Estimativa da densidade de potência eólica em cidades do nordeste do Brasil

Authors

  • Sandro Correia de Holanda CECA/UFAL
  • Jerusa Universidade Federal de Alagoas
  • Fábio Universidade Federal de Alagoas

DOI:

https://doi.org/10.21712/lajer.2024.v11.n1.p121-134

Keywords:

Palavras-chave: Vento, Energia, Weibull, Parâmetros, métodos.

Abstract

Melhorar a precisão da estimativa de produção de energia eólica é preponderante para o planejamento estratégico no setor elétrico de uma nação. Nesse contexto, esta pesquisa teve como objetivo estimar os parâmetros do modelo estatístico de Weibull e a densidade de potência eólica usando dados coletados de três cidades no nordeste do Brasil. Além disso, outro objetivo foi analisar o melhor ajuste entre a distribuição dos dados observados e o modelo de Weibull. Para atingir esses objetivos, quatro metodologias distintas, a saber, Método de Regressão de Mínimos Quadrados (MRMQ), Método de Momentos (MM), Método de Fator de Padrão de Energia (MFPE) e Método de Máxima Verossimilhança (MMV), foram empregadas para estimar os parâmetros de forma e escala do modelo de Weibull. A fim de analisar o melhor ajuste entre os dados observados do vento e o modelo estatístico de Weibull, foi aplicado o teste estatístico: Erro Médio Quadrático (EMQ). Por sua vez, os valores médios dos parâmetros estimados obtidos através das quatro metodologias foram utilizados para calcular a densidade de potência eólica em cada cidade investigada. Os resultados deste estudo mostram que os ventos que sopram no nordeste do Brasil são de excelente qualidade favorecendo, a geração eólica. Além disso, todos os métodos examinados (ou seja, MRMQ, MMV, MM e MFPE) demonstraram desempenho satisfatório na estimativa dos parâmetros da distribuição de Weibull.

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References

Akdag, SA and Dinler, A (2009) ‘A New Method to Estimate Weibull Parameters for Wind Energy Applications’, Energy Conversion and Management, [e-journal] 50(7), pp. 1761-1766. http://dx.doi.org/10.1016/j.enconman.2009.03.020 DOI: https://doi.org/10.1016/j.enconman.2009.03.020

Azad, AK, Rasul, MG, and Yusaf, T (2014) ‘Statistical Diagnosis of the Best Weibull Methods for Wind Power Assessment for Agricultural Applications’, Energies, [e-jounal] 7(5), pp. 3056-3085. https://doi.org/10.3390/en7053056 DOI: https://doi.org/10.3390/en7053056

ANEEL (2019) ‘ANEEL institutional website’. http://www.aneel.gov.br (accessed 01 september, 2019).

Bidaoui, H, El Abbassi, I, El Bouardi, A and Darcherif, A (2019) ‘Wind Speed Data Analysis Using Weibull and Rayleigh Distribution Functions, Case Study: Five Cities Northern Morocco’, Procedia Manufacturing, [e-journal], v. 32, pp.786-793. https://doi.org/10.1016/j.promfg.2019.02.286 DOI: https://doi.org/10.1016/j.promfg.2019.02.286

Brazilian association of wind power and new technologies (ABEEólica), 2022. Annual Wind Energy Report 2022. [pdf] Available at: https://abeeolica.org.br/wp-content/uploads/2023/08/WIND-ENERGY-REPORT-2022-1.pdf [accessed 26 dec 2023].

Carrillo, C, Cidrás, J, Díaz-Dorado, E and Obando-Montaño, AF (2014) ‘An Approach to Determine the Weibull Parameters for Wind Energy Analysis: The Case of Galcia (Spain)’, Energies, [e-journal] 7(4), pp. 2676-2700. https://doi.org/10.3390/en7042676 DOI: https://doi.org/10.3390/en7042676

Chaurasiya, PK, Ahmed, S and Warudkar, V (2018) ‘Study of different parameters estimation methods of Weibull distribution to determine wind power density using ground based Doppler SODAR Instrument’, Alexandria Engineering Journal, [e-journal] 57(4), pp. 2299-2311. https://doi.org/10.1016/j.aej.2017.08.008 DOI: https://doi.org/10.1016/j.aej.2017.08.008

Escritório Técnico de Estudos Econômicos do Nordeste (ETENE), 2023. Caderno Setorial 2023. [pdf] Available at: https://www.bnb.gov.br/s482-dspace/bitstream/123456789/1781/1/2023_CDS_288.pdf [accessed 16 may 2024].

Global Wind Energy Concil (GWEC), 2024. Global Wind Report 2024. [pdf] Available at: https://gwec.net/wp-content/uploads/2024/04/GWR-2024_digital-version_final-1.pdf [accessed 17 may 2024].

Indhumathy, D, Seshaiah, CV and Sukkiramai, K, 2014. Estimation of Weibull Parameters for Wind speed calculation at Kanyakumari in India. [pdf] Available at: https://www.ijirset.com/upload/2014/january/33_Estimation.pdf [accessed 9 january 2024].

IRENA (2023) ‘IRENA institutional website’. https://www.irena.org/Publications/2023/Mar/Renewable-capacity-statistics-2023 (accessed 22 dec 2023).

IEA (2024) ‘IEA institutional website’. https://www.iea.org/reports/electricity-2024 (accessed 16 may 2024).

Jiajin, X and Zhentong, G, 2023. From Gaussian Distribution to Weibull Distribution. [pdf] Available at: https://globaljournals.org/GJRE_Volume23/1-From-Gaussian-Distribution-to-Weibull.pdf [accessed 10 january 2024]. DOI: https://doi.org/10.34257/GJREIVOL23IS1PG1

Kumar, KSP and Gaddada, S (2015) ‘Statistical scrutiny of Weibull parameters for wind energy potential appraisal in the area of northern Ethiopia’, Renewables, [e-journal], vol. 2, no. 14, pp.1-15. https://doi.org/10.1186/s40807-015-0014-0. DOI: https://doi.org/10.1186/s40807-015-0014-0

Kumar, MBH, Balasubramaniyan, S, Padmanaban, S and Holm-Nielsen, JB (2019) ‘Wind Energy Potential Assessment by Weibull Parameter Estimation Using Multiverse Optimization Method: A Case Study of Tirumala Region in India’. Energies, [e-journal] 12(11), pp.1-21. https://doi.org/10.3390/en12112158. DOI: https://doi.org/10.3390/en12112158

Manwell, J, McGowan, J and Rogers, A (2009) Wind energy explained: theory, design, and application, 2nd edn. Chichester: Wiley. DOI: https://doi.org/10.1002/9781119994367

Pishgar-Komleh, SH. Keyhani, A and Sefeedpari, P (2015) ‘Wind speed and power density analysis based on Weibull and Rayleigh distributions, a case study: Firouzkooh county of Iran’, Renewable and Sustaineble Energy Reviews, [e-journal], vol. 42, pp.313–322. https://doi.org/10.1016/j.rser.2014.10.028. DOI: https://doi.org/10.1016/j.rser.2014.10.028

Pobocíková, Ivana, Sedliacková, Zuzana (2014). ‘Comparison of Four Methods for EstimatingWeibull Distribution Parameters’, Applied Mathematical Sciences, [e-journal], vol. 8, no. 83, pp.4137-4149. http://dx.doi.org/10.12988/ams.2014.45389. DOI: https://doi.org/10.12988/ams.2014.45389

Rocha, PAC, Sousa, RC, Andrade, CF, S, MEV (2012) ‘Comparison of Seven Numerical Methods for Determining Weibull Parameters for Wind Energy Generation in the Northeast Region of Brazil’, Applied Energy, [e-journal] 89(1), pp.395-400. https://doi.org/10.1016/j.apenergy.2011.08.003. DOI: https://doi.org/10.1016/j.apenergy.2011.08.003

Shu, ZR, Jesson, M (2021) ‘Estimation of Weibull parameters for wind energy analysis across the UK’, J. Renewable Sustainable Energy, [e-journal] 13(2), pp.1-18. https://doi.org/10.1063/5.0038001. DOI: https://doi.org/10.1063/5.0038001

WWEA (2023) ‘WWEA institutional website’. https://wwindea.org/wwea-half-year-report-2023-additional-momentum-for-windpower-in-2023 (accessed 16 may 2024).

Wadi, M., Elmasry, W. (2021). Statistical analysis of wind energy potential using different estimation methods for Weibull parameters: a case study. Electrical Engineering, [e-journal], vol. 103, pp.2573-2594. https://doi.org/10.1007/s00202-021-01254-0. DOI: https://doi.org/10.1007/s00202-021-01254-0

Weibull, WA, 1951. A statistical distribution function of wide applicability. [pdf] Available at: https://web.cecs.pdx.edu/cgshirl/Documents/Weibull-ASME-Paper-1951.pdf [accessed 10 january 2024].

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Published

05-06-2024

How to Cite

Correia de Holanda, S., Goes de Aragão Santana, J., & Farias Pereira, F. (2024). Estimativa da densidade de potência eólica em cidades do nordeste do Brasil. Latin American Journal of Energy Research, 11(1), 121–134. https://doi.org/10.21712/lajer.2024.v11.n1.p121-134

Issue

Section

Energias de Baixo Carbono