Supervised Learning for the Bidding of Grid-Connected Batteries in the Day-Ahead Market

Fabien Sanchez, Ahmed Mohamed, Rémy Rigo-Mariani () and Vincent Debusschere ()
Additional contact information
Fabien Sanchez: G2Elab-SYREL - G2Elab-SYstèmes et Réseaux ELectriques - G2ELab - Laboratoire de Génie Electrique de Grenoble - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes
Ahmed Mohamed: G2Elab-SYREL - G2Elab-SYstèmes et Réseaux ELectriques - G2ELab - Laboratoire de Génie Electrique de Grenoble - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes
Rémy Rigo-Mariani: G2Elab-SYREL - G2Elab-SYstèmes et Réseaux ELectriques - G2ELab - Laboratoire de Génie Electrique de Grenoble - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes
Vincent Debusschere: G2Elab-SYREL - G2Elab-SYstèmes et Réseaux ELectriques - G2ELab - Laboratoire de Génie Electrique de Grenoble - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes

Post-Print from HAL

Abstract: This paper discusses the implementation of supervised learning (SL) as a straightforward data-driven technique to compute the day-ahead bids of grid-connected battery energy systems (BESS) participating in energy markets. The objective is to implicitly account for price uncertainty in the BESS schedule before assessing the economic performance. The case study is a 10 MW BESS battery participating in the day-ahead market. Physic-Informed and more traditional loss functions and a large set of tuning parameters are compared based on the generated daily revenues. Either the power injected by the BESS or its state of charge is controlled, illustrating a compromise to find between the accuracy and the resilience of the results, once confronted with the high volatility of energy prices. The performance of AI-based controllers is assessed in terms of precision with a theoretical optimum obtained with a "perfect forecast". A reference bidding strategy using "backcasting" as a forecast is also considered. Simulation over the year 2021 with an hourly training data set of the energy prices of 2020 shows that SL models do not necessarily perform better than reference results (with a minimal error of 58 %). However, discussions about their tuning and design choices shed light on the complex implementation process of the selected case study.

Keywords: Machine Leaning; Energy Market; Storage (search for similar items in EconPapers)
Date: 2025-06-29
New Economics Papers: this item is included in nep-ene and nep-for
Note: View the original document on HAL open archive server: https://hal.science/hal-05310545v1
References: Add references at CitEc
Citations:

Published in 2025 IEEE Kiel PowerTech, Jun 2025, Kiel, Germany. pp.1-7, ⟨10.1109/PowerTech59965.2025.11180640⟩

Downloads: (external link)
https://hal.science/hal-05310545v1/document (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05310545

DOI: 10.1109/PowerTech59965.2025.11180640

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().