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.Keywords: electricity prices; hydrothermal power generation markets; machine learning; quantile regression; Gaussian process regression.JEL Classifications: C22, Q41, Q43, Q47DOI: https://doi.org/10.32479/ijeep.11346
Highlights
The different reforms in electricity markets defined electricity as a commodity, which can be sold, bought, and traded in a market (Berrie and Hoyle, 1985)
Based on a particular case of a hydrothermal power generation market which presents: (i) significant differences in the marginal costs of the generation sector; (ii) a small renewable generation capacity; (iii) a strong dependence on exogenous variables as fossil fuel prices and climatology factors; and, where (iv) the risk and uncertainty are higher for market agents, it has been observed that these features cause further increased in price variability (Mosquera-López et al, 2017a; Fernández-Blanco et al, 2017; Cotia et al, 2019)
A machine learning approach was used, through a gaussian process regression (GPR), to fit a multivariable model to predict daily electricity price and validate the importance of variables considered; second, a quantile regression model was fitted to evaluate the effects of these predictors on the electricity price dynamic
Summary
The different reforms in electricity markets defined electricity as a commodity, which can be sold, bought, and traded in a market (Berrie and Hoyle, 1985). Analyzing and predicting the spot prices is a challenge for academics and market agents. The market structure and generation technologies are fundamentals factors in the price formation. Based on a particular case of a hydrothermal power generation market which presents: (i) significant differences in the marginal costs of the generation sector; (ii) a small renewable generation capacity; (iii) a strong dependence on exogenous variables as fossil fuel prices and climatology factors; and, where (iv) the risk and uncertainty are higher for market agents, it has been observed that these features cause further increased in price variability (Mosquera-López et al, 2017a; Fernández-Blanco et al, 2017; Cotia et al, 2019). It is relevant to recognize the determinants that explain the electricity price behavior in this market structure
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