Nueva aproximación estadística a la clasificación de consumidores y la construcción de curvas de carga

Eduardo Sierra Gil; Alfredo Basulto Espinosa; Argelis Escalona Aguilar

doi:10.14482/inde.38.1.519.5

Authors

Eduardo Sierra Gil Universidad de Camagüey
Alfredo Basulto Espinosa Empresa Eléctrica Provincial Camagüey
Argelis Escalona Aguilar Empresa Inmobiliaria ALMEST

DOI:

https://doi.org/10.14482/inde.38.1.519.5

Keywords:

Distribution network, Distribution transformers, Electrical demand, Load profile

Abstract

It is of utmost importance in an electrical distribution system to have a detailed knowledge of the characteristics of the loads it feeds and that they determine, in a final extend, the behavior of parameters in the different regimens of operation, there are various methods for the classification of consumers and the construction of the typical daily load curves, however these methods do not mainly consider that these curves are subjected to the conduct of each kind of consumers. This work proposes a new approximation to this problem based on a method sustained by two statistical tools, Kendall matching coefficient and the correlation coefficient for ranges stated by Spearman and it is checked its effectiveness by means of its application in two distribution circuits, demonstrating that there is a coincidence between the load profiles obtained through the method proposed and the load profiles obtained through measurements accomplished at the substation.

Author Biography

Eduardo Sierra Gil, Universidad de Camagüey

Es el Jefe del Departamento de Proyectos de Investigación, adscrito a la Dirección de Ciencia Tecnología e Innovación de la Universidad de Camagüey además se ha desempeñado como Vicedecano de Investigación y Postgrado de la Facultad de Electromecánica, por lo que posee una vasta experiencia en la gestión de la ciencia y de proyectos I+D+i. Es un reconocido investigador en el área de la Energía con resultados investigativos publicados en 24 artículos científicos en memorias de eventos internacionales y en revistas de reconocido prestigio. Ha impartido 28 cursos de postgrado en Diplomados y Maestrías y ha asesorado 12 tesis de maestría. Además de estar vinculado a 10 proyectos de investigación, desempeñándose como Jefe de Proyecto en 3 de ellos. Lidera la Línea Científica Universitaria de Energía y el Grupo de Investigación Multidisciplinario de Energía de la Universidad de Camagüey. Desde el 2012 está certificado como experto de la Junta Nacional de Acreditación, Liderando varios procesos de acreditación, como directivo evaluado, en programas de pregrado y postgrado.

References

Y. Wang, Q. Chen, C. Kang, M. Zhang, K. Wang, Y. Zhao. “Load profiling and its application to demand response: A review”. Tsinghua Science and Technology. Vol. 20. 2015. pp. 117-129. Doi: 10.1109/TST.2015.7085625

A. Mutanen, M. Ruska, S. Repo, P. Jarventausta. “Customer Classification and Load Profiling Method for Distribution Systems” IEEE Transactions on Power Delivery. Vol. 26.2011. pp. 1755-1763. Doi: 10.1109/TPWRD.2011.2142198

J. A. Jardini, M.V. Tahan, M.R. Gouvea, S. Un Ahn. “Daily load profiles for residential, commercial and industrial low voltage consumers”. IEEE Transactions on Power Delivery. Vol. 15. 2000. pp. 375-380. Doi: 10.1109/61.847276

C. Mihai, I. Lepadat, E. Helerea, D. Câlin. “Load curve analysis for an industrial consumer”. in IEEE 12th International Conference on Optimization of Electrical and Electronic Equipment (OPTIM). Bazov. 2010. pp. 1275–1280. Doi: 10.1109/OPTIM.2010.5510494

J. Dickert, P. Schegner. “A time series probabilistic synthetic load curve model for residential customers”. in IEEE PowerTech Conference. 2011. pp. 1-6. Doi: 10.1109/PTC.2011.6019365

P. R. Harvey, B. Stephen, S. Galloway. “Classification of AMI Residential Load Profiles in the Presence of Missing Data”. IEEE Transactions on Smart Grid. Vol. 7. 2016. pp. 1944-1945. Doi: 10.1109/TSG.2016.2558459

F. McLoughlin, A. Duffy, M. Conlon. “A clustering approach to domestic electricity load profile characterisation using smart metering data”. Applied Energy. Vol. 141. 2015. pp. 190–199. Doi: 10.1016/j.apenergy.2014.12.039

J. L. Viegasa, S.M. Vieiraa, R. Melícioa, V.M.F. Mendesb, J.M.C. Sousaa. “Classification of new electricity customers based on surveys and smart metering data”. Energy. Vol. 107. 2016. pp. 804–817. Doi: 10.1016/j.energy.2016.04.065

G. Chicco, R. Napoli, F. Piglione. “Comparisons among clustering techniques for electricity customer classification”. IEEE Transactions on Power Systems. Vol. 21. 2006. pp. 933-940. Doi: 10.1109/TPWRS.2006.873122

D. Jang, J. Eom, M.J. Park, J.J. Rho. “Variability of electricity load patterns and its effect on demand response: A critical peak pricing experiment on Korean commercial and industrial customers”. Energy Policy. Vol. 88. 2016. pp. 11–26. Doi:10.1016/j.enpol.2015.09.029

S. V. Verdu, M.O. Garcia, C. Senabre, A.G. Marin, F.J.G. Franco “Classification, Filtering, and Identification of Electrical Customer Load Patterns Through the Use of Self-Organizing Maps”. IEEE Transactions on Power Systems. Vol. 21. 2006. pp. 1672-1682. Doi: 10.1109/TPWRS.2006.881133

E. D. Varga, S.F. Beretka, C. Noce, G. Sapienza. “Robust Real-Time Load Profile Encoding and Classification Framework for Efficient Power Systems Operation”. IEEE Transactions on Power Systems. Vol. 30. 2015. pp. 1897-1904. Doi:10.1109/TPWRS.2014.2354552

J. Bulckaen, I. Keseru, C. Macharis. “Sustainability versus stakeholder preferences: Searching for synergies in urban and regional mobility measures”. Research in Transportation Economics. Vol. 55. 2016. pp. 40–49. Doi:10.1016/j.retrec.2016.04.009

X. Qin, Y. Mo, L. Jing. “Risk perceptions of the life-cycle of green buildings in China”. Journal of Cleaner Production. Vol. 126. 2016. pp. 148–158. Doi: 10.1016/j.jclepro.2016.03.103

W. Xu, Z. Chen, W. Liu. “A new coefficient of concordance with applications to biosignal analysis” in MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, Enshi, China 2015. Doi:10.1117/12.2210993

T. W. MacFarland, J. M. Yates. “Spearman’s Rank-Difference Coefficient of Correlation”. in Introduction to Nonparametric Statistics for the Biological Sciences Using R, ed Cham: Springer International Publishing. 2016. pp. 249-297. Doi:10.1007/978-3-319-30634-6_8

M. T. Puth, M. Neuhäuser, G.D. Ruxton. “Effective use of Spearman’s and Kendall’s correlation coefficients for association between two measured traits”. Animal Behaviour. Vol. 102. 2015. pp. 77–84. Doi: 10.1016/j.anbehav.2015.01.010

O. Urbano, et al., “Enfoque técnico-económico para el dimensionamiento de transformadores de distribución,” Ingeniería y Desarrollo, Vol. 34. 2016. pp. 267-285. Doi: 10.14482/inde.33.2.6368