TLDR: This research introduces a new multi-criteria decision-making method using interval-valued Fermatean fuzzy sets and the Maximizing Deviation approach to select optimal renewable energy sources. It effectively handles uncertainty and partially known information to rank alternatives like hydro, wind, solar, biomass, and geothermal energy, providing robust insights for investment and policy decisions.
Making informed decisions about which renewable energy sources to invest in is a complex challenge, especially given the global push towards sustainable development and reducing carbon emissions. This research paper, titled “Renewable Energy Sources Selection Analysis with the Maximizing Deviation Method,” by Murat Kiri¸ sci, introduces a novel approach to help decision-makers navigate this intricate landscape.
The core problem lies in the multi-criteria nature of selecting renewable energy sources. Factors like economic viability, technical efficiency, environmental impact, and social acceptance all play a crucial role, and these factors often conflict with each other. Traditional decision-making methods sometimes struggle to accurately capture the inherent uncertainties and subjective judgments involved in such evaluations. This is where fuzzy set theory comes into play, offering a way to quantify the vagueness in human thoughts and perceptions.
The study utilizes a sophisticated framework known as the Fermatean fuzzy environment, which is an advanced form of fuzzy sets. This environment allows for a more nuanced representation of uncertainty compared to earlier fuzzy set theories. To determine the importance (weights) of various criteria, the paper proposes an optimization model based on the Maximizing Deviation (MD) method. This method is particularly useful because it assigns higher weights to criteria that show greater differences between alternatives, thus highlighting the most distinguishing factors in the decision process. Conversely, criteria that show little variation among options are given less weight, as they are less influential in differentiating choices.
The proposed method combines the Maximizing Deviation technique with interval-valued Fermatean fuzzy sets, allowing for a robust analysis even when information is partially known or imprecise. The researchers applied this innovative method to the real-world problem of selecting renewable energy sources, considering five alternatives: Geothermal Energy, Bioenergy/Biomass Energy, Solar Energy, Hydroelectric Energy, and Wind Energy. Ten criteria were used for evaluation, spanning economic (investment cost, O&M, electric cost), technical (efficiency, capacity factor, technical maturity), environmental (GHG emission, land use), and social (job creation, social acceptance) aspects.
Four decision-makers, each with extensive experience in the energy sector, provided their assessments. After a series of computations to determine criteria weights and aggregate preferences, the study ranked the renewable energy sources. The results indicated a clear preference: Hydroelectric Energy (S4) emerged as the top choice, followed by Wind Energy (S5), Solar Energy (S3), Bioenergy/Biomass Energy (S2), and finally Geothermal Energy (S1). This ranking, as discussed in the paper, aligns with the current energy mix and policy priorities in many countries, where hydro remains dominant and wind and solar are rapidly growing.
The implications of this research are significant for both managerial and policy-making bodies. From a managerial perspective, the ranking provides clear guidance for investment strategies, risk management, and infrastructure optimization. For instance, the high ranking of hydroelectric energy suggests its robust infrastructure and high production capacity, while the lower ranking of geothermal energy points to its limited widespread applicability and high initial costs. For policymakers, the method offers a tool to enhance energy security, reduce dependence on fossil fuel imports, and formulate climate policies in line with international commitments. It also highlights opportunities for regional development and employment through strategic renewable energy selection.
A crucial aspect of the study is its robustness analysis, which ensures the reliability of the results. By using the IVFF-COPRAS method and testing various scenarios, including removing alternatives, the researchers confirmed that the ranking remained stable, indicating the method’s dependability. This means that even minor changes in input data are unlikely to drastically alter the final decision, providing confidence in the model’s recommendations.
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This research offers a valuable framework for tackling complex multi-criteria decision-making problems, particularly in the critical field of renewable energy selection. By effectively handling uncertainty and providing a clear, robust ranking, it empowers decision-makers to make more strategic and sustainable choices for our energy future. You can find the full research paper here.
