IDEAS home Printed from https://ideas.repec.org/a/pab/rmcpee/v18y2014i1p54-87.html
   My bibliography  Save this article

Estimación del precio de oferta de la energía eléctrica en Colombia mediante inteligencia artificial || Estimating the Spot Market Price Bid in Colombian Electricity Market by Using Artificial Intelligence

Author

Listed:
  • Hurtado Moreno, Laura

    () (Universidad EAFIT, Medellín (Colombia))

  • Quintero Montoya, Olga Lucía

    () (Departamento de Ciencias Básicas, Universidad EAFIT, Medellín (Colombia))

  • García Rendón, John Jairo

    () (Departamento de Economía, Universidad EAFIT, Medellín (Colombia))

Abstract

Uno de los sectores económicos estratégicos más importantes en cualquier economía es el Mercado de Energía Mayorista, cuya característica fundamental es que se trata de un mercado oligopolístico, provocado por la barrera de entrada que supone tener economías de escala. De esta manera, los agentes pueden presentar comportamientos estratégicos que contribuyen a la maximización de sus utilidades, los cuales se ven reflejados en la oferta diaria del precio y de la cantidad de energía por hora en cada una de sus centrales de generación. En este trabajo se presenta una metodología para la estimación de los precios diarios a los que ofertan la energía que producen los principales recursos hídricos y térmicos en Colombia. Se emplean dos herramientas de Inteligencia Artificial: la Lógica Difusa y las Redes Neuronales. Dichas técnicas resultan ser parcialmente efectivas para seguir las tendencias de dichos precios. También se comparan los resultados con los de modelos autorregresivos, que resultan ser inapropiados para el caso de estudio. || 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.

Suggested Citation

  • Hurtado Moreno, Laura & Quintero Montoya, Olga Lucía & García Rendón, John Jairo, 2014. "Estimación del precio de oferta de la energía eléctrica en Colombia mediante inteligencia artificial || Estimating the Spot Market Price Bid in Colombian Electricity Market by Using Artificial Intelli," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 18(1), pages 54-87, December.
  • Handle: RePEc:pab:rmcpee:v:18:y:2014:i:1:p:54-87
    as

    Download full text from publisher

    File URL: http://www.upo.es/RevMetCuant/pdf/vol18/art96.pdf
    Download Restriction: no

    File URL: http://www.upo.es/RevMetCuant/art.php?id=96
    Download Restriction: no

    References listed on IDEAS

    as
    1. Guthrie, Graeme & Videbeck, Steen, 2007. "Electricity spot price dynamics: Beyond financial models," Energy Policy, Elsevier, vol. 35(11), pages 5614-5621, November.
    2. Green, Richard J, 1996. "Increasing Competition in the British Electricity Spot Market," Journal of Industrial Economics, Wiley Blackwell, vol. 44(2), pages 205-216, June.
    3. Ventosa, Mariano & Baillo, Alvaro & Ramos, Andres & Rivier, Michel, 2005. "Electricity market modeling trends," Energy Policy, Elsevier, vol. 33(7), pages 897-913, May.
    4. von der Fehr, Nils-Henrik Morch & Harbord, David, 1993. "Spot Market Competition in the UK Electricity Industry," Economic Journal, Royal Economic Society, vol. 103(418), pages 531-546, May.
    5. Swinand, Gregory & Scully, Derek & Ffoulkes, Stuart & Kessler, Brian, 2010. "Modeling EU Electricity Market Competition Using the Residual Supply Index," The Electricity Journal, Elsevier, vol. 23(9), pages 41-50, November.
    6. Green, Richard J & Newbery, David M, 1992. "Competition in the British Electricity Spot Market," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 929-953, October.
    7. Weron, Rafal & Misiorek, Adam, 2008. "Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models," International Journal of Forecasting, Elsevier, vol. 24(4), pages 744-763.
    8. Guthrie, Graeme & Videbeck, Steen, 2004. "Electricity Spot Price Dynamics: Beyond Financial Models," Working Paper Series 3866, Victoria University of Wellington, The New Zealand Institute for the Study of Competition and Regulation.
    9. de Frutos, Maria-Angeles & Fabra, Natalia, 2008. "On the Impact of Forward Contract Obligations in Multi-Unit Auctions," CEPR Discussion Papers 6756, C.E.P.R. Discussion Papers.
    10. Weron, R. & Kozłowska, B. & Nowicka-Zagrajek, J., 2001. "Modeling electricity loads in California: a continuous-time approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(1), pages 344-350.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    mercado eléctrico al contado; precio ofertado; Inteligencia Artificial; Lógica Difusa; wholesale energy market; price bid; Artificial Intelligence; Fuzzy Logic;

    JEL classification:

    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pab:rmcpee:v:18:y:2014:i:1:p:54-87. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Publicación Digital - UPO). General contact details of provider: http://edirc.repec.org/data/dmupoes.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.