Estimating the Spot Market Price Bid in Colombian Electricity Market by Using Artificial Intelligence
DOI:
https://doi.org/10.46661/revmetodoscuanteconempresa.2205Keywords:
Mercado eléctrico al contado, precio ofertado, Inteligencia Artificial, Lógica Difusa, wholesale energy market, price bid, Artificial Intelligence, Fuzzy LogicAbstract
One of the most important economic strategic sectors in any economy is the electricity market. Its main feature is its oligopolistic character favoured by the returns to scale which act as an entry barrier. As a result, the energy generators can use their power market in order to increase their benefits through the daily offered price and quantity of energy for each of their power plants. This paper presents a methodology for estimating the daily offered price of the most important power stations in Colombia (hydraulic and thermal) by applying artificial intelligence techniques: Fuzzy Logic and Neural Networks. Such techniques are found to be partially useful particularly for price tendencies. It also compares the results with autoregressive models that turned out inappropriate for the case of study.
Downloads
References
Aedo, C. (2005). Evaluación de impacto. Santiago de Chile: Naciones Unidas.
Álvarez, H.D. y Correa, G.J. (2002). Sistemas de Lógica Difusa (notas de clase). Medellín: Univesidad Nacional de Colombia. Septiembre.
Arellano, M. (2001). Introducción al análisis clásico de series de tiempo. Recuperado el 26 de julio de 2012, de: http://ciberconta.unizar.es/LECCION/seriest/100.htm
Azim, M., Jaffar, A. y Mirza, M. (2014). Fully automated real time fatigue detection of drivers through fuzzy expert systems, Applied Soft Computing 18, 25–38
Babuska, R. (1998). Fuzzy modeling for control. Kluwer Academic Publishers.
Bastidas, M., Quintero, O.L. y García, J. (2012). Inteligencia de Mercados: comportamientos estratégicos sobre precios de oferta en el pool eléctrico colombiano. Lima, Perú: XV Congreso Latinoamericano de Control Automático CLCA. Octubre.
Bolsa de Energía. (2012). Recuperado el 1 de agosto de 2012, de: http://web.ing.puc.cl/~power/alumno05/colombia/Proyect%20web_archivos/page0009.htm
Botero, J.A., García, J.J. y Vélez, L.G. (2013). Mecanismos utilizados para monitorear el poder de mercado en mercados eléctricos: reflexiones para Colombia. Cuadernos de Economía 32(60), 571–597.
Business Col (2012). BusinessCol-Sección Productos y Recursos. Glosario Contable. Recuperado el 12 de noviembre de 2012, de: http://www.businesscol.com/productos/glosarios/contable/glossary.php?word=INDICE%20DE %20PRECIOS%20AL%20PRODUCTOR%20(IPP)
Carlton, D. y Perloff, J. (2004). Modern industrial organization, 3rd ed., Addison-Wesley.
Comisión Federal de Regulación de los Estados Unidos –FERC (2006). Prohibition of energy market manipulation, Docket No. RM06-3.
Cruz, A., Muñoz, A., Zamora, J.L. y Espínola, R. (2011). The effect of wind generation and weekday on Spanish electricity spot price forecasting. Electric Power Systems Research, 1924– 1935.
De Frutos, M.A. y Fabra, N. (2008). On the impact of forward contract obligations in multi-unit auctions. CEPR Discussion Cap. nº 6756.
De Medeiros, L. (2003). Previsão do preço no mercado de energia elétrica. Río de Janeiro: Tesis de doctorado. Universidade Católica do Rio de Janeiro.
Domínquez Piedrahita, D.M. (2011). Curso de Electrónica Básica, Software aplicado y Química. Tecnológico Pascual Bravo-Institución Universitaria. Recuperado el 26 de julio de 2012, de: http://dianamardp.webnode.es/news/normas-icontec-2012/
Duque, I.E. (2009). Slideshare. Recuperado el 26 de julio de 2012, de: http://www.slideshare.net/guest5672989/normas-icontec (abril)
Earth System Research Laboratory. (2012). Multivariate ENSO Index (MEI). Recuperado el 28 de octubre de 2012, de U.S. Department of Commerce: http://www.esrl.noaa.gov/psd/enso/mei/#Home
Flood, I. (2008). Towards the next generation of artificial neural networks for civil engineering. Advanced Engineering Informatics, 4–14.
Galvis Gutiérrez, D.M. (2011). Comportamientos estratégicos sobre precios de oferta en el pool eléctrico colombiano. Medellín. 50 p. Trabajo de grado (Maestría en Economía). Universidad EAFIT. Escuela de Administración. Departamento de Economía.
García-Díaz, A. y Marín, P. (2003). Strategic bidding in electricity pools with short-lived bids: An application to the Spanish market. International Journal of Industrial Organization, 21(2), 201–222.
Gareta, R., Romeo, L.M. y Gil, A. (2006). Forecasting of electricity prices with neural networks. Energy Conversion and Management, 1770–1778.
Garrido, S. (1999). Identificación, estimación y control de sistemas no-lineales mediante RGO. Madrid: Universidad Carlos III.
Green, R.J. (1996). Increasing competition in the British electricity spot market. Journal of Industrial Economics, 44(2), 205–216.
Green, R.J. y Newbery, D.M. (1992). Competition in the British electricity spot market. Journal of Political Economy, 100(5), 929–953.
Guthrie, G. y Videbeck, S. (2007). Electricity spot price dynamics: Beyond financial models. Energy Policy , 5614–5621.
HCMC University of Technology. (2008). Artificial neural networks. Ho Chi Minh, Vietnam.
Hermann, G. (1990). Artificial intelligence in monitoring and the mechanics of machining. Computers in Industry, 131–135.
Hong, Y.-Y., y Lee, C.-F. (2005). A neuro-fuzzy price forecasting approach in deregulated electricity markets. Electric Power Systems Research, 151–157.
Jang, J.-S., Sun, C.-T. y Mizutan, E. (1997). Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Hsinchu: Prentice Hall.
López, G. (s.f.). Teoría económica para el análisis de los mercados eléctricos. Medellín: Departamento de Economía. Universidad EAFIT.
Madan, S. y Bollinger, K.E. (1997). Applications of artificial intelligence in power systems. Electric Power Systems Research, 171–131.
Narendra, K. y Parthasarathy, K. (1990). Identification and control of dynamical systems using neural networks. IEEE Tranactions on Neural Networks, 4–27.
Olivera, J. (1997). Identification of dynamic systems using neural networks. Architecture and Civil Engineering, 525–532.
Oropeza Clavel, C.A. (2007). Modelado y simulación de un sistema de detección de intrusos utilizando redes neuronales recurrentes. Puebla: Universidad de las Américas de Puebla.
Patel, V.L., Shortliffe, E.H., Stefanelli, M., Szolovits, P., Berthold, M.R., Bellazzi, R., y otros. (2009). The coming of age of artificial intelligence in medicine. Artificial Intelligence in Medicine, 5–17.
Pau, L. y Tan, P.Y. (2006). Artificial intelligence in economics and finance: A state of the art-1994: The real estate price and assets and liability analysis case. En: Handbook of Computational Economics, 405–439. Países Bajos: North-Holland.
Prévotet, J.C. (2010). Tutorial on neural networks. París, Francia: University of Paris VI. Enero.
Romano, I. (2014). Propuesta de valoración de las influencias entre Educación y Economía, Tesis Doctoral, Universidad Pablo de Olavide, de Sevilla.
Sachin, S., Dhaneshwar, K., Garima, K., Neha, G. y Ayush, S. (2013). Congestion control in wired network for heterogeneous resources using neural networks. International Journal of Advanced Research in Computer Science and Software Engineering. 3(5), 533–537.
Sierra, J. y Castaño, E. (2010). Pronóstico del precio spot del mercado eléctrico Colombiano con modelos de parámetros variantes en el tiempo y variables fundamentales. Estadística Aplicada: "Didáctica de la Estadística y Métodos Estadísticos en Problemas Socioeconómicos" Universidad Nacional de Colombia.
Singhai, D. y Swarup, K. (2011). Electricity price forecasting using artificial neural networks. International Journal of Electrical Power & Energy Systems, 550–555.
Stats Direct (2012). P-values. Recuperado el 9 de diciembre de 2012, de: http://www.statsdirect.co.uk/help/basics/pval.htm
Swider, D.J. y Weber, C. (2007). Extended ARMA models for estimating price developments on day-ahead electricity markets. Electric Power Systems Research, 583–593.
Swinand, G., Scully, D., Ffoulkes, S. y Kessler, B. (2010). Modeling EU electricity market competition using the residual supply index. The Electricity Journal, 41–50.
Takagi, T. y Sugeno, M. (1985). Fuzzy identificacion of systems and its applications to modeling and Control. IEEE Transactions of Systems, Man and cybernetics, 116–132.
Velásquez, J.D. y Dyner, I. (2001). Pronóstico de precios de la bolsa de electricidad usando un modelo de redes neuronales artificiales. Bogotá: EITI-2001 Universidad Nacional de Colombia.
Velásquez Henao, J.D., Dyner Resonsew, I. y Castro Souza, R. (2007). ¿Por qué es tan difícil obtener pronósticos de los precios de la electricidad en los mercados competitivos? Cuadernos de Administración 259–282.
Ventosa, M., Baillo, Á., Ramos, A. y Rivier, M. (2005). Electricity market modeling trends. Energy Policy , 897–913.
Villada, F., García, E. y Molina, J.D. (2011). Pronóstico del precio de la energía Eléctrica usando redes neuro-difusas. Información Tecnológica , 111–120.
Von der Fehr, N. y Harbord, D. (1993). Spot market competition in the UK electricity industry. The Economic Journal, 103(418), 531–546.
Wackerly, D. y Mendenhall, W. (2009). Estadística matemática con aplicaciones. Grupo Editorial Iberoamérica.
Weron, R., Kozlowska, B. y Nowicka-Zagrajek, J. (2001). Modeling electricity loads in California: a continuous time approach. Physica A: Statistical Mechanics and its Applications, 344–350.
Weron, R. y Misiorek, A. (2008). Forecasting spot electricity prices: A comparision of parametric and semiparametric time series models. International Journal of Forecasting , 744–763.
Wolak, F. (2009). Report on market performance and market monitoring in the Colombian electricity supply industry. Julio. Recuperado el 12 de febrero de 2013: http://www.stanford.edu/group/fwolak/cgi-bin/sites/default/files/files/sspd_report_wolak_july_30.pdf
Wolfram, C. (1998). Strategic bidding in a multiunit auction: An empirical analysis of bids to supply electricity in England and Gales. Rand Journal of Economics, 29(4), 703–725.
XM (2012). XM. Recuperado el 9 de diciembre de 2012, de Descripción del sistema eléctrico colombiano. Vaiables de mercado 2011: http://www.xm.com.co/Pages/DescripciondelSistemaElectricoColombiano.aspx
Zapata Ramírez, C. (2011). Evaluación del comportamiento de la competencia en la actividad de generación de Manizales: Tesis de Maestría en Administración. Universidad Nacional de Colombia.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2014 Revista de Métodos Cuantitativos para la Economía y la Empresa

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Submission of manuscripts implies that the work described has not been published before (except in the form of an abstract or as part of thesis), that it is not under consideration for publication elsewhere and that, in case of acceptance, the authors agree to automatic transfer of the copyright to the Journal for its publication and dissemination. Authors retain the authors' right to use and share the article according to a personal or instutional use or scholarly sharing purposes; in addition, they retain patent, trademark and other intellectual property rights (including research data).
All the articles are published in the Journal under the Creative Commons license CC-BY-SA (Attribution-ShareAlike). It is allowed a commercial use of the work (always including the author attribution) and other derivative works, which must be released under the same license as the original work.
Up to Volume 21, this Journal has been licensing the articles under the Creative Commons license CC-BY-SA 3.0 ES. Starting from Volume 22, the Creative Commons license CC-BY-SA 4.0 is used.