Electricity GANs: Generative Adversarial Networks for Electricity Price Scenario Generation

Bilgi Yilmaz (), Christian Laudagé (), Ralf Korn () and Sascha Desmettre ()
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Bilgi Yilmaz: Department of Mathematics, Rheinland-Pfälzische Technische Universität (RPTU), 67663 Kaiserslautern, Germany
Christian Laudagé: Department of Mathematics, Rheinland-Pfälzische Technische Universität (RPTU), 67663 Kaiserslautern, Germany
Ralf Korn: Department of Mathematics, Rheinland-Pfälzische Technische Universität (RPTU), 67663 Kaiserslautern, Germany
Sascha Desmettre: Institute of Financial Mathematics and Applied Number Theory, Johannes Kepler University (JKU) Linz, 4040 Linz, Austria

Commodities, 2024, vol. 3, issue 3, 1-27

Abstract: The dynamic structure of electricity markets, where uncertainties abound due to, e.g., demand variations and renewable energy intermittency, poses challenges for market participants. We propose generative adversarial networks (GANs) to generate synthetic electricity price data. This approach aims to provide comprehensive data that accurately reflect the complexities of the actual electricity market by capturing its distribution. Consequently, we would like to equip market participants with a versatile tool for successfully dealing with strategy testing, risk model validation, and decision-making enhancement. Access to high-quality synthetic electricity price data is instrumental in cultivating a resilient and adaptive marketplace, ultimately contributing to a more knowledgeable and prepared electricity market community. In order to assess the performance of various types of GANs, we performed a numerical study on Turkey’s intraday electricity market weighted average price (IDM-WAP). As a key finding, we show that GANs can effectively generate realistic synthetic electricity prices. Furthermore, we reveal that the use of complex variants of GAN algorithms does not lead to a significant improvement in synthetic data quality. However, it requires a notable increase in computational costs.

Keywords: generative adversarial networks; complex GAN variants; deep learning in energy markets; synthetic data generation; intraday electricity prices (search for similar items in EconPapers)
JEL-codes: C0 C1 C2 C3 C4 C5 C6 C7 C8 C9 D4 E3 E6 F0 F1 F3 F4 F5 F6 G1 O1 O5 Q1 Q2 Q4 (search for similar items in EconPapers)
Date: 2024
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