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
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Energy Economics and Policy
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
