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

Luca Di Persio (), Mohammed Alruqimi and Matteo Garbelli
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Luca Di Persio: Department of Computer Science, College of Mathematics, University of Verona, 37129 Verona, Italy
Mohammed Alruqimi: Department of Computer Science, College of Mathematics, University of Verona, 37129 Verona, Italy
Matteo Garbelli: Department of Computer Science, College of Mathematics, University of Verona, 37129 Verona, Italy

Energies, 2024, vol. 17, issue 23, 1-46

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.

Keywords: energy markets; power systems; market clearing; stochastic differential equations; optimization; ML (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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