Temporal Variability of Solar Energy Availability in the Conditions of the Southern Region of Mozambique

Authors

  • Fernando V. MucomoleDepartment of Physics & Energy Research Center, Faculty of Sciences, Eduardo Mondlane University, Mozambique
  • Carlos A. S. SilvaDepartment of Mechanical Engineering, Instituto Superior Técnico, University of Lisbon, Portugal
  • Lourenço L. MagaiaDepartmet of Mathematics and Informatics, Faculty of Sciences, Eduardo Mondlane University, Mozambique

DOI:

https://doi.org/10.54536/ajenr.v2i1.1311

Keywords:

Clear Sky Index, Irradiance, Intermediate Sky, Radiation, Variability

Abstract

The use of photovoltaic solar energy is affected by variations in the availability of solar radiation, which creates stability in solar panels. In our case, the need arose to study the temporal variability of solar energy in the southern region of Mozambique. This was followed by a descriptive sequence, applying the analytical method for the classification of days and the analysis of the day’s variability of clear, cloudy and intermediate skies in the data from three regional stations. The results show that it was mostly on clear sky days (44.64%), enhancing the use of solar energy. Statistical analysis of the frequency density variability shows that days with intermediate skies have a similar behavior, however they present a smooth decrease, because for variation of clear sky index ∆Kt* in the interval [-2,2] it is higher. The values of Kt* vary between 0.3342–1.2764, the minimum is observed in the month of July and the maximum in December and the variations during the daily course of the Kt* determined according to its standard deviation show such suitability to the model adopted for the calculation of global irradiation under the clear sky, as an appropriate choice of time interval and amplitude for the study of variations.

Downloads

Download data is not yet available.

References

Arias-Castro, E.; Kleissl, J. & Lave, M. (2014). A Poisson model for anisotropic solar ramp rate correlations, Sol. Energy, 101, 182–200.https://doi.org/10.1016/j.solener.2013.12.028

Barreto, E. J. F. & Pinho, J. T. (2008). Hybrid Systems, Energy Solutions for the Amazon; Ministry of Mines, 1st Edition, Brazil.https://www.mme.gov.br/luzparatodos/downloads/Solucoes_Energeticas_para_a_Amazonia_Hibrido.pdf

Belúcio, L. P., Silva, A. P. N. D., Souza, L. R., & Moura, G. B. D. A. (2014). Radiação solar global estimada a partir da insolação para Macapá (AP). Revista Brasileira de Meteorologia, 29, 494-504.https://doi.org/10.1590/0102-778620130079

Burilo, G. A.; Estefanel, V.; Heldwein, A. B.; Prestes, S.D. & Horn, J. F. C. (2012). Estimativa da Radiação Solar Global A partir dos Dados de Insolação, Para Santa Maria – RS, Ciência Rural, 42, 1563-1567, Brazil.https://doi.org/10.1590/S0103-84782012005000059

Cumbane, J. J. (1994). Estudo do Efeito de Temperatura no Rendimento das Células Solares, Eduardo Mondlane University, 1, 19-78, Mozambique.https://energypedia.info/images/0/0f/PT_temperatura_das_celulas_solares_Cumbane.pdf

Corel draw (2020).https://coreldraw.en.uptodown.com/windows

Calif, R.; Schmitt, F. G.; Huang, Y., & Soubdhan, T. (2013). Intermittency study of high frequency global solar radiation sequences under a tropical climate, Sol. Energy, 98, 249–366.https://doi.org/10.1016/j.solener.2013.09.018

Curtright, A. E. & Apt, J. (2018). The character of power output from utility-scale photovoltaic systems, Prog. Photovoltaics, 16, 239–259,https://doi.org/10.1002/pip.786

Devore, J. (2015). Probability and Statistics for Engineering and the Sciences, Brooks/Cole, Cengage Learning, Boston.https://faculty.ksu.edu.sa/sites/default/files/probability_and_statistics_for_engineering_and_the_sciences.pdf

Duffie, A. J. & Beckman, A. W. (1991). Solar Engineering of Thermal Processes, 2ª edition, John Wiley and Sons INC, 1, USA, New York.https://www.sku.ac.ir/Datafiles/BookLibrary/45/John%20A.%20Duffie,%20William%20A.%20Beckman(auth.)-Solar%20Engineering%20of%20Thermal%20Processes,%20Fourth%20Edition%20(2013).pdf

EP – Energy Pedia (2022).https://energypedia.info/wiki/Potencial_em_Energias_Renov%C3%A1veis

Elsinga, B. & van Sark, W. (2014). Spatial power fluctuation correlations in urban rooftop photovoltaic systems: Spatial power fluctuation correlations, Prog. Photovoltaics, 23, 1380–1400.https://doi.org/10.1002/pip.2539

Freitas, S. S. (2008). Dimensionamento de Sistemas Fotovoltaicos, Master Thesis.https://bibliotecadigital.ipb.pt/bitstream/10198/2098/1/Susana_Freitas_MEI_2008.pdf

Fernando, D. M. Z. (2018). Irradiação Solar Global para Cidade de Maputo - Moçambique: Evolução Temporal das Medidas, Estudo da Cobertura do Céu e Modelagem Estatística, Master Thesis, Botucatu, Brazil.https://repositorio.unesp.br/bitstream/handle/11449/180261/fernando_dmz_me_botfca.pdf?sequence=3&isAllowed=y

FUNAE (2012, 2013 e 2014). Solar radiation datta

Fernando, D. M. Z. (2018). Irradiação Solar Global para Cidade de Maputo - Moçambique: Evolução Temporal das Medidas e Modelagem Estatística, Botucatu, Brasil.https://revistas.fca.unesp.br/index.php/energia/article/view/EnergAgric.2019v34n1p82-93

FUNAE–National Fund of Energy, (2022).https://funae.co.mz/quem-somos/

Gallego, C., Costa, A., Cuerva, A., Landberg, L., Greaves, B., & Collins, J. (2013). A wavelet-based approach for large wind power ramp characterization: A wavelet-based approach for large wind power ramp characterisation, Wind Energy, 16, 236–274.https://doi.org/10.1002/we.550

Greenpro (2004). Energia Fotovoltaica - Manual Sobre Tecnologia Projecto e Instalação de Sitemas Fotovoltaicos, European Union.https://onlinelibrary.wiley.com/doi/epdf/10.1002/we.550

Google Earth (2021).https://www.googleearth.com

Hottel, H.C. (1971). Solar Energy, A simple model, for estimating the transmittance for direct Solar radiation Thought Clear Atmosphere, USA, New York.https://www.osti.gov/biblio/7348362;

Hoff, T. E. & Perez, R. (2010). Quantifying PV power output variability, Sol. Energy, 84, 1744–1830.https://www.solaranywhere.com/wp-content/uploads/2021/07/081_QuantifyingPVPowerOutputVariability.pdf

Hoff, T. E. & Perez, R. (2012). Modeling PV fleet output variability, Sol. Energy, 86, 1000–2190,https://doi.org/10.1016/j.solener.2011.11.005

Hinkelman, L. M. (2013). Differences between along-wind and cross-wind solar irradiance variability on small spatial scales, Sol. Energy, 88, 189–303,https://doi.org/10.1016/j.solener.2012.11.011

Inman, R. H.; Pedro, H. T. C. & Coimbra, C. F. M. (2013). Solar forecasting methods for renewable energy integration, USA.https://doi.org/10.1016/j.pecs.2013.06.002

Izidine, P. (2008). Elaboração de um atlas de ventos para Moçambique usando o Modelo Regional do Clima RegCM, Eduardo Mondlane University, Department of Physics, Mozambique.http://monografias.uem.mz/bitstream/13456789/590/1/2008%20-%20Pinto%2C%20Izidine.pdf;

INAM. (2020, 2021 and 2022). Solar radiation datta – Traditional station.

INAM. (2020, 2021, 2022). Solar radiation datta – Automatic station (Davis Station).

Iqbal, M. (1983). An introduction to solar radiation, Academic Toronto.https://shop.elsevier.com/books/an-introduction-to-solar-radiation/iqbal/978-0-12-373750-2

Kumar, D. (2016). Sacramento Solar Variability, A Thesis Presented to Faculty of The Department of Mechanical Engineering, California State University Sacramento, Master of science in Mechanical Engineering, California, Sacramento (not published)

Klima, K. & Apt, J. (2015). Geographic smoothing of solar PV: results from Gujarat, Environ. Res. Lett., 10, 104001.https://doi.org/10.1088/1748-9326/10/10/104001

Lohmann, G.M.; Monahan, A.H. & Heinemann, D. (2016). Local short-term variability in solar irradiance, Atmos. Chem. Phys. 16, 6365–6379, Canada.https://doi.org/10.5194/acp-16-6365-2016

Lohmann, G. M. (2018). Irradiance Variability Quantification and Small-Scale Averaging in Space and Time: A Short Review, Energy Meteorology Group, Institute of Physics, June, Oldenburg University, Germany.https://doi.org/10.3390/atmos9070264

Liu, B. Y. H. & Jordan, R. C. (1960). Solar energy The Interrelationship and Characteristic Distribution of Direct and Total Solar Radiation, 4, USA, New York.https://doi.org/10.1016/0306-2619(87)90044-4

Lave, M. and Kleissl, J. (2010). Solar variability of four sites across the state of Colorado, Renew. Energ., 35, 2633–2944.https://doi.org/10.1016/j.renene.2010.05.013

Lave, M. & Kleissl, J. (2013). Cloud speed impact on solar variability scaling – Application to the wavelet variability model, Sol. Energy, 91, 10–19.https://doi.org/10.1063/5.0050428

Lave, M., Kleissl, J., & Arias-Castro, E. (2012). High-frequency irradiance fluctuations and geographic smoothing, Sol. Energy, 86, 2080–2233.https://doi.org/10.1016/j.solener.2011.06.031

Lave, M., Kleissl, J., & Stein, J. S. (2013). A Wavelet-Based Variability Model (WVM) for Solar PV Power Plants, IEEE Transactions on Sustainable Energy, 4, 411–612.https://doi.org/10.1109/TSTE.2012.2205716

Lonij, V. P., Brooks, A. E., Cronin, A. D., Leuthold, M., & Koch, K. (2013). Intra-hour forecasts of solar power production using measurements from a network of irradiance sensors, Sol. Energy, 97, 56–70.https://doi.org/10.1016/j.solener.2013.08.002

Luoma, J., Kleissl, J., & Murray, K. (2012). Optimal inverter sizing considering cloud enhancement, Sol. Energy, 86, 421–430.https://doi.org/10.1016/j.solener.2011.10.012

Madhavan, B. L., Kalisch, J., & Macke, A. (2016). Shortwave surface radiation network for observing small-scale cloud inhomogeneity fields, Atmos. Meas. Tech., 9, 1090–1200.https://doi.org/10.5194/amt-9-1153-2016

Marcos, J., Marroyo, L., Lorenzo, E., Alvira, D. & Izco, E. (2011). From irradiance to output power fluctuations: the PV plant as a low pass filter, Prog. Photovoltaics, 19, 415–600, 2011.https://onlinelibrary.wiley.com/doi/abs/10.1002/pip.1063

Marcos, J., Marroyo, L., Lorenzo, E., Alvira, D., & Izco, E. (2011). Power output fluctuations in large scale PV plants: One year observations with one second resolution and a derived analytic model, Prog. Photovoltaics, 19, 218–227.https://doi.org/10.1002/pip.1016

Mills, A. (2011). Implications of Wide-Area Geographic Diversity for Short-Term Variability of Solar Power, Lawrence Berkeley National Laboratory, Berkeley, California, USA.https://www.osti.gov/servlets/purl/986925

Macedo, Al. S. & Fisch, G. (2017). Variabilidade Temporal da Radiação Solar Durante o Experimento GOAmazon 2014/15, Revista Brasileira de Meteorologia, 33(1), 353-365, Brazil.https://doi.org/10.1590/0102-7786332017. Available online:https://www.scielo.br/j/rbmet/a/FXTDwZB6hWzgyJbRYdDBTbs/?lang=pt, accessed on May 01, 2021 at 18:25 PM

Melo, V. F. (2003). Estudo do Comportamento da Radiação Solar na Região Sul do Save, Eduardo Mondlane University, 1(1), Mozambique.https://energypedia.info/wiki/File:PT Estudo_do_comportamento_da_radiacao_solar_da_regiao_Sul_do_Save-Victor_Fl%C3%A1vio_de_Melo.pdf

Mucomole, F. V., Dombo, C & Cuamba, B. C. (2013). Dimensionameto de Um sistema Fotovoltaico Para Fornecer Energia Eléctrica Numa Incubadora, Licenciate final work, Eduardo Mondlane University, Mozambique.https://pt.scribd.com/

Mucomole, F. V., Bnitez, E. R. V & Cuambe, V. A. (2021). Variabilidade temporal da disponibilidade da energia solar nas condições da Cidade de Maputo – caso do ano 2012, Master thesis, Eduardo Mondlane University, Mozambique

NOAA (2020, 2021 and 2022). Solar radiation datta.http://www.ncdc.noaa.gov/orders/isd/3072547975015dat.txt

Neggers, R. A. J., Jonker, H. J. J. & Siebesma, A. P. (2003). Size statistics of cumulus cloud populations in large-eddy simulations, J. Atmos. Sci., 60, 1050–1074.https://journals.ametsoc.org/view/journals/atsc/60/8/1520-0469_2003_60_1060_ssoccp_2.0.co_2.xml

Ohmura, A.; Gilgen, H.; Hegner, H.; Müller, G.; Wild, M. ; Dutton, E. G. ; Forgan, B.; Fröhlich, C.; Philipona, R.; Heimo, A.; König-Langlo, G.; McArthur, B.; Pinker, R.; Whitlock, C. H.; & Dehne, K.. (1998). Baseline Surface Radiation Network (BSRN/WCRP): New Precision Radiometry for Climate Research, B. Am. Meteorol. Soc. 79, 2115–2136.https://doi.org/10.1175/15200477(1998)079 <2115:BSRNBW>2.0.CO;2

Perpiñán, O.; Marcos, J. & Lorenzo, E. (2009). Electrical power fluctuations in a network of DC/AC inverters in a large PV plant: Relationship between correlation, distance and time scale, Sol. Energy, 88, 227–241, 2-13.https://doi.org/10.1016/j.solener.2012.12.004

Piacentini, R. D.; Salum, G. M.; Fraidenraich, N. & Tiba, C. (2011). Extreme total solar irradiance due to cloud enhancement at sea level of the NE Atlantic coast of Brazil, Renew. Energ., 36, 409–412.https://doi.org/10.1016/j.renene.2010.06.009

Paint Net (2022).https://www.getpaint.net/download.html

Python 3.8.5 software (2020).https://python.en.uptodown.com/windows

Perez, R.; David, M., Hoff, T.; Kivalov, S.; Kleissl, J.; Lauret, P. & Perez, M. (2016). Spatial and Temporal Variability of Solar Energy, Foundations and Trens in Renewable Energy, USA, New York.https://doi.org/10.1561/2700000006

PER–Portal of Renewable Energies (2022).https://www.portal-energia.com/capacidade-solar-fotovoltaica-2050-148115/

Perez, R.; Kivalov, S.; Schlemmer, J.; Hemker Jr., K. & Hoff, T. E. (2012). Short-term irradiance variability: Preliminary estimation of station pair correlation as a function of distance, Sol. Energy, 86, 2170–2176.http://www.clca.columbia.edu/9_Perez_Solar_Variability.pdf

Stetz, T.; von Appen, J.; Niedermeyer, F.; Scheibner, G.; Sikora, R.; & Braun, M. (2015). Twilight of the Grids: The Impact of Distributed Solar on Germany’s Energy Transition, Power and Energy Magazine, IEEE, 13, 50–61.https://doi.org/10.1109/MPE.2014.2379971

Suri, M.; Huld, T.; Dunlop, E.; Albuisson, M.; Lefevre, M. & Wald, L. (2007). Uncertainties in solar electricity yield prediction from fluctuation of solar radiation, 22nd European Photovoltaic Solar Energy Conference, September, Milan, Italy.https://publications.jrc.ec.europa.eu/repository/handle/JRC36426

Souza, M. J. H.; Ribeiro, A.; Leite, F. P. & Gois, G. (2005). Avaliação do Modelo de Bristow & Campbell na Estimativa, Média Mensal dos Totais Diários da Irradiação Solar Global Para o Vale do Rio Doce, MG. In: Congresso Brasileiro de Agrometeorologia, Anais., Campinas: SB. Agro, CD-ROM, Brazil, Campinas.http://sbagro.org/files/biblioteca/1645.pdf

Twidell, John & Weir, Tony (1996). Renewable Energy Resources, 3rd edition, E & FN SPON editor, An Imprint of Chapman & Hall, 2, USA, New York.https://doi.org/10.4324/9781315766416

UEM (2012). Solar radiation data (one minute temporal resolution)

Van Haaren, R.; Morjaria, M. & Fthenakis, V. (2018). Empirical assessment of short-term variability from utility-scale solar PV plants: Assessment of variability from utility-scale solar PV plants, Prog. Photovoltaics, 22, 548–559.https://doi.org/10.1002/pip.2302

Vianello, R. L. & Alves, A. R. (1991). Meteorologia Básica e Aplicações, Viçosa: UFV Imprensa Universitária, 300-410, Brasil.https://www.editoraufv.com.br/produto/meteorologia-basica-e-aplicacoes-2-edicao/1110587

Wenham, Stuart R.; Green, M.A; Watt, M.E. & Corkish, R. (2007). Applied Photovoltaics, Second Edition, Britsh library, Earthscan in the UK and USA.https://10.4324/9781849776981;

Yordanov, G.; Saetre, T. & Midtgard, O.-M. (2013). Optimal temporal resolution for detailed studies of cloud-enhanced sunlight (Overirradiance), Photovoltaic Specialists Conference (PVSC), IEEE 39th, 0985–0988.https://doi.org/10.1109/PVSC.2013.6744306,2013b

Yordanov, G.; Midtgård, O.-M.; Saetre, T.; Nielsen, H. & Norum, L. (2013). Overirradiance (Cloud Enhancement) Events at High Latitudes, IEEE Journal of Photovoltaics, 3, 271–277.https://10.1109/JPHOTOV.2012.2213581

Zekai, S. (2008). Solar Energy Fundamentals and Modeling Techniques, Atmosphere Environment Climate Change and Renewable Energy, London, England.https://download.e-bookshelf.de/download/0000/0078/33/L-G-0000007833-0002336688.pdf

Published

2023-03-27

How to Cite

Mucomole, F. V., Silva, C. A. S., & Magaia, L. L. (2023). Temporal Variability of Solar Energy Availability in the Conditions of the Southern Region of Mozambique.American Journal of Energy and Natural Resources,2(1), 27–50. https://doi.org/10.54536/ajenr.v2i1.1311

License

Copyright (c) 2023 Fernando V. Mucomole, Carlos A. S. Silva, Lourenço L. Magaia

Creative Commons License

This work is licensed under aCreative Commons Attribution 4.0 International License.

Most read articles by the same author(s)