SENSITIVITY TESTS USING WRF-SOLAR TO ESTIMATE HORIZONTAL GLOBAL IRRADIANCE IN FIVE CLIMATE REGIONS IN BRAZIL
DOI:
https://doi.org/10.59627/cbens.2024.2537Keywords:
Numerical modeling, Solar resource, WRFAbstract
Version 4.4.1 of the WRF-Solar model was used to simulate the Global Horizontal Irradiance (GHI) of five Brazilian climatic regions: Sousa-PB, São João do Piauí-PI, Ilha Solteira-SP, Mossoró-RN and Bom Jesus da Lapa-BA. The main objective was to verify the performance of the Fast All-sky Radiation Model Solar Applications (FARMS) algorithm in two experiments, for the rainy month and the dry month in each location. To validate the data, GHI data measured by automatic solarimetric stations (ESAs) installed and operated by the SENAI Institute for Innovation in Renewable Energy, in each of the locations, was used. Observational data were explored as proposed by the Baseline Surface Radiation Network (BSRN). To perform the modeling, we used input data from ERA5 reanalysis. The Typical Meteorological Year was calculated using the Sandia method. The use of the FARMS algorithm managed to improve the RMSE relative to the GHI during the rainy month of three seasons, however in the dry month the results did not show significant changes. The results showed errors between 8 and 20%, between the rainy and dry months, values much lower than the literature shows, and in general the sensitivity tests obtained more efficient results for the dry months.
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References
Bussoni, C. V. A., Moreira, D. S., & Machado, J. P. (2022). Avaliação do Modelo WRF para Aplicação de um índice de Previsão de Geada na Região Sul do Brasil. Revista Brasileira de Meteorologia, 37, 279-287.
De Sousa Stilpen, D. V.; Cheng, V. Solar photovoltaics in Brazil: A promising renewable energy market. In: 2015 3rd International Renewable and Sustainable Energy Conference (IRSEC). IEEE, 2015. p. 1-5.
Deng, A.; B. J. Gaudet; J. Dudhia; K. Alapaty. Im plementation and evaluation of a new shallow convection scheme in WRF. 26th Conf. on Weather Analysis and Fore casting/22nd Conf. on Numerical Weather Prediction, Atlanta, GA, Amer. Meteor. Soc., 12.5. Available online at https://
ams.confex.com/ams/94Annual/webprogram/Paper236925.html. 2014.
Fountoukis, C., Martín-Pomares, L., Perez-Astudillo, D., Bachour, D., Gladich, I., 2018. Simulating global horizontal irradiance in the Arabian Peninsula: Sensitivity to explicit treatment of aerosols, Solar Energy, v. 163, p. 347 – 355. https://doi.org/10.1016/j.solener.2018.02.00
Ginoux, P.; Chin, M.; Tegen, I.; et al. Sources and distributions of dust aerosols simulated with the GOCART model. Journal of Geophysical Research: Atmospheres, v. 106, n. D17, p. 20255–20273, 2001
Gueymard, C.A.; Jimenez, P.A., 2018. Validation of real-time solar irradiance simulations over Kuwait using WRF-solar. In: the 12th International Conference on Solar Energy for Buildings and Industry, EuroSun2018, Rapperswil, Switzerland.
Kim, J. H., Jimenez, P. A., Dudhia, J., Yang, J., Sengupta, M., Xie, Y., 2020. Probabilistic Forecast of All-Sky Solar Radiation Using Enhanced WRF-Solar: Preprint. Golden, CO: National Renewable Energy Laboratory. NREL/CP-5D00-77693.
Kim, J. H.; Jimenez, P. A., Sengupta, M., Yang, J., Dudhia, J., Alessandrini, S., Xie, Y., 2022. The WRF-Solar Ensemble Prediction System to Provide Solar Irradiance Probabilistic Forecasts. Journal of Photovoltaics, v.12, n. 1.
Leal, S. S.; Tiba, C. Iluminância e irradiação solar global na região Nordeste do Brasil. Proceedings of the 6. Encontro de Energia no Meio Rural, 2006.
Lima, F. J. L.; Pereira, E. B.; Martins, F. R. Forecast for surface solar irradiance at the Brazilian Northeastern region using NWP model and artificial neural networks. Renewable Energy, v. 87, p. 807-8186, 2016.
Lima, F. et al. Comparing solar data from NWP models for Brazilian territory. IEEE Latin America Transactions, v. 18, n. 05, p. 899-906, 2020.
Long, C. N., Shi, Y., 2008. “An Automated Quality Assessment and Control Algorithm for Surface Radiation Measurements”, The Open Atmospheric Science Journal, v.2, n.1, p.23–37. doi: 10.2174/1874282300802010023.
Machado, I. S.; Borba, B. S. M. C.; Maciel, R. S. Modeling distributed PV market and its impacts on distribution System: A Brazilian case study. IEEE Latin America Transactions, v. 14, n. 11, p. 4520-4526, 2016.
Martins, F. R.; Pereira, E. B.; Abreu, S. L. Satellite derived solar resources maps for Brazil under SWERA project. Solar Energy, v. 81, p. 517-528, 2007.
Martins, F. R. et al. Solar energy scenarios in Brazil. Part two: Photovoltaics applications. Energy Policy, v. 36, n. 8, p. 2865-2877, 2008.
Martins, F. R.; Pereira, E. B. Estudo comparativo da confiabilidade de estimativas de irradiação solar para o sudeste brasileiro obtidas a partir de dados de satélite e por interpolação/extrapolação de dados de superfície. Revista Brasileira de Geofísica, v. 29, p. 265-276, 2011.
Nobre, A. M. Short-term solar irradiance forecasting and photovoltaic systems performance in a tropical climate in Singapore. Tese de doutorado. Universidade Federal de Santa Catarina: Programa de pós-graduação em engenharia civil. Florianópolis, 2015
Pereira, E. B. et al. Atlas Brasileiro de Energia Solar. INPE. São José dos Campos: Instituto Nacional de Pesquisas Espaciais, 2006.
Pereira, E. B., Martins, F. R., Gonçalves, A. R., Costa, R. S., Lima, F. L., Rüther, R., Abreu, S. L., Tiepolo, G. M., Pereira, S. V., Souza, J. G., 2017. Atlas brasileiro de energia solar. 2.ed. São José dos Campos: INPE. 80p. Disponível em: http://doi.org/10.34024/978851700089
Ramos, D. N. S. et al. Avaliação preliminar do modelo wrf-solar para previsão de curto prazo das irradiâncias global horizontal e direta normal. In: VII Congresso Brasileiro de Energia Solar-CBENS 2018. 2020.
Ruiz-Arias, J. A., J. Dudhia, F. J. Santos-Alamillos, and D. Pozo Vázquez, 2013: Surface clear-sky shortwave radiative closure intercomparisons in the Weather Research and Forecasting model. J. Geophys. Res. Atmos., 118, 9901–9913, doi:10.1002/ jgrd.50778.
Ruiz-Arias, J. A.; Dudhia, J.; Gueymard, C. A. A simple parameterization of the short-wave aerosol optical properties for surface direct and diffuse irradiances assessment in a numerical weather model. Geoscientific Model Development, v. 7, n. 3, p. 1159–1174, 2014.
Ruiz, J. J., Saulo, S. y Nogués-Paegle, 2010. WRF Model Sensitivity to Choice of Parameterization over South America: Validation against Surface Variables. Monthly Weather Review, 138, 3342–3351.
Sengupta, M. et al., 2018. “The national solar radiation data base (NSRDB),” Renewable Sustain. Energy Rev., vol. 89, pp. 51–60.
Thompson, G., and T. Eidhammer, 2014: A Study of Aerosol Impacts on Clouds and Precipitation Development in a Large Winter Cyclone. J. Atmos. Sci., 71, 3636–3658, https://doi.org/10.1175/JAS-D-13-0305.1.
Wilcox, S., Marion, W., 2008. Users Manual for TMY3 Data Sets. National Renewable Energy Laboratory.
Wilks, D. S., 2006. Statistical Methods in the Atmospheric Sciences. International Geophysics Series. 2a. Edição, Estados Unidos da América, Academic Press, v. 91, 627 p.
Yagli, G. M.; YAng, D.; Srinivasan, D. Reconciling solar forecasts: Sequential reconciliation. Solar Energy, v. 179, p. 391-397, 2019
Zempila M.-M., Giannaros T.M., Bais A. and Melas D., 2016. Evaluation of WRF shortwave radiation parameterizations in predicting global horizontal irradiance in Greece. Renew. Energ. 86, 831-840. doi: 10.1016/j.renene.2015.08.057
Zepka, G.S. Previsão de Descargas Atmosféricas Usando o Modelo de Mesoescala WRF. Tese de Doutorado em Geofísica Espacial, Instituto Nacional de Pesquisas Espaciais, 178 p, 2011.
Xie, Y., Sengupta, M., & Dudhia, J. (2016). A Fast All-sky Radiation Model for Solar applications (FARMS): Algorithm and performance evaluation. Solar Energy, 135, 435-445.
