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Electricity Price Fundamentals in Hydrothermal Power Generation Markets Using Machine Learning and Quantile Regression Analysis

Author

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  • Andr s Oviedo-G mez

    (School of Electrical and Electronic Engineering, Universidad del Valle, Cali, Colombia,)

  • Sandra Milena Londo o-Hern ndez

    (School of Electrical and Electronic Engineering, Universidad del Valle, Cali, Colombia,)

  • Diego Fernando Manotas-Duque

    (School of Industrial Engineering, Universidad del Valle, Cali, Colombia.)

Abstract

A hydrothermal power generation market is characterized by a strong dependence on water reservoir capacity and fossil fuel sources, which causes differences in generation marginal costs and high variability of the electricity spot price. Therefore, this study proposes an empirical approach to identify the price determinants and their effects on price dynamics. This paper presents two methodologies: a machine learning approach and a quantile regression analysis. The first method is used to validate the price determinants through a prediction process, and the second, the quantile regression, to identify the non-linear effects. The most important factors observed are total market demand, water reservoirs capacity for generation, and fossil fuel consumption. The results offer a new perspective about the market structure and spot price volatility.

Suggested Citation

  • Andr s Oviedo-G mez & Sandra Milena Londo o-Hern ndez & Diego Fernando Manotas-Duque, 2021. "Electricity Price Fundamentals in Hydrothermal Power Generation Markets Using Machine Learning and Quantile Regression Analysis," International Journal of Energy Economics and Policy, Econjournals, vol. 11(5), pages 66-77.
    • Handle: RePEc:eco:journ2:2021-05-10
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    References listed on IDEAS

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    Cited by:

    1. Andr s Oviedo-G mez & Sandra Milena Londo o-Hern ndez & Diego Fernando Manotas-Duque, 2023. "Directional Spillover of Fossil Fuels Prices on a Hydrothermal Power Generation Market," International Journal of Energy Economics and Policy, Econjournals, vol. 13(1), pages 85-90, January.

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    More about this item

    Keywords

    electricity prices; hydrothermal power generation markets; machine learning; quantile regression; Gaussian process regression.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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