Scandinavian Working Papers in Business Administration
Discussion Papers,
Norwegian School of Economics, Department of Business and Management Science
No 2026/11: The influence of gas and renewable energy sources on the tail of the electricity price distribution
Samaneh Sheybanivaziri () and Evangelos Kyritsis ()
Additional contact information
Samaneh Sheybanivaziri: Dept. of Business and Management Science, Norwegian School of Economics, Postal: NHH , Department of Business and Management Science, Helleveien 30, N-5045 Bergen, Norway
Evangelos Kyritsis: Dept. of Business and Management Science, Norwegian School of Economics, Postal: NHH , Department of Business and Management Science, Helleveien 30, N-5045 Bergen, Norway
Abstract: Europe’s move toward renewable energy has sped up as the need to cut emissions has become closely linked with the need to secure energy supply. The EU’s Fit for 55 package proposed a 40% renewable energy target for 2030 [16]. The REPowerEU plan later raised the ambition to 45% in May 2022 in response to the need to reduce dependence on Russian fossil fuels [17]. Besides the significant contribution of renewable energies to the generation mix, they have created a dichotomy in electricity prices. An abundance of renewables can create extremely low prices and high volatility. On the other hand, their absence or insufficiency turns the gas-fired units or some fossil fuels on, which can create extremely high power prices. This phenomenon, in combination with geopolitical factors, has generated a binary fat-tailed distribution in electricity prices, which motivated us to study it more closely. Therefore, in this paper, we analyse the determinants of extreme electricity prices by modelling the conditional tail index. Additionally, we extend the analysis by [18] to a more recent sample period. We show that observable market conditions, such as TTF prices, load, and renewable generation in Germany and Italy from 2018 to 2023, affect the heaviness of the price distribution tail, a dimension of risk that is not captured by standard approaches such as quantile regression.
Keywords: Extreme electricity prices; conditional tail-index estimator
Language: English
41 pages, September 21, 2026
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