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A storage degradation model of Li-ion batteries to integrate ageing effects in the optimal management and design of an isolated microgrid

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  • Seger, Pedro V.H.
  • Rigo-Mariani, Rémy
  • Thivel, Pierre-Xavier
  • Riu, Delphine

Abstract

Li-ion batteries are being increasingly used in stationary applications, allowing for greater autonomy and facilitating the integration of renewable energies. However, these devices lose their capacity over time, especially during their so-called second life. This degradation affects the operation of the system and its cost, and must be taken into consideration if an optimal management is to be found. In this work, we present a framework for the integration of the battery aging in a microgrid design and energy management problem. To do so, we first propose a method to simplify a reference model for cyclic degradation of batteries. The results show that the battery loss of capacity shall be compute on a weekly to monthly basis to accurately keep track of degradation effects. Also, at a first order, we show that ageing is dependent with the initial battery State of Health at every update and the energy exchanged normalized by the nominal capacity. This simplified model formulated with Mixed Integer Linear Programming is integrated in a management problem of an isolated energy system for cost minimization. Optimization results show the necessary trade-off between storage degradation and overall system performances. Finally, a tailored dynamic programming is proposed to simulate successive years for the optimal sizing of the considered system in terms of battery capacity and its replacements. Compared to the proposed pproach, not accounting for the storage degradation leads to inaccurate results and significant cost underestimations (>20 %).

Suggested Citation

  • Seger, Pedro V.H. & Rigo-Mariani, Rémy & Thivel, Pierre-Xavier & Riu, Delphine, 2023. "A storage degradation model of Li-ion batteries to integrate ageing effects in the optimal management and design of an isolated microgrid," Applied Energy, Elsevier, vol. 333(C).
    • Handle: RePEc:eee:appene:v:333:y:2023:i:c:s0306261922018414
    DOI: 10.1016/j.apenergy.2022.120584
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    References listed on IDEAS

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    1. Song, Ziyou & Hofmann, Heath & Li, Jianqiu & Han, Xuebing & Ouyang, Minggao, 2015. "Optimization for a hybrid energy storage system in electric vehicles using dynamic programing approach," Applied Energy, Elsevier, vol. 139(C), pages 151-162.
    2. Rigo-Mariani, Rémy & Chea Wae, Sean Ooi & Mazzoni, Stefano & Romagnoli, Alessandro, 2020. "Comparison of optimization frameworks for the design of a multi-energy microgrid," Applied Energy, Elsevier, vol. 257(C).
    3. Radet, Hugo & Roboam, Xavier & Sareni, Bruno & Rigo-Mariani, Rémy, 2021. "Dynamic aware aging design of a simple distributed energy system: A comparative approach with single stage design strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
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    Cited by:

    1. Koláček, Jan & Vališ, David & Fuksová, Mária & Hlinka, Jiří & Procházka, Petr, 2025. "Perspective modelling and measuring discharge voltage on truncated data of long-term stored Li-ion batteries based on functional state space model," Applied Energy, Elsevier, vol. 377(PB).
    2. 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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