Catalán Ros, Leyre

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Catalán Ros

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Leyre

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Ingeniería

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ISC. Institute of Smart Cities

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  • PublicationOpen Access
    Design and optimization of thermoelectric generators for harnessing geothermal anomalies: a computational model and validation with experimental field results
    (Elsevier, 2024) Alegría Cía, Patricia; Catalán Ros, Leyre; Araiz Vega, Miguel; Erro Iturralde, Irantzu; Astrain Ulibarrena, David; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
    Thermoelectric generators have been recently proved to be a feasible alternative to harness hot dry rock fields with very promising results transforming the geothermal heat into electricity. This research deepens in the study of these generators, developing a versatile computational model that serves as a tool to design and optimize this type of thermoelectric generators. This tool is important to develop this thermoelectric technology on a large scale, to produce clean and renewable electrical energy especially in the Timanfaya National Park, in Lanzarote (Spain), where some of the most important shallow geothermal anomalies in the world are located, in order to promote self-consumption in this zone. However, it could be employed in other areas with different boundary conditions. The model, based in the finite difference method applied to the thermal-electrical analogy of a geothermal thermoelectric generator, has been validated with the experimental field results of two thermoelectric generators installed in two different zones of geothermal anomalies. It has achieved a relative error of less than 10% when predicting the power and between 0.5–1.6% in the annual energy generation, what makes it a very reliable and useful computational tool. The developed model has been employed for the first time to estimate the electrical energy that could be generated if harnessing the characterized area of anomalies in Lanzarote. Here, given the continuity of geothermal energy, 7.24 GWh per year could be generated, which means annually 1.03 MWh/m2.