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

Authors

  • Andres Oviedo-Gomez Engineering Doctorate Student, Universidad del Valle, Cali, Colombia.School of Electrical and Electronic Engineering, Universidad del Valle, Cali, Colombia
  • Sandra Milena Londono-Hernandez 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.

Keywords: electricity prices; hydrothermal power generation markets; machine learning; quantile regression; Gaussian process regression.

JEL Classifications: C22, Q41, Q43, Q47

DOI: https://doi.org/10.32479/ijeep.11346

Downloads

Download data is not yet available.

Downloads

Published

2021-08-20

How to Cite

Oviedo-Gomez, A., Londono-Hernandez, S. M., & Manotas-Duque, D. F. (2021). Electricity Price Fundamentals in Hydrothermal Power Generation Markets Using Machine Learning and Quantile Regression Analysis. International Journal of Energy Economics and Policy, 11(5), 66–77. Retrieved from https://econjournals.com/index.php/ijeep/article/view/11346

Issue

Section

Articles