊最优方法（FBWM）和模糊最优估计，引入了一种考虑混合数据的可持续性优先排序框架。 （FBWP）。其中，将三角模糊数纳入框架，以解决不确定性下的混合数据问题；FGD通过执行小组共识决策合理地确定关键标准，FBWM通过在不确定性下保持认知过程的一致性来准确地为标准分配权重，FBWP通过纳入关于多准则的绝对和相对绩效，对备选方案进行严格排名。研究了关于氨生产系统的说明性案例，同时通过结果比较验证了该框架的有效性和优势。总而言之，这项工作为能源系统的可持续性优先排序提供了方法论上的贡献，包括在决策中适当考虑不确定的混合数据，合理选择关键标准并准确确定其权重，以及在可持续性背景下可靠地对替代系统进行排名。结果比较验证了该框架的有效性和优势。总而言之，这项工作为能源系统的可持续性优先排序提供了方法论上的贡献，包括在决策中适当考虑不确定的混合数据，合理选择关键标准并准确确定其权重，以及在可持续性背景下可靠地对替代系统进行排名。结果比较验证了该框架的有效性和优势。总而言之，这项工作为能源系统的可持续性优先排序提供了方法论上的贡献，包括在决策中适当考虑不确定的混合数据，合理选择关键标准并准确确定其权重，以及在可持续性背景下可靠地对替代系统进行排名。