This study assessed ten alternatives, comprising nine power generation technologies and Battery Energy Storage Systems (BESS), using a combined hybrid approach based on group Multi-Criteria Decision-Making (MCDM) methods. Specifically, AHP was employed for determining criteria weights, while fuzzy VIKOR was utilised for ranking the alternatives. Six electricity sector experts evaluated each technology, organised within a hierarchical decision model that included four main criteria: economic, environmental, technical, and social, along with 13 subcriteria. To mitigate subjectivity in criteria weights stemming from diverse expert backgrounds, a consensus technique was implemented post-AHP. Fuzzy VIKOR was employed to address uncertainty in expert ratings. The findings revealed a significant preference towards renewable technologies, with Photovoltaic (PV) and Wind at the forefront, whereas Coal occupied the lowest position. A validation process was conducted using BWM for criteria weights and fuzzy TOPSIS for ranking alternatives. This hybrid soft computing method’s key contributions include its modular design, allowing for the sequential determination of criteria weights, followed by the calculation of alternative rankings, fostering interactive and collaborative evaluations of various energy mixes by expert groups. Additionally, the study evaluated three emerging energy technologies: BESS, Small Modular Nuclear Reactors (SMRs), and Hydrogen, highlighting their potential in the evolving energy landscape.
Rivero-Iglesias et al. (Tue,) studied this question.