Prediction of extreme price occurrences in the German day-ahead electricity market. Issue 12 (1st December 2016)

• Record Type: Journal Article
• Authors: Hagfors, Lars Ivar; Kamperud, Hilde Hørthe; Paraschiv, Florentina; Prokopczuk, Marcel; Sator, Alma; Westgaard, Sjur
• Is Part Of: Quantitative finance. Volume 16:Issue 12(2016)
• Page Start: 1929
• Page End: 1948
• Publication Date: 2016-12-01
• Subjects: Energy markets -- Fundamental analysis -- Spikes -- EPEX; Finance -- Periodicals; Business mathematics -- Periodicals; Finance -- Mathematical models -- Periodicals; Investments -- Mathematics -- Periodicals; Economics -- Periodicals; Finances -- Modèles mathématiques -- Périodiques; 332.015118
• DOI: 10.1080/14697688.2016.1211794
• Languages: English
• ISSNs: 1469-7688

Abstract

Abstract : Understanding the mechanisms that drive extreme negative and positive prices in day-ahead electricity prices is crucial for managing risk and market design. In this paper, we consider the problem of understanding how fundamental drivers impact the probability of extreme price occurrences in the German day-ahead electricity market. We develop models using fundamental variables to predict the probability of extreme prices. The dynamics of negative prices and positive price spikes differ greatly. Positive spikes are related to high demand, low supply and high prices the previous days, and mainly occur during the morning and afternoon peak hours. Negative prices occur mainly during the night and are closely related to low demand combined with high wind production levels. Furthermore, we do a closer analysis of how renewable energy sources, hereby photovoltaic and wind power, impact the probability of negative prices and positive spikes. The models confirm that extremely high and negative prices have different drivers, and that wind power is particularly important in relation to negative price occurrences. The models capture the main drivers of both positive and negative extreme price occurrences and perform well with respect to accurately forecasting the probability with high levels of confidence. Our results suggest that probability models are well suited to aid in risk management for market participants in day-ahead electricity markets.