Abstract
This research focuses on predicting the performance of photovoltaic (PV) systems using the PVWatts Calculator. The study examines three types of PV modules—Standard, Premium, and Thin Film—under the environmental conditions of Yogyakarta, Indonesia, with simulations set for the year 2024. Input parameters, including geographic coordinates, solar radiation intensity, average air temperature, and module specifications, were collected from reliable sources such as Suncalc. Simulations were conducted to evaluate annual and monthly energy outputs, considering factors such as system capacity, panel orientation, inverter efficiency, and system losses. The results show that Premium modules achieve the highest energy output, while Standard and Thin Film modules provide nearly comparable performance, making them cost-effective alternatives. Despite differences in efficiency, the performance gap between the module types remains relatively small under similar light intensity conditions. The findings highlight the importance of selecting PV modules based on specific needs, budget constraints, and environmental factors to optimize solar energy system performance. This study provides a comprehensive framework for PV performance calculations using PVWatts and offers valuable insights to support renewable energy development in the Yogyakarta region.
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References
[1] T. M. Razykov, C. S. Ferekides, D. Morel, E. Stefanakos, H. S. Ullal, and H. M. Upadhyaya, “Solar photovoltaic electricity: Current status and future prospects,” Sol. Energy, vol. 85, no. 8, pp. 1580–1608, 2011, doi: 10.1016/j.solener.2010.12.002.
[2] R. A. Marques Lameirinhas, J. P. N. Torres, and J. P. de Melo Cunha, “A Photovoltaic Technology Review: History, Fundamentals and Applications,” Energies, vol. 15, no. 5, pp. 1–44, 2022, doi: 10.3390/en15051823.
[3] M. Bazilian et al., “Re-considering the economics of photovoltaic power,” Renew. Energy, vol. 53, pp. 329–338, 2013, doi: 10.1016/j.renene.2012.11.029.
[4] J. J. Roberts, A. A. Mendiburu Zevallos, and A. M. Cassula, “Assessment of photovoltaic performance models for system simulation,” Renew. Sustain. Energy Rev., vol. 72, no. October, pp. 1104–1123, 2017, doi: 10.1016/j.rser.2016.10.022.
[5] S. Dervishi and A. Mahdavi, “Computing diffuse fraction of global horizontal solar radiation: A model comparison,” Sol. Energy, vol. 86, no. 6, pp. 1796–1802, 2012, doi: 10.1016/j.solener.2012.03.008.
[6] J. Freeman, J. Whitmore, N. Blair, and A. P. Dobos, “Validation of multiple tools for flat plate photovoltaic modeling against measured data,” 2014 IEEE 40th Photovolt. Spec. Conf. PVSC 2014, pp. 1932–1937, 2014, doi: 10.1109/PVSC.2014.6925304.
[7] C. S. Psomopoulos, G. C. Ioannidis, S. D. Kaminaris, K. D. Mardikis, and N. G. Katsikas, “A Comparative Evaluation of Photovoltaic Electricity Production Assessment Software (PVGIS, PVWatts and RETScreen),” Environ. Process., vol. 2, no. January 2014, pp. S175–S189, 2015, doi: 10.1007/s40710-015-0092-4.
[8] S. Mohana Krishnan, S. Rawat, M. Surender, R. Balakrishna, and R. Anandan, “Implementing an energy calculator in a mobile based application for solar potential measurement,” Int. J. Eng. Technol., vol. 7, no. 3.6 Special Issue 6, pp. 403–406, 2018, doi: 10.14419/ijet.v7i3.6.16012.
[9] S. M. Young and A. M. Rappe, “First principles calculation of the shift current photovoltaic effect in ferroelectrics,” Phys. Rev. Lett., vol. 109, no. 11, 2012, doi: 10.1103/PhysRevLett.109.116601.
[10] S. Anselmo and M. Ferrara, “Trends and Evolution of the GIS-Based Photovoltaic Potential Calculation,” Energies, vol. 16, no. 23, 2023, doi: 10.3390/en16237760.
[11] R. Daniel M., F. Jeffrey E., and G. Gerald R., “A Photovoltaic System Payback Calculator,” no. June, 2016, [Online]. Available: https://www.osti.gov/servlets/purl/1259560
[12] Y. W. Msiska, “SOLAR (PHOTOVOLTAIC) BIOGAS HYBRID ENERGY CALCULATOR FOR HOME AND FARM SETUP,” The University of Zambia Lusaka, 2019.
[13] E. B. Agyekum, U. Mehmood, S. Kamel, M. Shouran, E. Elgamli, and T. S. Adebayo, “Technical Performance Prediction and Employment Potential of Solar PV Systems in Cold Countries—A Case Study of the Sverdlovsk Region of Russia,” Sustain., vol. 14, no. 6, 2022, doi: 10.3390/su14063546.
[14] H. Alghanem and A. Buckley, “Global Benchmarking and Modelling of Installed Solar Photovoltaic Capacity by Country,” Energies, vol. 17, no. 8, pp. 1–29, 2024, doi: 10.3390/en17081812.
[15] J. Mathe, N. Miolane, N. Sebastien, and J. Lequeux, “PVNet: A LRCN Architecture for Spatio-Temporal Photovoltaic PowerForecasting from Numerical Weather Prediction,” 2019, [Online]. Available: http://arxiv.org/abs/1902.01453
[16] J. H. Yousif, H. A. Kazem, and J. Boland, “Predictive models for photovoltaic electricity production in hotweather conditions,” Energies, vol. 10, no. 7, 2017, doi: 10.3390/en10070971.
[17] J. T. Putra, Sarjiya, and M. I. B. Setyonegoro, “Modeling of high uncertainty photovoltaic generation in quasi dynamic power flow on distribution systems: A case study in Java Island, Indonesia,” Results Eng., vol. 21, no. January, p. 101747, 2024, doi: 10.1016/j.rineng.2023.101747.
[18] B. M. Ziapour, V. Palideh, and M. Baygan, “Performance comparison of four passive types of photovoltaic-thermal systems,” Energy Convers. Manag., vol. 88, pp. 732–738, 2014, doi: 10.1016/j.enconman.2014.09.011.
[19] A. Limmanee et al., “Field performance and degradation rates of different types of photovoltaic modules: A case study in Thailand,” Renew. Energy, vol. 89, pp. 12–17, 2016, doi: 10.1016/j.renene.2015.11.088.
[20] E. Elibol, Ö. T. Özmen, N. Tutkun, and O. Köysal, “Outdoor performance analysis of different PV panel types,” Renew. Sustain. Energy Rev., vol. 67, pp. 651–661, 2017, doi: 10.1016/j.rser.2016.09.051.
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