The impact of the El Nino phenomenon on electricity prices in hydrologic-based production systems: A switching regime semi-nonparametric approach

Electricity production in highly hydrological-dependent systems is determined by different weather phenomena, which strongly impact spot prices. To account for such stylized facts, we propose a stochastic process with a mean reversion and switching regime component to represent the dynamics of the spot price. The short-term movements are represented by semi-nonparametric (SNP) distributions, in contrast to previous studies that traditionally assume Gaussian processes. We consider the Colombian electricity market as a study case, in which 68% of its electrical generation comes from water resources, and the El Nino phenomenon represents a critical source of risk for maintaining long-term supply, sustainability of investments, and efficiency of prices. We show that under scarcity seasons, the spot price mean, variance, and some superior-order moments of electricity price distribution increase, as does the risk level of the system. In particular, the switching regime model with SNP distributions for the random components outperforms traditional models, leading to accurate estimates and simulations, thus being a helpful tool for resource planning, risk management, and policy-makers for electricity markets with high climatic dependency.

Automated summary

Electricity production in hydrological-dependent systems is influenced by weather phenomena, impacting spot prices. We propose a stochastic process with mean reversion and switching regime component to represent spot price dynamics. Our study on the Colombian electricity market shows that scarcity seasons increase spot price mean, variance, and risk level. The switching regime model with semi-nonparametric distributions outperforms traditional models, making it a valuable tool for resource planning and risk management in electricity markets with high climatic dependency.