Method for estimating the wind power micro and minigeneration applied to a city with a subtropical climate in south America

Authors

DOI:

https://doi.org/10.33448/rsd-v10i12.20009

Keywords:

Wind energy potential; Mapping; QGis; Low heights; Different heights.

Abstract

The search for renewable resources has become necessary for sustainable development. Wind energy is a clean energy source and of global importance, but most studies refer to high altitude and are carried out by researchers in developed countries. In this work, the objective was to evaluate the potential of micro and mini-generation (low heights) wind power in Cascavel-PR, a city located in southern Brazil, as an example of the application of the proposed methodology. For this purpose, wind speed and direction data were used, with a historical series of 21 years (1997-2017). The land use and occupation were performed in a semi-automatic way using Sentinel-2 satellite images. To generate the maps, an algorithm was created in Spring software, which correlated the land use and land cover information, the wind speed kriging and the formula coefficient values according to the identified obstacles. With this it was possible to conclude that regarding the period, spring is considered the season of the year with the greatest energy potential, where the highest averages for speed and power were estimated. It was also noted that the 40-meter-high range has the greatest potential and that the prevailing winds come from the northeast.

References

ABEolica. (2019). Boletim Anual de Geração Eólica 2019. http://abeeolica.org.br/wp-content/uploads/2020/06/PT_Boletim-Anual-de-Geração-2019.pdf

Almeida, D. L., & Benassi, R. F. (2015). Crise hídrica e de energia elétrica entre 2014- 2015 na região Sudeste. Revista Hipótese, 1(2), 65–76.

Alvares, C. A., Stape, J. L., Sentelhas, P. C., de Moraes Gonçalves, J. L., & Sparovek, G. (2013). Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift, 22(6), 711–728. https://doi.org/10.1127/0941-2948/2013/0507

Resolução Normativa no 687 de 2015 da ANEEL, Pub. L. No. no 687, Aneel 24 (2015). https://www2.aneel.gov.br/cedoc/ren2015687.pdf

Araújo, F. R. P. de, Pereira, M. G., Freitas, M. A. V., da Silva, N. F., & Dantas, E. J. de A. (2021). Bigger Is Not Always Better: Review of Small Wind in Brazil. Energies, 14(4), 976. https://doi.org/10.3390/en14040976

Archer, C. L., & Jacobson, M. Z. (2005). Evaluation of global wind power. Journal of Geophysical Research, 110(D12), D12110. https://doi.org/10.1029/2004JD005462

Bittencourt, L., & Cândido, C. (2006). Introdução à ventilação natural (2nd ed.). EDUFAL.

Bolfarine, H., & Bussab, W. O. (2005). Elementos da amostragem. Blücher.

Brewer, C. (The P. S. U., & Harrower, M. (2020). Color Brewer. https://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3

Byrne, R., Hewitt, N. J., Griffiths, P., & MacArtain, P. (2019). An assessment of the mesoscale to microscale influences on wind turbine energy performance at a peri-urban coastal location from the Irish wind atlas and onsite LiDAR measurements. Sustainable Energy Technologies and Assessments, 36(September), 100537. https://doi.org/10.1016/j.seta.2019.100537

Carta, J. A., Velázquez, S., & Cabrera, P. (2013). A review of measure-correlate-predict (MCP) methods used to estimate long-term wind characteristics at a target site. Renewable and Sustainable Energy Reviews, 27, 362–400. https://doi.org/10.1016/j.rser.2013.07.004

Chagas, C. C. M., Pereira, M. G., Rosa, L. P., da Silva, N. F., Freitas, M. A. V., & Hunt, J. D. (2020). From megawatts to kilowatts: A review of small wind turbine applications, lessons from the US to Brazil. Sustainability (Switzerland), 12(7). https://doi.org/10.3390/su12072760

Chamanehpour, E., Ahmadizadeh, & Akbarpour. (2017). Site selection of wind power plant using multi-criteria decision-making methods in GIS: A case study. Computational Ecology and Software, 7(2), 49–64. http://www.iaees.org/publications/journals/ces/articles/2017-7(2)/multi-criteria-decision-making-methods-in-GIS.pdf%0Awww.iaees.org

Clarke, L., Edmonds, J., Krey, V., Richels, R., Rose, S., & Tavoni, M. (2009). International climate policy architectures: Overview of the EMF 22 International Scenarios. Energy Economics, 31(SUPPL. 2), 64–81. https://doi.org/10.1016/j.eneco.2009.10.013

Cochran, W. G. (1965). Técnicas de Amostragem.

Cruz, D. T. (2015). Micro e minigeração eólica e solar no Brasil: Propostas para desenvolvimento do setor. In Escola Politécnica da Universidade de São Paulo. http://www.teses.usp.br/teses/disponiveis/3/3143/tde-04082015-153708/pt-br.php

Drechsler, M., Egerer, J., Lange, M., Masurowski, F., Meyerhoff, J., & Oehlmann, M. (2017). Efficient and equitable spatial allocation of renewable power plants at the country scale. Nature Energy, 2(9), 17124. https://doi.org/10.1038/nenergy.2017.124

Emeksiz, C., & Cetin, T. (2019). In case study: Investigation of tower shadow disturbance and wind shear variations effects on energy production, wind speed and power characteristics. Sustainable Energy Technologies and Assessments, 35(June), 148–159. https://doi.org/10.1016/j.seta.2019.07.004

Friedland, C. J., Joyner, T. A., Massarra, C., Rohli, R. V., Treviño, A. M., Ghosh, S., Huyck, C., & Weatherhead, M. (2017). Isotropic and anisotropic kriging approaches for interpolating surface-level wind speeds across large, geographically diverse regions. Geomatics, Natural Hazards and Risk, 8(2), 207–224. https://doi.org/10.1080/19475705.2016.1185749

Garrido-Perez, J. M., Ordóñez, C., Barriopedro, D., García-Herrera, R., & Paredes, D. (2020). Impact of weather regimes on wind power variability in western Europe. Applied Energy, 264(February), 114731. https://doi.org/10.1016/j.apenergy.2020.114731

Giannini, M., Dutra, R. M., & Guedes, V. G. (2013). Estudo prospectivo do mercado de energia eólica de pequeno porte no Brasil. 21. http://www.cresesb.cepel.br/publicacoes/download/artigo/BrazilWindpower2013-Estudo_Prospectivo_do_Mercado_de_Energia_Eólica_de_Pequeno _Porte_ no_Brasil.pdf

Golden Software. (1999). Surfer (No. 15-Trial Version). www.goldensoftware.com

IPARDES. (2021). Município de cascavel Abril 2021. http://www.ipardes.gov.br/cadernos/MontaCadPdf1.php?Municipio=85800

Johansson, B., & Chen, D. (2003). The influence of wind and topography on precipitation distribution in Sweden: statistical analysis and modelling. International Journal of Climatology, 23(12), 1523–1535. https://doi.org/10.1002/joc.951

Jung, C., & Schindler, D. (2020). The annual cycle and intra-annual variability of the global wind power distribution estimated by the system of wind speed distributions. Sustainable Energy Technologies and Assessments, 42(October). https://doi.org/10.1016/j.seta.2020.100852

Lakes Environmental. (2018). WRPlot View (8.0.2). https://www.weblakes.com/products/wrplot/index.html

Lu, X., & McElroy, M. B. (2017). Global Potential for Wind-Generated Electricity. Wind Energy Engineering: A Handbook for Onshore and Offshore Wind Turbines, 106(27), 51–73. https://doi.org/10.1016/B978-0-12-809451-8.00004-7

MME. (2007). Atlas do Potencial Eólico do Paraná. http://www.cresesb.cepel.br/publicacoes/download/atlas_eolico/Atlas_do_Potencial_Eol ico_do_Estado_do_Parana.pdf

MME. (2018). Balanço Energético Nacional 2018. http://www.epe.gov.br/sites-pt/publicacoes-dados-abertos/publicacoes/PublicacoesArquivos/publicacao-303/topico-419/BEN2018__Int.pdf

Neiva, A. C. de B., Dutra, R. M., Melo, S. R. F. C. de, Guedes, V. G., Cabrera, A. A. M., Almeida, W. G. de, & Braz, R. de O. (2017). Atlas do Potencial Eólico Brasileiro: Simulações 2013. Habitat International, 52. http://www.seinfra.ce.gov.br/index.php/downloads/category/6-energia?download=16:p

Nitsche, P. R., Caramori, P. H., Ricce, W. da S., & Pinto, L. F. D. (2019). Atlas Climático do Estado do Paraná. http://www.iapar.br/arquivos/File/zip_pdf/AtlasClimaticoPR.pdf

Nunes, J. E. de O., Neto, J. V. de S., Silva, M. M. de L., Cordeiro, N. M., Pessoa, R. V. S., Barreto, I. D. de C., Bejan, L. B., & Stosic, T. (2021). Wind speed analysis using Markov chain. Research, Society and Development, 10(9), 1–9.

Oksanen, J., Blanchett, F. G., & Kindt, R. et al. (2015). Vegan: community ecology package.

Paraná Weather System. (2020). SIMEPAR. www.simepar.br/prognozweb/simepar/post/14037

Pereira, M. G., Da Silva, N. F., De Melo, S. R. F. C., Freitas, M. A. V., & Guedes, V. G. (2018). Integrated assessment of energy potential and social aspects: Case study of small wind in the state of Rio Grande do Norte. SBSE 2018 - 7th Brazilian Electrical Systems Symposium, 1–6. https://doi.org/10.1109/SBSE.2018.8395887

Prates, J. E., Zaicovski, M. B., & K., G. (2000). Variabilidade temporal e espacial do vento médio e de rajada no Paraná. Encyclopedia of Volcanoes., 1995, 662.

QGIS Development Team. (2019). QGIS (3.4.4-Madeira). General ublic License (GNU). www.qgis.org/

R Core Team. (2014). R. www.rproject.org

Ramachandra, T. V., Hegde, G., & Krishnadas, G. (2014). Potential assessment and decentralized applications of wind energy in Uttara Kannada, Karnataka. International Journal of Renewable Energy Research, 4(1), 1–10.

REN21. (2019). Renewables 2019 Global Status Report Collaborative. In Ren21 (Vol. 105, Issue July). http://www.ren21.net/gsr-2019/pages/foreword/foreword/

Ruggiero, S., Varho, V., & Rikkonen, P. (2015). Transition to distributed energy generation in Finland: Prospects and barriers. Energy Policy, 86, 433–443. https://doi.org/10.1016/j.enpol.2015.07.024

Sampaio, K. R. A., & Batista, V. (2021). The current scenario of wind energy production in Brazil : A literature review. Research, Society and Development, 10(1), 1–8.

Silva, G. F. da, Barreto, I. D. de C., & Stosic, T. (2021). Multiscale entropy analysis of wind speed dynamics in Petrolina, Northeast Brazil. Research, Society and Development, 10(1), 1–9. https://doi.org/10.33448/rsd-v10i1.11460

Sobral, B. S., Oliveira Júnior, J. F. de, Gois, G. de, Terassi, P. M. de B., & Pereira, C. R. (2018). Regime de Vento na Serra do Mar - Rio de Janeiro, Brasil. Revista Brasileira de Meteorologia, 33(3), 441–451. https://doi.org/10.1590/0102-7786333004

Tolmasquim, M. T. (2002). Fontes Renováveis de Energia no Brasil. Editora Interciência Ltda.

Tubelis, A., & Nascimento, F. J. L. do. (1980). Meteorologia descritiva: fundamentos e aplicações brasileiras. Nobel.

Vargas, S. A., Esteves, G. R. T., Maçaira, P. M., Bastos, B. Q., Cyrino Oliveira, F. L., & Souza, R. C. (2019). Wind power generation: A review and a research agenda. Journal of Cleaner Production, 218, 850–870. https://doi.org/10.1016/j.jclepro.2019.02.015

Wang, Z., Bui, Q., Zhang, B., Nawarathna, C. L. K., & Mombeuil, C. (2021). The nexus between renewable energy consumption and human development in BRICS countries: The moderating role of public debt. Renewable Energy, 165, 381–390. https://doi.org/10.1016/j.renene.2020.10.144

Weekes, S. M., Tomlin, A. S., Vosper, S. B., Skea, A. K., Gallani, M. L., & Standen, J. J. (2015). Long-term wind resource assessment for small and medium-scale turbines using operational forecast data and measure-correlate-predict. Renewable Energy, 81, 760–769. https://doi.org/10.1016/j.renene.2015.03.066

Whiteman, D. C. (2000). Mountain Meteorology: Fundamentals and Applications. Oxford University Press.

Wiegleb, G. (1980). Some applications of principal components analysis in vegetation: Ecological research of aquatic communities. Vegetatio, 42(1–3), 67–73. https://doi.org/10.1007/BF00048872

Wohlfarth, K., Klobasa, M., & Gutknecht, R. (2020). Demand response in the service sector – Theoretical, technical and practical potentials. Applied Energy, 258(October 2019), 114089. https://doi.org/10.1016/j.apenergy.2019.114089

Downloads

Published

14/09/2021

How to Cite

GUICHO, R.; FERREIRA, J. H. D.; MEDEIROS, G.; SIQUEIRA, J. A. C.; SOUZA, S. N. M. de; PRIOR, M. Method for estimating the wind power micro and minigeneration applied to a city with a subtropical climate in south America. Research, Society and Development, [S. l.], v. 10, n. 12, p. e105101220009, 2021. DOI: 10.33448/rsd-v10i12.20009. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/20009. Acesso em: 4 mar. 2024.

Issue

Section

Agrarian and Biological Sciences