Wind Energy Potential by the Weibull Distribution at High-Altitude Peruvian Highlands

Elmer Rodrigo Aquino Larico

Abstract


Wind energy in recent years has experienced accelerated growth compared to other renewable energy sources, therefore it is important to determine the potential of wind energy available for application in wind systems. The objective of this study was to determine the energy potential by means of the Weibull distribution and the wind rose in the Peruvian highlands at an altitude of more than 3800 m. The two-parameter Weibull distribution function method was used to estimate the profile of the wind speed and the wind perspective, based on observations of the wind during one year by the meteorological station located in the Peruvian highlands of Juliaca. The results show that the wind characteristics are irregular, where the annual mean shape and scale parameters were 2.16 and 2.20 m/s evaluated 24 hours, however, the mean annual shape and scale parameters in the afternoons were 5.01 and 4.46 m/s respectively. Therefore, this site presents a high wind energy potential in the afternoon, because the wind flow is not constant during the 24 hours, but it is in the afternoons.

Keywords


Parameters; wind; Weibull distribution; wind energy.

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References


K. Ivanovski, A. Hailemariam, and R. Smyth, “The effect of renewable and non-renewable energy consumption on economic growth: Non-parametric evidence,” Journal of Cleaner Production, pp. 124956, November 2020.

B. Dai, P. Zhao, S. Liu, M. Su, D. Zhong, J. Qian, X. Hu, and Y. Hao, “Assessment of heat pump with carbon dioxide/low-global warming potential working fluid mixture for drying process: Energy and emissions saving potential,” Energy Conversion and Management, vol. 222, pp. 113225, December 2020.

G. Vural, “How do output, trade, renewable energy and non-renewable energy impact carbon emissions in selected Sub-Saharan African Countries?,” Resources Policy, vol. 69, pp. 101840, December 2020.

E. Garbolino, W. Daniel, and G. H. Mendoza, "Chapter 8 - Geoprospective assessment of the wood energy supply chain sustainability in a context of global warming and land use change within 2050 in Mediterranean area," Ecosystem and Territorial Resilience, E. Garbolino and C. Voiron-Canicio, eds., pp. 219-245: Elsevier, 2021.

H. Sözer, and H. Sözen, “Energy saving, global warming and waste recovery potential of retrofitting process for a district,” Journal of Cleaner Production, vol. 238, pp. 117915, November 2019.

B. Karatop, B. Ta?kan, E. Adar, and C. Kubat, “Decision Analysis related to the Renewable Energy Investments in Turkey Based on a Fuzzy AHP-EDAS-Fuzzy FMEA Approach,” Computers & Industrial Engineering, pp. 106958, November 2020.

Q. Wang, and Y. Liu, “India’s renewable energy: New insights from multi-regional input output and structural decomposition analysis,” Journal of Cleaner Production, pp. 124230, October 2020.

F. S. Mohd Chachuli, S. Mat, N. A. Ludin, and K. Sopian, “Performance evaluation of renewable energy R&D activities in Malaysia,” Renewable Energy, vol. 163, pp. 544-560, January 2021.

R. P. Praveen, V. Keloth, A. G. Abo-Khalil, A. S. Alghamdi, A. M. Eltamaly, and I. Tlili, “An insight to the energy policy of GCC countries to meet renewable energy targets of 2030,” Energy Policy, vol. 147, pp. 111864, December 2020.

Y. Gao, S. Ma, T. Wang, T. Wang, Y. Gong, F. Peng, and A. Tsunekawa, “Assessing the wind energy potential of China in considering its variability/intermittency,” Energy Conversion and Management, vol. 226, pp. 113580, December 2020.

A. Mostafaeipour, M. Jahangiri, A. Haghani, S. J. H. Dehshiri, S. S. H. Dehshiri, A. Issakhov, A. Sedaghat, H. Saghaei, E. T. Akinlabi, S. M. Sichilalu, S. Chowdhury, and K. Techato, “Statistical evaluation of using the new generation of wind turbines in South Africa,” Energy Reports, vol. 6, pp. 2816-2827, November 2020.

J. Dorrell, and K. Lee, “The Politics of Wind: A state level analysis of political party impact on wind energy development in the United States,” Energy Research & Social Science, vol. 69, pp. 101602, November 2020.

Y. Wang, E. Yang, T. Chen, J. Wang, Z. Hu, J. Mi, X. Pan, and M. Xu, “A novel humidity resisting and wind direction adapting flag-type triboelectric nanogenerator for wind energy harvesting and speed sensing,” Nano Energy, vol. 78, pp. 105279, December 2020.

J. Zhang, M. Zhang, Y. Li, J. Qin, K. Wei, and L. Song, “Analysis of wind characteristics and wind energy potential in complex mountainous region in southwest China,” Journal of Cleaner Production, vol. 274, pp. 123036, November 2020.

J. Drücke, M. Borsche, P. James, F. Kaspar, U. Pfeifroth, B. Ahrens, and J. Trentmann, “Climatological analysis of solar and wind energy in Germany using the Grosswetterlagen classification,” Renewable Energy, vol. 164, pp. 1254-1266, February 2021.

S. Höltinger, B. Salak, T. Schauppenlehner, P. Scherhaufer, and J. Schmidt, “Austria's wind energy potential – A participatory modeling approach to assess socio-political and market acceptance,” Energy Policy, vol. 98, pp. 49-61, November 2016.

S. Sang, H. Wen, A. X. Cao, X. R. Du, X. Zhu, Q. Shi, and C. H. Qiu, “Dynamic modification method for BEM of wind turbine considering the joint action of installation angle and structural pendulum motion,” Ocean Engineering, vol. 215, pp. 107528, November 2020.

C. Kan, Y. Devrim, and S. Eryilmaz, “On the theoretical distribution of the wind farm power when there is a correlation between wind speed and wind turbine availability,” Reliability Engineering & System Safety, vol. 203, pp. 107115, November 2020.

H. Sun, C. Qiu, L. Lu, X. Gao, J. Chen, and H. Yang, “Wind turbine power modelling and optimization using artificial neural network with wind field experimental data,” Applied Energy, vol. 280, pp. 115880, December 2020.

N. K. Siavash, B. Ghobadian, G. Najafi, A. Rohani, T. Tavakoli, E. Mahmoodi, R. Mamat, and m. Mohamed, “Prediction of Power Generation and Rotor Angular Speed of a Small Wind Turbine Equipped to a Controllable Duct Using Artificial Neural Network and Multiple Linear Regression,” Environmental Research, pp. 110434, November 2020.

P. Bórawski, A. Be?dycka-Bórawska, K. J. Jankowski, B. Dubis, and J. W. Dunn, “Development of wind energy market in the European Union,” Renewable Energy, vol. 161, pp. 691-700, December 2020.

Z. R. Shu, Q. S. Li, and P. W. Chan, “Investigation of offshore wind energy potential in Hong Kong based on Weibull distribution function,” Applied Energy, vol. 156, pp. 362-373, October 2015.

S. Deep, A. Sarkar, M. Ghawat, and M. K. Rajak, “Estimation of the wind energy potential for coastal locations in India using the Weibull model,” Renewable Energy, vol. 161, pp. 319-339, December 2020.

J. A. Guarienti, A. Kaufmann Almeida, A. Menegati Neto, A. R. de Oliveira Ferreira, J. P. Ottonelli, and I. Kaufmann de Almeida, “Performance analysis of numerical methods for determining Weibull distribution parameters applied to wind speed in Mato Grosso do Sul, Brazil,” Sustainable Energy Technologies and Assessments, vol. 42, pp. 100854, December 2020.

M. M. Islam, X. Zhong, Z. Sun, H. Xiong, and W. Hu, “Real-time frequency regulation using aggregated electric vehicles in smart grid,” Computers & Industrial Engineering, vol. 134, pp. 11-26, August 2019.

M. R. Islam, R. Saidur, and N. A. Rahim, “Assessment of wind energy potentiality at Kudat and Labuan, Malaysia using Weibull distribution function,” Energy, vol. 36, no. 2, pp. 985-992, February 2011.

M. H. Soulouknga, S. Y. Doka, N.Revanna, N.Djongyang, and T.C.Kofane, “Analysis of wind speed data and wind energy potential in Faya-Largeau, Chad, using Weibull distribution,” Renewable Energy, vol. 121, pp. 1-8, June 2018.

J. C. Aririguzo, and E. B. Ekwe, “Weibull distribution analysis of wind energy prospect for Umudike, Nigeria for power generation,” Robotics and Computer-Integrated Manufacturing, vol. 55, pp. 160-163, February 2019.

A. Keyhani, M. Ghasemi-Varnamkhasti, M. Khanali, and R. Abbaszadeh, “An assessment of wind energy potential as a power generation source in the capital of Iran, Tehran,” Energy, vol. 35, no. 1, pp. 188-201, 2010.

A. Serban, L. S. Paraschiv, and S. Paraschiv, “Assessment of wind energy potential based on Weibull and Rayleigh distribution models,” Energy Reports, vol. 6, pp. 250-267, November 2020.

Q. Feng, X. Zhao, D. Fan, B. Cai, Y. Liu, and Y. Ren, “Resilience design method based on meta-structure: A case study of offshore wind farm,” Reliability Engineering & System Safety, vol. 186, pp. 232-244, 2019.

Q. Zhao, Y. Yuan, W. Sun, X. Fan, P. Fan, and Z. Ma, “Reliability analysis of wind turbine blades based on non-Gaussian wind load impact competition failure model,” Measurement, vol. 164, pp. 107950, November 2020.

S. Ke, W. Yu, T. Wang, and Y. Ge, “Aerodynamic performance and wind-induced effect of large-scale wind turbine system under yaw and wind-rain combination action,” Renewable Energy, vol. 136, pp. 235-253, June 2019.

F. Papi, F. Balduzzi, G. Ferrara, and A. Bianchini, “Uncertainty quantification on the effects of rain-induced erosion on annual energy production and performance of a Multi-MW wind turbine,” Renewable Energy, vol. 165, pp. 701-715, March 2021.

C. Hasager, F. Vejen, J. I. Bech, W. R. Skrzypi?ski, A. M. Tilg, and M. Nielsen, “Assessment of the rain and wind climate with focus on wind turbine blade leading edge erosion rate and expected lifetime in Danish Seas,” Renewable Energy, vol. 149, pp. 91-102, April 2020.




DOI (PDF): https://doi.org/10.20508/ijsmartgrid.v5i3.199.g154

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