Forecasting price spikes in day-ahead electricity markets: techniques, challenges, and the road ahead

Samaneh Sheybanivaziri (samaneh.sheybanivaziri@nhh.no), Jérôme Le Dréau (jledreau@univ-lr.fr) and Hussain Kazmi (hussain.kazmi@kuleuven.be)
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, https://www.nhh.no/en/employees/faculty/samaneh-sheybanivaziri/
Jérôme Le Dréau: Laboratoire des Sciences de l’Ingénieur pour l’Environnement (LaSIE), La Rochelle University, Postal: La Rochelle University, LaSIE UMR CNRS 7356, Pôle Sciences et Technologie, Avenue Michel Crépeau, 17042 La Rochelle Cedex 1, France, https://lasie.univ-larochelle.fr/LE-DREAU-Jerome
Hussain Kazmi: Dept. of Electrical Engineering, KU Leuven, Postal: KU Leuven , Electrical Energy Systems and Applications (ELECTA), Kasteelpark Arenberg 10 - box 2445, 3001 Leuven, Belgium, https://www.kuleuven.be/wieiswie/en/person/00107015

No 2024/1, Discussion Papers from Norwegian School of Economics, Department of Business and Management Science

Abstract: Due to the increase in renewable energy production and global socioeconomic turmoil, the volatility in electricity prices has considerably increased in recent years, leading to extreme positive and negative price spikes in many electricity markets. Forecasting (the risk of) these prices accurately in advance can enable risk-informed decision-making by both consumers and generators, as well as by the grid operators. In this work, focusing on day-ahead markets, we review recent developments in how price spikes are defined, as well as which explanatory factors and methodologies have been used to forecast them. The paper identifies seven categories of influencing factors, which come with over 30 sub-classifications that can cause price spikes. In terms of methodologies, probabilistic models are being increasingly utilized to capture uncertainty in the price forecast. The review uncovers a wide range in all of these choices as well as others, which makes it difficult to compare methods and select best practices for predicting price spikes.

Keywords: Spikes; Electricity markets; Day-ahead market; Point forecast; Probabilistic forecasts (search for similar items in EconPapers)
JEL-codes: C00 C10 C53 (search for similar items in EconPapers)
Pages: 33 pages
Date: 2024-01-17
New Economics Papers: this item is included in nep-ene and nep-for
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