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Operational Planning Strategies to Mitigate Price Uncertainty in Day-Ahead Market for a Battery Energy System

Author

Listed:
  • 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, 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, 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

    (Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes)

  • Lionel Pin

    (Atos Worldgrid [Grenoble])

Abstract

As renewable energy sources become more prevalent, effective grid balancing becomes crucial due to their inherent uncertainty. Battery Energy Storage Systems (BESS) can enhance grid reliability and efficiency by complementing these variable sources. However, to encourage investments in BESS, market participation must be economically viable for owners. Energy arbitrage is one of the main revenue streams for BESS allowing them to buy electricity when prices are low and sell it when they become higher, thus optimizing the revenues. However, in energy markets such as the Day-Ahead market (DA), the BESS owners submit their bids/offers one day before delivery, without perfect foresight of the future rates. This uncertainty poses a challenge that limits the energy provision capabilities and can incur a loss of profit due to the imperfect price forecast. Tailored strategies are then needed to mitigate those uncertainties and minimize the profit loss. This article proposes different operational planning strategies for a BESS participating in DA. Specific interest is attached to the explainability of the proposed methods to assure high profits while reducing the model's complexity and computational time. The proposed strategies include 1) price forecast and scenario generation, using Geometric Brownian Motion (GBM) based either on a single-point forecast or historical data; 2) optimization process; and 3) choice of a single BESS bidding and operating schedule that is ultimately applied in real-time. Two baselines are introduced, one relying on a back-casting method, and a second based on traditional stochastic optimization. Several studies have neglected to thoroughly assess the bidding strategies by evaluating the profit against the actual prices. Hence, this study assesses the performance of the proposed methods and the baselines relative to the profit obtained in an ideal scenario with a perfect forecast in the French market over 2021

Suggested Citation

  • Ahmed Mohamed & Rémy Rigo-Mariani & Vincent Debusschere & Lionel Pin, 2024. "Operational Planning Strategies to Mitigate Price Uncertainty in Day-Ahead Market for a Battery Energy System," Post-Print hal-04628352, HAL.
    • Handle: RePEc:hal:journl:hal-04628352
    DOI: 10.1109/access.2024.3415811
    Note: View the original document on HAL open archive server: https://hal.science/hal-04628352v1
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    References listed on IDEAS

    as
    1. Nojavan, Sayyad & Najafi-Ghalelou, Afshin & Majidi, Majid & Zare, Kazem, 2018. "Optimal bidding and offering strategies of merchant compressed air energy storage in deregulated electricity market using robust optimization approach," Energy, Elsevier, vol. 142(C), pages 250-257.
    2. Mercier, Thomas & Olivier, Mathieu & De Jaeger, Emmanuel, 2023. "The value of electricity storage arbitrage on day-ahead markets across Europe," Energy Economics, Elsevier, vol. 123(C).
    3. Mohamed, Ahmed & Rigo-Mariani, Rémy & Debusschere, Vincent & Pin, Lionel, 2023. "Stacked revenues for energy storage participating in energy and reserve markets with an optimal frequency regulation modeling," Applied Energy, Elsevier, vol. 350(C).
    4. Ahmed Mohamed & Rémy Rigo-Mariani & Vincent Debusschere & Lionel Pin, 2023. "Stacked Revenues for Energy Storage Participating in Energy and Reserve Markets with an Optimal Frequency Regulation Modeling," Post-Print hal-04182119, HAL.
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    Cited by:

    1. Veenstra, Arjen T. & Mulder, Machiel, 2025. "Profitability of batteries in day-ahead and intraday electricity markets: Assessment of operation strategies with endogenous prices," Energy Economics, Elsevier, vol. 148(C).
    2. Ahmed Mohamed & Rémy Rigo-Mariani & Vincent Debusschere, 2025. "Impact of modeling assumptions on the economic performance assessment of a storage participating in energy and reserve markets," Post-Print hal-05212162, HAL.

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