Prioritizing Critical Design Requirements for Human-Centered AI-Supported Power Control Systems in Smart Energy Infrastructure: A CSH–FUCOM Framework

Document Type : Research Article

Author

Associate Professor, College of Farabi, University of Tehran, Iran

10.22091/jaem.2026.17097.1058

Abstract

The increasing use of artificial intelligence in power-system control rooms requires design approaches that address technical performance alongside human authority, governance, accountability, and stakeholder participation. This study develops and applies an exploratory sequential mixed-methods framework that combines Critical Systems Heuristics (CSH) with the Full Consistency Method (FUCOM) to identify and prioritize context-specific design criteria for human-centered AI-supported power control systems. In the qualitative phase, semi-structured interviews were conducted with twenty experts from the Iranian electricity sector. The interview data were analyzed through a structured coding process involving open, axial, and selective coding, used as analytical techniques rather than as a full grounded theory methodology. The resulting categories were then interpreted through the twelve boundary questions of CSH. The analysis generated 286 initial codes, 84 consolidated concepts, 21 axial categories, and 12 context-specific boundary-based criteria. In the quantitative phase, eighteen experts ranked and compared the twelve criteria using FUCOM. The highest-ranked criteria were human decision authority, representation and accountability, and the integration of valid knowledge sources. The results suggest that experts assign substantial importance to governance, traceability, multidisciplinary knowledge, and the preservation of human responsibility in safety-critical AI-supported environments. The proposed framework contributes a structured method for connecting critical stakeholder analysis with quantitative prioritization. Because the empirical findings are based on a relatively small expert panel from one national context, the resulting priorities should be interpreted as context-sensitive rather than universally generalizable.

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Advances in Energy and Materials Research

Articles in Press, Accepted Manuscript
Available Online from 25 August 2026

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