Educational Tool to Estimate and Analyze the Wave Energy Potential

Authors

Keywords:

Bigdata, microgrid, ocean waves, renewable source, sustainability, wave energy potential.

Abstract

In order to search new alternatives for electric power generation, the study of the wave energy potential was considered in this work, it deals with the develop for academic and research purposes an executable tool capable of providing the wave energy potential in any point of interest in the coastal zone of Mexico. To develop this tool, the main parameters were used: the significant height of the wave and its period. These parameters were obtained from an easily accessible oceanographic from the National Oceanic and Atmospheric Administration (NOAA) over a period of 6 years. The large database was statistically processed to obtain a reference base and applying numerical techniques for curve fitting, it was possible to obtain a characteristic equation to emulate the wave movement, this emulating model allows estimating the wave energy potential as a function of time and most attached to reality. This work contributes in the academic part in the estimation of wave energy potential, their visualization and understanding, to be a tool in the design of microgrids with renewables.

Downloads

Download data is not yet available.

Author Biographies

Iván Abel Hernández Robles, Universidad de Guanajuato, Salamanca, Mexico.

Ivan A. Hernandez Robles (Member, IEEE) received the B.Sc. degree in Electrical Engineering from the Universidad de Guanajuato, Guanajuato, Mexico, in 2002, and the M.Sc. and Ph.D. degrees in Electrical Engineering from the CINVESTAV Guadalajara, Guadalajara, Mexico, in 2005 and 2013, respectively. He has worked in the field of electrical engineering at electrical construction projects and a Design Engineer at EATON-Cooper Power, Guadalajara, for two years with CIATEQ and CONACYT, working on the design and optimization of linear generators and electrical motors. He is currently a Professor with the Department of Electrical Engineering, Universidad de Guanajuato. His research interests are numerical analysis applied to electrical machine design and renewable energies.

Xiomara González Ramírez, Universidad de Guanajuato, Salamanca, Mexico.

Xiomara Gonzalez Ramirez received the B.Sc. degree in Electrical Engineering from the Universidad del Valle, Cali, Colombia, in 2008, and the M.Sc. and Ph.D. degrees in Electrical Power Systems from CINVESTAV Guadalajara, Guadalajara, Mexico, in 2010 and 2015, respectively. She is currently a Professor with the Department of Electrical Engineering, Universidad de Guanajuato, Irapuato-Salamanca, Guanajuato, Mexico. She is specialist in distributed generation and optimization of electric power flows. Her main research lines are the operation and optimization of electric power systems and distributed generation by renewable energies.

Paulina Lizeth Cuevas Muñoz, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV), Guadalajara, Mexico

Paulina L. Cuevas Muñoz, a Mexican PhD student, received the bachelor’s degree in Electrical Engineering from Universidad de Guanajuato in Salamanca Guanajuato in 2020. She received a Master of Science in Electrical Engineering from the Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV) in Guadalajara, Mexico in 2022, and is currently working towards the degree of Science PhD in Electrical Engineering.

References

World Energy Council. (2017). World Energy Trilemma Index 2017: Monitoring the Sustainability of National Energy Systems. London.

Zhou, M. (25 de Julio de 2013). World Energy Consumption to Increase 56% by 2040 Led by Asia. Bloomberg.

Altintas K., Turk T., Vayvay O. (2016), Renewable Energy for a Sustainable Future, Turquía, Marmara Journal of Pure and Applied Sciences.

Oviedo-Salazar, J. L., Badii M. H., Guillen A., Lugo Serrato O. (2015). History and Use of Renewable Energies. Daena: International Journal of Good Conscience.

Casarín, R. S. (2017). Anual report an overview of ocean energy activities in 2017. Ocean Energy Sistems (OES), 12, 19.

Stahl A. (1892). The utilization of the power of occean waves. Trans Am Soc Mech Eng, 13: 438 - 506.

Hayward, J., Behrens, S., McGarry, S., & Osman, P. (2012). Economic modelling of the potential of wave energy. Renewable Energy, 48.

Saket, A., & Etemad-Shahidi, A. (2012). Wave energy potential along the northern coasts of the Gulf of Oman, Iran. Renewable Energy, 40.

Fadaeenejad, M., Shamsipour, R., Rokni, S., & Gomes, C. (2014). New approaches in harnessing wave energy: With special attention to small islands. ELSEVIER: Renewable and Sustainable Energy Reviews.

Fernández Díez, P. F. (2005). Energía de las olas. Universidad de Cantabria: Departamento de Ingeniería eléctrica y energética. Obtenido de Energía del mar: http://es.pfernandezdiez.es/libro/?id=5

J. Falnes, “Ocean waves and oscillating systems” En ISBN 0-511-03093-2. Inglaterra: Cambridge University press, 2002.

J. Falnes, “A review of wave-energy extraction” Elsevier: Marine Structures, pp. 186 – 200, 2007.

González-Ramírez X., Hernández-Robles I. A. y Barrios-Piña H. (2017). Potencial energético undimotriz en nodos costeros de México, Parte 1: estimación energética, Tecnología y Ciencias del Agua, vol. VIII, 11,10.

Pessanha J. F. M., Melo A. C. G., Caldas R. P and Falcão D. M.(2020) An Approach for Data Treatment of Solar Photovoltaic Generation. IEEE Latin America Transactions, vol. 18, nO. 9, sept 2020.

Gonzalez X., Guzman Cabrera R. Hernandez I.A., Guzman Sepulveda J. R., (2022) Statidistical technique to improve potential estimation for the design of wave generators. Energy Conversion and Management X. ELSEVIER, vol. 14, sept 2022.

MathWorks. (2006). Curve Fitting. Obtenido de MathWorks: https://la.mathworks.com/help/curvefit/curvefitting-app.html.

MathWorks. (2019). Gaussian Models. Obtenido de MathWorks: https://la.mathworks.com/help/curvefit/gaussian.html.

Google My Maps (2016). Mapa de los mares mexicanos. Recuperado de https://www.google.com/maps/d/edit.

Chapra, S. C. (2007). Métodos numéricos para ingenieros Quinta edición. ISBN-13: 978-970-10-6114-5: McGRAW-HILL/INTERAMERICANA EDITORES.

MathWorks. (2019). Fourier Series. Obtenido de MathWorks: https://la.mathworks.com/help/curvefit/fourier.html.

MathWorks. (2019). Exponential Models. Obtenido de MathWorks: https://la.mathworks.com/help/curvefit/exponential.html.

MathWorks. (2019). Sum of Sines Models. Obtenido de MathWorks: https://la.mathworks.com/help/curvefit/sum-of-sine.html.

Castellano A.O., Lago R.D., Bory H., (2022). Programa para determinar los parámetros que caracterizan el oleaje marino y simular su comportamiento. Ingeniería Hidráulica y Ambiental, vol. XLIII, No. 2, Abr-Jun 2022, p. 29-43.

Hernandez-Robles I.A., Gonzalez-Ramirez X., Gomez J., Ramirez J.M., (2021). Wave power assessment for electricity generation with powerbouy system by wave motion emulation modelling. Sustainable Energy Technologies and Assessments, vol. 43, 2021, p. 1-12.

Downloads

Published

2023-12-21

How to Cite

Hernández Robles, I. A., González Ramírez, X., & Cuevas Muñoz, P. L. (2023). Educational Tool to Estimate and Analyze the Wave Energy Potential. IEEE Latin America Transactions, 22(1), 55–62. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/8405

Issue

Vol. 22 No. 1 (2024): Ordinary Issue

Section

Electric Energy