Electricity price spike clustering: A zero-inflated GARX approach

Abstract

Sudden dramatic rises in electricity prices, known as electricity “price spikes”, are ubiquitous in electricity spot markets worldwide. These price spikes often cluster. Energy retailers in many countries, including Australia, purchase electricity in an unregulated spot market and sell it to consumers at a heavily regulated price. As a result, the occurrence and clustering of spikes in the spot electricity price is particularly hazardous for retailers, and effective modelling and forecasting of these spikes is of foremost importance for effective risk management. This paper uses a data-dependent two-regime threshold autoregression to determine the electricity price spikes and proposes a zero-inflated generalized autoregression with exogenous covariates (ZIGARX) for modelling the time series count of price spikes. This model is shown to well capture the salient features of electricity price spikes, and can provide effective rolling probability forecasts of price spike occurrence. We apply the proposed approach to analyze the spot prices in four major regions in the Australian National Electricity Market (NEM) over the period April 2016 to February 2022. Results show that price spike clustering is captured very well using our model, and the persistence in price spike occurrence remains even after controlling for the relevant exogenous covariates. We also find that, in most of our sample period, the increasing use of renewable generation has mixed effects on the occurrence of price spikes.