Akademik Veri Yönetim Sistemi
Journal of Information Science and Engineering, cilt.38, sa.5, ss.923-935, 2022 (SCI-Expanded)
© 2022 Institute of Information Science. All rights reserved.Hydropower is one of the most efficient renewable energy sources for the sustainability of the environment if the plant location is well decided. The plant location should satisfy different criteria emerged by a wide range of criteria, consisting of law, environment, and the expectations of the investors and residents. Some of these criteria can be conflicting. Some of them may also be in a relationship with each other. Moreover, they may be evaluated in a system that contains uncertainty consisting of a lack of information, impreciseness in the data, and human hesitancy. These aspects can be a powerful effect on the location selection of the hydropower plants and are considered in mathematical formulations. So, the problem can be considered as a multi-criteria decision-making (MCDM) problem under uncertainty. In this study, by considering the types of uncertainties, impreciseness of the available data, and hesitancy of the experts, an integrated MCDM methodology consisting of DEMATEL, cognitive mapping, and TOPSIS methods has been extended based on hesitant fuzzy z-numbers. Then the proposed methodology has been applied for the evaluation of potential locations for hydropower plants in Turkey. For this aim, a hierarchical structure consisting of twenty-nine criteria and four alternative locations has been determined for the assessment by combining literature analysis and expert knowledge. In the first stage, the criteria “Availability of Water”, “Annual flow”, “Technology”, “Capacity”, and “Annual energy production” have been demonstrated as the most influential criteria for location selection. Then, based on the z-number fuzzy TOPSIS method, the most appropriate alternative for the construction location has been determined in Turkey. The findings have been checked in terms of validation and flexibility of the given decisions by applying sensitivity and comparative analyses.