Characterization of the wind with the Weibull function for a high Andean zone, Laraqueri - Peru
Published 2022-08-25
Keywords
- Weibull distribution,
- wind power density,
- rose wind,
- wind speed
How to Cite
Abstract
It is important to study the availability of renewable energy and in particular wind power for its evaluation. Therefore, this article analyzes the wind energy potential of a site located in southern Peru (Laraqueri), using wind data from 2020 at a height of 10 meters above ground level. Two numerical methods were used to estimate the parameters of the Weibull distribution function and the power density was calculated for each month. The degree of error of the Weibull function was also calculated with the observed data. It is concluded that the proposed location is appropriate for the generation of low power wind power and the proposed methodology can be used in other places.
References
- Adnan, M., Ahmad, J., Ali, S. F., & Imran, M. (2021). A techno-economic analysis for power generation through wind energy: A case study of Pakistan. Energy Reports, 7, 1424–1443. https://doi.org/10.1016/J.EGYR.2021.02.068
- Akdağ, S. A., & Dinler, A. (2009). A new method to estimate Weibull parameters for wind energy applications. Energy Conversion and Management, 50(7), 1761–1766. https://doi.org/10.1016/j.enconman.2009.03.020
- Alsamamra, H. R., Salah, S., Shoqeir, J. A. H., & Manasra, A. J. (2022). A comparative study of five numerical methods for the estimation of Weibull parameters for wind energy evaluation at Eastern Jerusalem, Palestine. Energy Reports, 8, 4801–4810. https://doi.org/10.1016/J.EGYR.2022.03.180
- Fazelpour, F., Soltani, N., Soltani, S., & Rosen, M. A. (2015). Assessment of wind energy potential and economics in the north-western Iranian cities of Tabriz and Ardabil. Renewable and Sustainable Energy Reviews, 45, 87–99. https://doi.org/10.1016/j.rser.2015.01.045
- Gars, J., Spiro, D., & Wachtmeister, H. (2022). What is the effect of EU’s fuel-tax cuts on Russia’s oil income? https://doi.org/10.48550/arxiv.2204.03318
- He, J. Y., Chan, P. W., Li, Q. S., & Lee, C. W. (2022). Characterizing coastal wind energy resources based on sodar and microwave radiometer observations. Renewable and Sustainable Energy Reviews, 163, 112498. https://doi.org/10.1016/J.RSER.2022.112498
- Juanpera, M., Domenech, B., Ferrer-Martí, L., Garzón, A., & Pastor, R. (2021). Renewable-based electrification for remote locations. Does short-term success endure over time? A case study in Peru. Renewable and Sustainable Energy Reviews, 146, 111177. https://doi.org/10.1016/J.RSER.2021.111177
- Justus, C. ~G., & Mikhail, A. (1976). Height variation of wind speed and wind distributions statistics. grl, 3(5), 261–264. https://doi.org/10.1029/GL003i005p00261
- Justus, C. G., Hargraves, W. R., Mikhail, A., & Graber, D. (1978). Methods for estimating wind speed frequency distributions. 17:3. https://doi.org/10.1175/1520-0450(1978)017<0350:MFEWSF>2.0.CO;2
- Kaplan, Y. A. (2018). Performance assessment of Power Density Method for determining the Weibull Distribution Coefficients at three different locations. Flow Measurement and Instrumentation, 63, 8–13. https://doi.org/10.1016/J.FLOWMEASINST.2018.07.004
- Keyhani, A., Ghasemi-Varnamkhasti, M., Khanali, M., & Abbaszadeh, R. (2010). An assessment of wind energy potential as a power generation source in the capital of Iran, Tehran. Energy, 35(1), 188–201. https://doi.org/10.1016/j.energy.2009.09.009
- Khahro, S. F., Tabbassum, K., Soomro, A. M., Dong, L., & Liao, X. (2014). Evaluation of wind power production prospective and Weibull parameter estimation methods for Babaurband, Sindh Pakistan. Energy Conversion and Management, 78, 956–967. https://doi.org/10.1016/j.enconman.2013.06.062
- Khalid Saeed, M., Salam, A., Rehman, A. U., & Abid Saeed, M. (2019). Comparison of six different methods of Weibull distribution for wind power assessment: A case study for a site in the Northern region of Pakistan. Sustainable Energy Technologies and Assessments, 36, 100541. https://doi.org/10.1016/j.seta.2019.100541
- López, M. V. (2012). Ingeniería de la Energía Eólica. Marcombo.
- Manwell, J. F., McGowan, J. G., & Rogers, A. L. (2010). Wind Energy Explained: Theory, Design and Application. In Wind Energy Explained: Theory, Design and Application. https://doi.org/10.1002/9781119994367
- Martins, F., Felgueiras, C., & Smitková, M. (2018). Fossil fuel energy consumption in European countries. Energy Procedia, 153, 107–111. https://doi.org/10.1016/J.EGYPRO.2018.10.050
- Mohammadi, K., Alavi, O., Mostafaeipour, A., Goudarzi, N., & Jalilvand, M. (2016). Assessing different parameters estimation methods of Weibull distribution to compute wind power density. Energy Conversion and Management, 108, 322–335. https://doi.org/10.1016/J.ENCONMAN.2015.11.015
- National Aeronautics and Space Administration. (2022, January 13). NASA POWER - Prediction Of Worldwide Energy Resources. https://power.larc.nasa.gov/data-access-viewer
- Neely, C. J. (2022). The Russian Invasion, Oil and Gasoline Prices, and Recession. Economic Synopses, 2022(10). https://doi.org/10.20955/ES.2022.10
- Ouahabi, M. H., Elkhachine, H., Benabdelouahab, F., & Khamlichi, A. (2020). Comparative study of five different methods of adjustment by the Weibull model to determine the most accurate method of analyzing annual variations of wind energy in Tetouan - Morocco. Procedia Manufacturing, 46, 698–707. https://doi.org/10.1016/j.promfg.2020.03.099
- Patidar, H., Shende, V., Baredar, P., & Soni, A. (2022). Comparative study of offshore wind energy potential assessment using different Weibull parameters estimation methods. Environmental Science and Pollution Research 2022, 1–16. https://doi.org/10.1007/S11356-022-19109-X
- Saleh, H., Abou El-Azm Aly, A., & Abdel-Hady, S. (2012). Assessment of different methods used to estimate Weibull distribution parameters for wind speed in Zafarana wind farm, Suez Gulf, Egypt. Energy, 44(1), 710–719. https://doi.org/10.1016/j.energy.2012.05.021
- Servicio Nacional de Meteorología e Hidrología del Perú. (2022). SENAMHI - Perú. https://www.senamhi.gob.pe/?&p=estaciones
- Shoaib, M., Siddiqui, I., Rehman, S., Khan, S., & Alhems, L. M. (2019). Assessment of wind energy potential using wind energy conversion system. Journal of Cleaner Production, 216, 346–360. https://doi.org/10.1016/j.jclepro.2019.01.128
- Soulouknga, M. H., Doka, S. Y., N.Revanna, N.Djongyang, & T.C.Kofane. (2018). Analysis of wind speed data and wind energy potential in Faya-Largeau, Chad, using Weibull distribution. Renewable Energy, 121, 1–8. https://doi.org/10.1016/j.renene.2018.01.002
- Tito, U. Y., Huayta, O. A. V., Borja, M. G. B., & Quispe, G. B. (2021). Optimization of a Wind-Photovoltaic Hybrid System for a Rural Housing Isolated from the Network in the District of Paucarcolla-Perú. Proceedings of the 2021 IEEE 28th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2021. https://doi.org/10.1109/INTERCON52678.2021.9533024
- Tito, U. Y., Vilca-Huayta, O. A., & Quispe-Huaman, L. (2020). Estimation of the wind energy potential: A case study for a site in the southern region of Peru. 2020 IEEE ANDESCON, ANDESCON 2020. https://doi.org/10.1109/ANDESCON50619.2020.9272028
- Vidal, J. (2008). Atlas Eólico del Perú. 87.
- Wais, P. (2017). A review of Weibull functions in wind sector. Renewable and Sustainable Energy Reviews, 70, 1099–1107. https://doi.org/10.1016/J.RSER.2016.12.014