A Policy-Constrained Soft-Computing Framework for Robust Multi-Criteria Energy Scenario Ranking under Weighting Uncertainty

This study was conducted to address the growing need for the simultaneous supply of electricity, heat, and clean hydrogen in the building sector under conditions of uncertainty in decision-makers’ preferences; an issue that is particularly important in Iran due to the high dependence on the grid and the environmental sensitivity of metropolitan areas. The case study was carried out in the city of Tehran (a 90 m² apartment unit), and four residential energy scenarios including Grid, PV–Grid, Wind–Grid, and PV–Wind–Grid were evaluated. The scientific/applied novelty of the present work lies in the development of a “robust and policy-constrained decision-support framework” that, instead of relying on fixed weights, models the uncertainty of criteria weights using Monte Carlo simulation while simultaneously imposing minimum policy significance for key criteria (such as and ); subsequently, the ranking is performed by aggregating the results of six MCDM methods (SAW, TOPSIS, VIKOR, MOORA, COPRAS, WASPAS). The main criteria included COE, COH, ERF, HRF, and CO 2 . Quantitative–comparative results showed that the PV–Grid scenario achieved first rank with a probability of 99.94% and was downgraded to second rank in only 0.06% of cases; similarly, the PV–Wind–Grid scenario obtained second rank with a probability of 99.94% and achieved first rank in only 0.06% of cases. In contrast, the main competition at the lower end of the ranking occurred between Wind–Grid and Grid; such that Wind–Grid held third rank with a probability of 85.20% and fourth rank with 14.80% (and conversely for Grid). This pattern qualitatively indicates that the superiority of solar-based options is structural and robust, whereas decisions regarding weaker options are more sensitive to weighting.