Choosing the best site for wind and solar with fuzzy logic

An international research team is proposing a fuzzy logic approach for optimal site selection of large-scale wind, solar and hybrid wind-solar power projects. The new methodology is based on criterial components for energy optimization through climatological, topographic and human factors.

Researchers from the University of Mauritius and Australia’s Curtin University have developed a new methodology based on fuzzy logic to determine the best location for utility-scale wind, solar and hybrid wind-solar power projects.

Although it is based on mathematical concepts, fuzzy logic is known for providing clear solutions to complex problems, as it somehow reflects human thinking and decision making. While conventional logic works on precise input and produces a definite output like true or false and “yes” or “no”, fuzzy logic tends to include all range of possibilities between “yes” or “no”, like for example “certainly yes” or “possibly not”, among more others.

The proposed analytical framework, described in the study “Identification of optimal wind, solar and hybrid wind-solar farming sites using fuzzy logic modelling,” published in the ScienceDirect site, is based on criterial components for energy optimization through climatological, topographic and human factors.

The researchers specified that the human factors are based on the proximity of the settlement areas and grid transmission lines to the hybrid wind and solar farm, public recreational area and agricultural land, while the climatological factors are based on the wind and solar resource assessments. As for the topographic factors, they depend on the appropriate slope angle of the terrain under investigation.

The model represents all of the criterial components as fuzzy sets to evaluate the best project location. “A triangular membership function was used for both solar and wind parameters as they are reported to achieve better performance accuracies as compared to other membership functions for variable parameters,” the researchers stated. An optimization algorithm has been integrated in a MATLAB programming environment to optimize the parameters of the membership functions belonging to the different fuzzy sets.

The scientists said their method is able to provide a cheaper, fast and accurate solution for site identification of hybrid wind-solar projects, as it is able to considerably reduce the time needed for implementing a thorough analysis of a site. “The method employed does not have a lot of drawbacks apart from the fact that it requires good computational resources and relies on extensive computational memory to perform the geographic information system (GIS) processing and amalgamation of the different influential factors,” they wrote. “The findings of this study aim to guide energy and urban planners to better identify optimum sites for wind, solar and hybrid wind-solar farm construction whilst making optimal use of land resources to achieve both sustainable dimensions and energy economic resilience,” they concluded.

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