International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Clustering of PV Load Patterns Based on Any Colony Centroid Model

  • Munshi, Amr (Computer Engineering Department, Umm Al-Qura University)
  • Received : 2022.05.05
  • Published : 2022.05.30
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Abstract

There has been a significant growth in global population and industrialization, as a consequence demand for electricity is increasing rapidly and the power systems need to increase the electricity generation. Currently, most of generated electricity is generated from fossil fuels. However, there are many financial and environmental concerns associated with the generation of electricity from such resource. Photovoltaic )PV) solar as a renewable resource is promising. The power output of PV systems is mainly affected by the solar irradiation and ambient temperature. This paper attempts at reducing the burden and improving the accuracy of the extensive simulations related to integrating PV systems into the electrical grid.

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References

  1. IEA (2019), World Energy Outlook 2019, IEA, Paris [Online]. Available: https://www.iea.org/reports/worldenergy-outlook-2019
  2. IEA (2020), Renewables 2020 Analysis and forecast to 2025, IEA, Paris [Online]. Available: https://iea.blob.core.windows.net/assets/3350006e-c203-4b21-aa45-faefd36f22ad/Renewables2020-ExecutiveSummary.pdf
  3. G. Chicco, R. Napoli, P. Postolache, M. Scutariu, and C. Toader, "Customer characterization options for improving the tariff offer," IEEE Trans. Power Syst., vol. 18, no. 1, pp. 381-387, Feb. 2003.
  4. V. Figueiredo, F. Rodrigues, Z. Vale, and J. B. Gouveia, "An electric energy consumer characterization framework based on data mining techniques," IEEE Trans. Power Syst., vol. 20, no. 2, pp. 596-602, May 2005. https://doi.org/10.1109/TPWRS.2005.846234
  5. D. Gerbec, S. Gasperic, I. Smon, and F. Gubina, "Determining the load profiles of consumers based on fuzzy logic and probability neural networks," Proc. Inst. Elect. Eng., Gener. Transm., Distrib., vol. 151, no. 3, pp. 395-400, 2004. https://doi.org/10.1049/ip-gtd:20040472
  6. A. Munshi, and Y. A.-R. I. Mohamed, "Comparisons among Bat algorithms with various objective functions on grouping photovoltaic power patterns," Solar Energy, vol. 144, pp. 254-266, 2017. https://doi.org/10.1016/j.solener.2017.01.034
  7. G. Chicco, R. Napoli, and F. Piglione, "Load pattern clustering for short-term load forecasting of anomalous days," Proc. IEEE PowerTech Conf., Porto, 10-13 Sept 2001, vol. 2.
  8. A. Gabaldon, A. Guillamon, M. C. Ruiz, S. Valero, C. Alvarez, M. Ortiz, and C. Senabre, "Development of a methodology for clustering electricity-price series to improve customer response initiatives," IET Gener., Transm., Distrib., vol. 4, no. 6, pp. 706-715, 2010. https://doi.org/10.1049/iet-gtd.2009.0112
  9. G. J. Tsekouras, C. A. Anastasopoulos, F. D. Kanellos, V. T. Kontargyri, I. S. Karanasiou, A. D. Salis, and N. E. Mastorakis, "A demand side management program of vanadium redox energy storage system for an interconnected power system," Proc. WSEAS EPESE, Corfu Island, Greece, 2008.
  10. G. J. Tsekouras, F. D. Kanellos, V.T. Kontargyri, I. S. Karanasiou, A. D. Salis, and N. E. Mastorakis, "A new classification pattern recognition methodology for power system typical load profiles," WSEAS Trans. Circuits and Systems, vol. 12, no. 7, pp. 1090-1104, 2008.
  11. G. J. Tsekouras, I.K. Hatzilau, J. Prousalidis, "A new pattern recognition methodology for classification of load profiles for ships electric consumers," Journal of Marine Engineering and Technology, no. A14, pp. 45-58, 2009.
  12. G. Tsamopoulos, N. Giannitsas, F. D. Kanellos, and G. J. Tsekouras, "Load estimation for war-ships based on pattern recognition methods," Journal of Computations and Modeling, vol. 4, no. 1, pp. 207-222, 2014.
  13. M. Ali, I. S. Ilie, J. V. Milanovic, and G. Chicco, "Wind farm model aggregation using probabilistic clustering," IEEE Trans. Power Syst., vol. 28, no.1, pp. 309-316, Feb. 2013. https://doi.org/10.1109/TPWRS.2012.2204282
  14. F. J. Duarte, J. M .M. Duarte, S. Ramos, A. Fred, and Z. Vale, "Daily wind power profiles determination using clustering algorithms," Proc. IEEE Power Syst. Tech., pp. 1-6, Oct. 30-Nov. 2 2012.
  15. G. Farivar, B. Asaei, N. Haghdadi, and H. Iman-Eini, "A novel temperature estimation method for solar cells," Proc. PEDSTC, pp. 336-341, 16-17 Feb. 2011.
  16. P. S. Shelokar, V. K. Jayaraman, and B. D. Kulkarni, "An ant colony approach for clustering," Analytica Chimica Acta, vol. 504, pp. 187-195, 2004.
  17. A. P. Engelbrecht, Computational Intelligence: An Introduction, 2nd Edition, John Wiley & Son, 2007, pp. 135-137.
  18. N. Bolshakova and F. Azuaje, "Cluster validation techniques for genome expression data", Signal Processing, vol. 83, pp. 825-833, 2002. https://doi.org/10.1016/S0165-1684(02)00475-9
  19. R. Jain, and R. Koronios, "Innovation in the cluster validating techniques," Fuzzy Optimization and Decision Making, vol. 7, no. 3, pp. 233-241, 2008. https://doi.org/10.1007/s10700-008-9033-2
  20. Solar Radiation Research Laboratory (BMS) available online at: http://www.nrel.gov/midc/srrl_bms.