In the quest to harness the power of wind, researchers have developed a novel approach to tackle one of the energy sector’s most pressing challenges: site selection for wind power plants. A recent study published in the journal *Mathematics* introduces a sophisticated algorithm that could revolutionize how we evaluate and choose locations for renewable energy projects.
At the heart of this research is Bibhuti Bhusana Meher, a scientist from the Department of Engineering Sciences at the Atal Bihari Vajpayee Indian Institute of Information Technology and Management in Gwalior, India. Meher and his team have pioneered a method that leverages trapezoidal-valued intuitionistic fuzzy numbers (TrVIFNs) to model the inherent uncertainties and imprecision in real-world data. “This approach allows us to handle the complexities and ambiguities that are often present in decision-making processes for renewable energy projects,” Meher explains.
The study introduces several new aggregation operators, including the TrVIF Aczel-Alsina weighted averaging operator, the TrVIF Aczel-Alsina ordered weighted averaging operator, and the TrVIF Aczel-Alsina hybrid averaging operator. These operators provide a flexible and robust framework for evaluating multiple criteria simultaneously. “By incorporating these operators, we can better account for the various factors that influence the selection of a wind power plant location, such as wind speed, land availability, environmental impact, and economic considerations,” Meher adds.
One of the key innovations in this research is the development of a new algorithm called the ‘three-stage multi-criteria group decision-making’ method. This algorithm uses the newly proposed TrVIF-MEREC method to obtain criteria weights, providing a more nuanced and accurate evaluation of potential sites. Additionally, the study introduces a modified version of the Weighted Aggregated Sum Product Assessment (WASPAS) method, tailored for the trapezoidal-valued intuitionistic fuzzy environment, known as TrVIF-WASPAS.
The practical implications of this research are significant. By providing a more precise and comprehensive evaluation framework, the proposed method can help energy companies make better-informed decisions about where to invest in wind power plants. This, in turn, can lead to more efficient and cost-effective renewable energy projects, ultimately accelerating the transition to a sustainable energy future.
The study also includes a case study on location selection for a wind power plant project, demonstrating the efficacy of the proposed method. Sensitivity analysis is conducted to examine the robustness of the algorithm under different parameter values, and comparisons are made with existing methods to highlight the advantages of the new approach.
As the energy sector continues to evolve, the need for sophisticated decision-making tools becomes increasingly apparent. This research offers a promising solution, one that could shape the future of renewable energy site selection and contribute to a more sustainable and energy-efficient world. “Our goal is to provide decision-makers with the tools they need to navigate the complexities of renewable energy projects and make choices that benefit both the environment and the economy,” Meher concludes.
Published in the journal *Mathematics*, this groundbreaking study not only advances the field of multi-criteria decision-making but also paves the way for more informed and strategic investments in the renewable energy sector.