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Stochastic Approaches to Energy Markets: From Stochastic Differential Equations to Mean Field Games and Neural Network Modeling

Author

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  • Luca Di Persio

    (Department of Computer Science, College of Mathematics, University of Verona, 37129 Verona, Italy
    These authors contributed equally to this work.)

  • Mohammed Alruqimi

    (Department of Computer Science, College of Mathematics, University of Verona, 37129 Verona, Italy
    These authors contributed equally to this work.)

  • Matteo Garbelli

    (Department of Computer Science, College of Mathematics, University of Verona, 37129 Verona, Italy
    These authors contributed equally to this work.)

Abstract

This review paper examines the current landscape of electricity market modelling, specifically focusing on stochastic approaches, transitioning from Mean Field Games (MFGs) to Neural Network (NN) modelling. The central objective is to scrutinize and synthesize evolving modelling strategies within power systems, facilitating technological advancements in the contemporary electricity market. This paper emphasizes the assessment of model efficacy, particularly in the context of MFG and NN applications. Our findings shed light on the diversity of models, offering practical insights into their strengths and limitations, thereby providing a valuable resource for researchers, policy makers, and industry practitioners. The review guides navigating and leveraging the latest stochastic modelling techniques for enhanced decision making and improved market operations.

Suggested Citation

  • Luca Di Persio & Mohammed Alruqimi & Matteo Garbelli, 2024. "Stochastic Approaches to Energy Markets: From Stochastic Differential Equations to Mean Field Games and Neural Network Modeling," Energies, MDPI, vol. 17(23), pages 1-46, December.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:6106-:d:1536590
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    References listed on IDEAS

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