Data-Informed Evaluation Framework for Integrated Energy Systems: Insights from Power, Process Heat, and Hydrogen Production Applications

Selecting suitable technologies for integrated energy systems (IESs) can be likened to an apples-to-oranges comparison, given the heterogeneous factors at stake. Consequently, past research has often employed multicriteria decision analysis (MCDA) methods with a mixture of qualitative and quantitative criteria. MCDA provides a systematic evaluation of the diverse preferences and performance metrics associated with alternative solutions. While the method proves effective in handling the intricate interplay of criteria, the resulting rankings and scores can vary from study to study. As a result, decision makers frequently find it challenging to establish clear connections between specific criteria and the resulting scores, as the transformation of criteria into ordinal scores results in a substantial loss of information. To address this challenge, we introduce a data-informed IES evaluation framework that offers comprehensive, interpretable, and traceable evaluations backed by quantifiable rationale. First, we identified key IES evaluation criteria from a decade of literature, focusing on relevant IES applications in power, process heat, and hydrogen production. Next, we established thresholds by applying change-point analysis, categorizing the preferences of decision makers into distinct utility functions. To demonstrate the impact of our framework, we conducted case studies on six reactor designs (AP1000, NuScale, BWRX-300, Xe-100, eVinci, iMSR) for the three applications. Our methodology mitigates the subjective bias inherent in traditional scoring approaches by using data-driven thresholds. The framework standardizes evaluations across different stakeholder preferences to guide capital-intensive energy initiatives, while explicitly accounting for uncertainty rather than single-point estimates.