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Electricity GANs: Generative Adversarial Networks for Electricity Price Scenario Generation

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  • Bilgi Yilmaz

    (Department of Mathematics, Rheinland-Pfälzische Technische Universität (RPTU), 67663 Kaiserslautern, Germany
    Faculty of Science, Mathematics Department, Kahramanmaras Sutcu Imam University, 46050 Kahramanmaras, Turkey)

  • 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)

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.

Suggested Citation

  • Bilgi Yilmaz & Christian Laudagé & Ralf Korn & Sascha Desmettre, 2024. "Electricity GANs: Generative Adversarial Networks for Electricity Price Scenario Generation," Commodities, MDPI, vol. 3(3), pages 1-27, July.
    • Handle: RePEc:gam:jcommo:v:3:y:2024:i:3:p:16-280:d:1430997
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    References listed on IDEAS

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