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Ageing and Efficiency Aware Battery Dispatch for Arbitrage Markets Using Mixed Integer Linear Programming †

Author

Listed:
  • Holger C. Hesse

    (Department of Electrical and Computer Engineering, Technical University of Munich (TUM), 80333 Munich, Germany
    These authors contributed equally to this work.)

  • Volkan Kumtepeli

    (Energy Research Institute @ NTU, Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore 637371, Singapore
    These authors contributed equally to this work.)

  • Michael Schimpe

    (Department of Electrical and Computer Engineering, Technical University of Munich (TUM), 80333 Munich, Germany)

  • Jorn Reniers

    (Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
    Energy Technology Unit, Vlaamse Instelling voor Technologisch Onderzoek (VITO), Boeretang 200, 2400 Mol, Belgium)

  • David A. Howey

    (Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK)

  • Anshuman Tripathi

    (Energy Research Institute @ NTU, Nanyang Technological University, Singapore 637141, Singapore)

  • Youyi Wang

    (School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore)

  • Andreas Jossen

    (Department of Electrical and Computer Engineering, Technical University of Munich (TUM), 80333 Munich, Germany)

Abstract

To achieve maximum profit by dispatching a battery storage system in an arbitrage operation, multiple factors must be considered. While revenue from the application is determined by the time variability of the electricity cost, the profit will be lowered by costs resulting from energy efficiency losses, as well as by battery degradation. In this paper, an optimal dispatch strategy is proposed for storage systems trading on energy arbitrage markets. The dispatch is based on a computationally-efficient implementation of a mixed-integer linear programming method, with a cost function that includes variable-energy conversion losses and a cycle-induced battery capacity fade. The parametrisation of these non-linear functions is backed by in-house laboratory tests. A detailed analysis of the proposed methods is given through case studies of different cost-inclusion scenarios, as well as battery investment-cost scenarios. An evaluation with a sample intraday market data set, collected throughout 2017 in Germany, offers a potential monthly revenue of up to 8762 EUR/MWh cap installed capacity, without accounting for the costs attributed to energy losses and battery degradation. While this is slightly above the revenue attainable in a reference application—namely, primary frequency regulation for the same sample month (7716 EUR/MWh cap installed capacity)—the situation changes if costs are considered: The optimisation reveals that losses in battery ageing and efficiency reduce the attainable profit by up to 36% for the most profitable arbitrage use case considered herein. The findings underline the significance of considering both ageing and efficiency in battery system dispatch optimisation.

Suggested Citation

  • Holger C. Hesse & Volkan Kumtepeli & Michael Schimpe & Jorn Reniers & David A. Howey & Anshuman Tripathi & Youyi Wang & Andreas Jossen, 2019. "Ageing and Efficiency Aware Battery Dispatch for Arbitrage Markets Using Mixed Integer Linear Programming †," Energies, MDPI, vol. 12(6), pages 1-28, March.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:6:p:999-:d:213970
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    References listed on IDEAS

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    6. Kanbur, Baris Burak & Kumtepeli, Volkan & Duan, Fei, 2020. "Thermal performance prediction of the battery surface via dynamic mode decomposition," Energy, Elsevier, vol. 201(C).
    7. Collath, Nils & Cornejo, Martin & Engwerth, Veronika & Hesse, Holger & Jossen, Andreas, 2023. "Increasing the lifetime profitability of battery energy storage systems through aging aware operation," Applied Energy, Elsevier, vol. 348(C).
    8. Harun Or Rashid Howlader & Oludamilare Bode Adewuyi & Ying-Yi Hong & Paras Mandal & Ashraf Mohamed Hemeida & Tomonobu Senjyu, 2019. "Energy Storage System Analysis Review for Optimal Unit Commitment," Energies, MDPI, vol. 13(1), pages 1-21, December.
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