Integration of a Monitoring System of Climatic Conditions to the National Energy Management System

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José Enríquez
https://orcid.org/0000-0002-3711-9588
Carlos Del Hierro
https://orcid.org/0000-0001-5795-0312
Roberto Sánchez
https://orcid.org/0000-0002-5824-5137
David Panchi

Abstract

This work presents the development of a data acquisition system in real time for climatic variables such as temperature and climatic conditions; and its integration with the Energy Management System - EMS of CENACE. The acquired climatic variables will be used to analyze the relationship between temperature and electricity demand in different cities of Ecuador. Additionally, the presentation of meteorological conditions in an operational display within SCADA / EMS will help decision-making in the real-time operation of the National Interconnected System. This article shows the different concepts and components used for its development, as well as the results achieved at the visualization level.
Finally, the results of an analysis of the variability of the daily hourly demand for six months will be presented, focusing on the cities of Guayaquil, Quito and Cuenca. Through the data mining technique called Orthogonal Empirical Functions, this work explains the existing correlation between the amplitude of the orthogonal empirical vectors calculated of the demand and the temperature of each city.

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How to Cite
Enríquez , J., Del Hierro, C. ., Sánchez, R., & Panchi, D. (2021). Integration of a Monitoring System of Climatic Conditions to the National Energy Management System . Revista Técnica "energía", 17(2), PP. 124–132. https://doi.org/10.37116/revistaenergia.v17.n2.2021.441
Section
TECNOLÓGICOS E INNOVACIÓN

References

[1] P. C. Unit, “Network Manager Training Process Communication Unit , PCU400 General solution for multi protocol data acquisition from RTUs / IEDs,” no. May, 2012.
[2] C. D. E. T. Se and S. Internacional, “Capítulo 3 : Elementos sensores y transductores de temperatura,” pp. 23–68, 2018.
[3] P. Van Eijsden, “Situational awareness,” Ned. Tijdschr. Geneeskd., vol. 159, no. 30, pp. 186–187, 2015.
[4] “Weather API - OpenWeatherMap.” .
[5] “Python’s Requests Library (Guide) – Real Python.” .
[6] D. K. Mahto and L. Singh, “A dive into Web Scraper world,” in 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), 2016, pp. 689–693.
[7] C. Giannakopoulos and B. Psiloglou, “Trends in energy load demand for Athens, Greece: weather and on-weather related factors,” Clim. Res., vol. 31, pp. 97–108, 2006.
[8] A. Henley and J. Peirson, “Non‐Linearities in Electricity Demand and Temperature: Parametric Versus Non‐Parametric Methods,” Oxf. Bull. Econ. Stat., vol. 59, no. 1, pp. 149–162, 1997.
[9] M. Ali, M. J. Iqbal, and M. Sharif, “Relationship between extreme temperature and electricity demand in Pakistan,” Int. J. Energy Environ. Eng., vol. 4, no. 1, p. 36, 2013.
[10] C. L. Hor, S. J. Watson, and S. Majithia, “Analyzing the impact of weather variables on monthly electricity demand,” IEEE Trans. Power Syst., vol. 20, no. 4, pp. 2078–2085, 2005.
[11] M. Bessec and J. Fouquau, “The non-linear link between electricity consumption and temperature in Europe: A threshold panel approach,” Energy Econ., vol. 30, no. 5, pp. 2705–2721, 2008.
[12] A. Pardo, V. Meneu, and E. Valor, “Temperature and seasonality influences on Spanish electricity load,” Energy Econ., vol. 24, no. 1, pp. 55–70, 2002.
[13] D. J. Sailor and J. R. Muiqoz, “Pergamon pll:. S0360-5442(97)000M-0,” vol. 22, no. 10, pp. 987–998, 1997.
[14] H. Moazamigoodarzi, R. Gupta, S. Pal, P. J. Tsai, S. Ghosh, and I. K. Puri, “Modeling temperature distribution and power consumption in IT server enclosures with row-based cooling architectures,” Appl. Energy, vol. 261, no. August 2019, pp. 1–13, 2020.
[15] M. Shakeri et al., “An intelligent system architecture in home energy management systems (HEMS) for efficient demand response in smart grid,” Energy Build., vol. 138, pp. 154–164, 2017.
[16] E. Valor, V. Meneu, and V. Caselles, “Daily Air Temperature and Electricity Load in Spain,” J. Appl. Meteorol., vol. 40, no. 8, pp. 1413–1421, Aug. 2001.
[17] “OpenWeatherMap® API: Get Historical & Current Weather Data | RapidAPI.” .
[18] S. Alburqueque, “Funciones ortogonales empíricas y su aplicación a datos de temperatura superficial del mar,” 2019.
[19] J. C. Cepeda, Real Time Vulnerability Assessment of Electric Power Systems Using Synchronized Phasor Measurement Technology. 2013.
[20] 2011 Bruce, “Variabilidad y tendencias del nivel del mar en las costas de las penìnsula Ibérica y zonas limítrofes:su relación con parámetros meteorológicos,” J. Chem. Inf. Model., vol. 53, no. 9, pp. 1689–1699, 2013.
[21] M. Kezunovic and A. Bose, “The future EMS design requirements,” Proc. Annu. Hawaii Int. Conf. Syst. Sci., pp. 2354–2363, 2013.
[22] P. Schober and L. A. Schwarte, “Correlation coefficients: Appropriate use and interpretation,” Anesth. Analg., vol. 126, no. 5, pp. 1763–1768, 2018.

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