Assessing wind energy exploitation potential in several regions of Viet Nam using Kernel density estimation model
,
and
*
Article Sidebar
Full Text: 
PDF
Main Article Content
Abstract
This article analyzes and assesses the potential for wind energy exploitation in six regions of Vietnam. The wind speed data are used to construct wind speed probability distributions (WSPDs) based on kernel density estimation (KDE). The KDE distribution, with six bandwidth selection methods, is implemented to generate probability density functions (PDFs) for each region's data to describe wind speed characteristics. The statistical tests Cramér-Von Mises (CvM), Anderson-Darling (A-D), and Kolmogorov-Smirnov (K-S) are applied to evaluate the PDFs' goodness-of-fit performance. The analysis results present the KDE distribution using the least-squares cross-validation (LSCV), and the Scott bandwidth selection method has outstanding fitting performance. Based on these PDF distributions, the wind turbine (WT) power curve is used to estimate and predict the amount of electricity that can be produced. This study also proposes a reliable method for wind power output planning based on wind speed that can be universally applied.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
IRENA. (2024). The Global Atlas for Renewable Energy: A decade in the making, International Renewable Energy Agency, Abu Dhabi. www.irena.org/Publications
Global Wind Report 2024 - Global Wind Energy Council. (n.d.). Retrieved October 17, 2024, from https://gwec.net/global-wind-report-2024/
Aries, N., Boudia, S. M., & Ounis, H. (2018). Deep assessment of wind speed distribution models: A case study of four sites in Algeria. Energy Convers. Manag., 155, 78–90.
Azad, A. K., Rasul, M. G., Alam, M. M., Uddin, S. M. A., & Mondal, S. K. (2014). Analysis of wind energy conversion system using Weibull distribution. Procedia Eng., 90, 725–732.
Bidaoui, H., Abbassi, I. El, & Bouardi Abdelmajid El and Darcherif, A. (2019). Wind speed data analysis using Weibull and Rayleigh distribution functions, case study: Five cities Northern Morocco. Procedia Manuf., 32, 786–793.
Carta, J. A., & Ramírez, P. (2007). Analysis of two-component mixture Weibull statistics for estimation of wind speed distributions. Renew. Energy, 32(3), 518–531.
Chang, T. P. (2011). Estimation of wind energy potential using different probability density functions. Appl. Energy, 88(5), 1848–1856.
Corder, G. W., & Foreman, D. I. (2011). Nonparametric Statistics for Non-Statisticians: A Step-by-Step Approach. Nonparametric Statistics for Non-Statisticians: A Step-by-Step Approach, 1–536. https://doi.org/10.1002/9781118165881
Darling, D. A. (1957). The Kolmogorov-Smirnov, Cramer-von Mises Tests. The Annals of Mathematical Statistics, 28(4), 823–838. http://www.jstor.org/stable/2237048
Demir, S. (2018). Adaptive kernel density estimation with generalized least square cross-validation. Hacet. J. Math. Stat., 48(3).
Di Leo, G., & Sardanelli, F. (2020). Statistical significance: p value, 0.05 threshold, and applications to radiomics-reasons for a conservative approach. Eur. Radiol. Exp., 4(1), 18.
Elliott, D., Schwartz, M., & Scott, G. (2004). Wind Resource Base. In Encyclopedia of Energy (pp. 465–479). Elsevier.
Farghali, M., Osman, A. I., Chen, Z., Abdelhaleem, A., Ihara, I., Mohamed, I. M. A., Yap, P. S., & Rooney, D. W. (2023). Social, environmental, and economic consequences of integrating renewable energies in the electricity sector: A review. Environmental Chemistry Letters, 21(3), 1381–1418. https://doi.org/10.1007/S10311-023-01587-1
Harpole, J. K., Woods, C. M., Rodebaugh, T. L., Levinson, C. A., & Lenze, E. J. (2014). How bandwidth selection algorithms impact exploratory data analysis using kernel density estimation. Psychol. Methods, 19(3), 428–443.
Kang, D., Ko, K., & Huh, J. (2015). Determination of extreme wind values using the Gumbel distribution. Energy (Oxf.), 86, 51–58.
Khaloie, H., Abdollahi, A., Shafie-khah, M., Anvari-Moghaddam, A., Nojavan, S., Siano, P., & Catalão, J. P. S. (2020). Coordinated wind-thermal-energy storage offering strategy in energy and spinning reserve markets using a multi-stage model. Applied Energy, 259, 114168. https://doi.org/https://doi.org/10.1016/j.apenergy.2019.114168
Masseran, N. (2016). Modeling the fluctuations of wind speed data by considering their mean and volatility effects. Renew. Sustain. Energy Rev., 54, 777–784.
Ouarda, T. B. M. J., Charron, C., Shin, J.-Y., Marpu, P. R., Al-Mandoos, A. H., Al-Tamimi, M. H., Ghedira, H., & Al Hosary, T. N. (2015). Probability distributions of wind speed in the UAE. Energy Convers. Manag., 93, 414–434.
Qin, Z., Li, W., & Xiong, X. (2011). Estimating wind speed probability distribution using kernel density method. Electric Power Syst. Res., 81(12), 2139–2146.
Scott, D. W. (1992). Multivariate density estimation. John Wiley & Sons.
Sheather, S. J., & Jones, M. C. (1991). A reliable data-based bandwidth selection method for kernel density estimation. J. R. Stat. Soc. Series B Stat. Methodol., 53(3), 683–690.
Shi, H., Dong, Z., Xiao, N., & Huang, Q. (2021). Wind speed distributions used in wind energy assessment: A review. Front. Energy Res., 9.
Silverman, B. W. (2018). Density estimation: For statistics and data analysis. Density Estimation: For Statistics and Data Analysis, 1–175. https://doi.org/10.1201/9781315140919/DENSITY-ESTIMATION-STATISTICS-DATA-ANALYSIS-BERNARD-SILVERMAN
Terrell, G. R. (1990). The maximal smoothing principle in density estimation. J. Am. Stat. Assoc., 85(410), 470.
Zhang, J., Chowdhury, S., Messac, A., & Castillo, L. (2013). A Multivariate and Multimodal Wind Distribution model. Renew. Energy, 51, 436–447.