IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v399y2025ics030626192501222x.html

Modeling battery aging in linear energy system optimizations by applying convex hulls - accuracy benefits and computational costs

Author

Listed:
  • Petrin, Geert Kristian
  • Arens, Stefan
  • Schoenfeldt, Patrik
  • Klement, Peter
  • Schlüters, Sunke

Abstract

Linear energy system optimization plays an important role in the design of future energy systems, due to its availability and low computational requirements. While linear optimization offers many benefits, the rise in popularity of battery storage and its use cases represents a challenge, commonly causing oversimplification by ignoring aging and its causes. Thus, this paper aims to include battery aging and subsequently battery optimized operation, to lower aging, into linear system optimization. Two approaches are highlighted, namely a basic linear aging implementation, as well as a convex hull approximation. These are then compared against the generic implementation found in oemof.solph, an open source energy modeling tool, to assess the benefits and drawbacks of including aging. The chosen case study for evaluation models a buffer storage for an overhead line island to reduce peak loads, enabling connections to weaker grids. The results show up to 46.9 % lower battery aging for the convex approach, compared to the generic baseline. Furthermore, the amount of aging is precisely determined by the convex approach, with errors below 0.4 % and the computational times close to the same magnitude, due to the lack of mixed integer linear programming. The suggested approaches of integrating battery aging into linear optimization should be integrated into future models to better predict and reduce battery lifetimes, allowing for better cost calculations in such systems.

Suggested Citation

  • Petrin, Geert Kristian & Arens, Stefan & Schoenfeldt, Patrik & Klement, Peter & Schlüters, Sunke, 2025. "Modeling battery aging in linear energy system optimizations by applying convex hulls - accuracy benefits and computational costs," Applied Energy, Elsevier, vol. 399(C).
  • Handle: RePEc:eee:appene:v:399:y:2025:i:c:s030626192501222x
    DOI: 10.1016/j.apenergy.2025.126492
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030626192501222X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2025.126492?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Yang, Yuqing & Bremner, Stephen & Menictas, Chris & Kay, Merlinde, 2022. "Modelling and optimal energy management for battery energy storage systems in renewable energy systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    2. Tomasz Jałowiec & Henryk Wojtaszek & Ireneusz Miciuła, 2022. "Analysis of the Potential Management of the Low-Carbon Energy Transformation by 2050," Energies, MDPI, vol. 15(7), pages 1-29, March.
    3. Christoph Streuling & Johannes Pagenkopf & Moritz Schenker & Kim Lakeit, 2021. "Techno-Economic Assessment of Battery Electric Trains and Recharging Infrastructure Alternatives Integrating Adjacent Renewable Energy Sources," Sustainability, MDPI, vol. 13(15), pages 1-30, July.
    4. Katarzyna Chudy-Laskowska & Tomasz Pisula, 2022. "An Analysis of the Use of Energy from Conventional Fossil Fuels and Green Renewable Energy in the Context of the European Union’s Planned Energy Transformation," Energies, MDPI, vol. 15(19), pages 1-23, October.
    5. Klemm, Christian & Vennemann, Peter, 2021. "Modeling and optimization of multi-energy systems in mixed-use districts: A review of existing methods and approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    6. Ayuso, Pablo & Beltran, Hector & Segarra-Tamarit, Jorge & Pérez, Emilio, 2021. "Optimized profitability of LFP and NMC Li-ion batteries in residential PV applications," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 183(C), pages 97-115.
    7. Petit, Martin & Prada, Eric & Sauvant-Moynot, Valérie, 2016. "Development of an empirical aging model for Li-ion batteries and application to assess the impact of Vehicle-to-Grid strategies on battery lifetime," Applied Energy, Elsevier, vol. 172(C), pages 398-407.
    8. Maheshwari, Arpit & Paterakis, Nikolaos G. & Santarelli, Massimo & Gibescu, Madeleine, 2020. "Optimizing the operation of energy storage using a non-linear lithium-ion battery degradation model," Applied Energy, Elsevier, vol. 261(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhou, Yuekuan, 2024. "AI-driven battery ageing prediction with distributed renewable community and E-mobility energy sharing," Renewable Energy, Elsevier, vol. 225(C).
    2. Andre Leippi & Markus Fleschutz & Michael D. Murphy, 2022. "A Review of EV Battery Utilization in Demand Response Considering Battery Degradation in Non-Residential Vehicle-to-Grid Scenarios," Energies, MDPI, vol. 15(9), pages 1-22, April.
    3. Joanna Alicja Dyczkowska & Aleksandra Panek & Norbert Chamier-Gliszczynski, 2025. "Identification of Energy Storage in Distribution Channels," Energies, MDPI, vol. 18(18), pages 1-23, September.
    4. Ahmed Gailani & Maher Al-Greer & Michael Short & Tracey Crosbie & Nashwan Dawood, 2020. "Lifetime Degradation Cost Analysis for Li-Ion Batteries in Capacity Markets using Accurate Physics-Based Models," Energies, MDPI, vol. 13(11), pages 1-21, June.
    5. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    6. Gils, Hans Christian & Gardian, Hedda & Kittel, Martin & Schill, Wolf-Peter & Zerrahn, Alexander & Murmann, Alexander & Launer, Jann & Fehler, Alexander & Gaumnitz, Felix & van Ouwerkerk, Jonas & Bußa, 2022. "Modeling flexibility in energy systems — comparison of power sector models based on simplified test cases," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    7. Shen, Feifei & Zhao, Liang & Wang, Meihong & Du, Wenli & Qian, Feng, 2022. "Data-driven adaptive robust optimization for energy systems in ethylene plant under demand uncertainty," Applied Energy, Elsevier, vol. 307(C).
    8. Hossein Yousefi & Mohammad Hasan Ghodusinejad & Armin Ghodrati, 2022. "Multi-Criteria Future Energy System Planning and Analysis for Hot Arid Areas of Iran," Energies, MDPI, vol. 15(24), pages 1-25, December.
    9. Alessandro Giuliano & Yuandi Wu & John Yawney & Stephen Andrew Gadsden, 2025. "Transformer-Based Transfer Learning for Battery State-of-Health Estimation," Energies, MDPI, vol. 18(20), pages 1-21, October.
    10. Vasallo, Manuel Jesús & Cojocaru, Emilian Gelu & Gegúndez, Manuel Emilio & Marín, Diego, 2021. "Application of data-based solar field models to optimal generation scheduling in concentrating solar power plants," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 1130-1149.
    11. Lin, Mingqiang & Wu, Denggao & Meng, Jinhao & Wang, Wei & Wu, Ji, 2023. "Health prognosis for lithium-ion battery with multi-feature optimization," Energy, Elsevier, vol. 264(C).
    12. Ma, Jian & Xu, Shu & Shang, Pengchao & ding, Yu & Qin, Weili & Cheng, Yujie & Lu, Chen & Su, Yuzhuan & Chong, Jin & Jin, Haizu & Lin, Yongshou, 2020. "Cycle life test optimization for different Li-ion power battery formulations using a hybrid remaining-useful-life prediction method," Applied Energy, Elsevier, vol. 262(C).
    13. He, Yongda & Du, Anna Min & Lin, Boqiang & Scrimgeour, Frank, 2025. "Energy-capital substitution, technological innovation, and monetary policy," Research in International Business and Finance, Elsevier, vol. 79(C).
    14. Li, Yan & Feng, Tian-tian & Liu, Li-li & Zhang, Meng-xi, 2023. "How do the electricity market and carbon market interact and achieve integrated development?--A bibliometric-based review," Energy, Elsevier, vol. 265(C).
    15. Emil Petkovski & Iacopo Marri & Loredana Cristaldi & Marco Faifer, 2023. "State of Health Estimation Procedure for Lithium-Ion Batteries Using Partial Discharge Data and Support Vector Regression," Energies, MDPI, vol. 17(1), pages 1-14, December.
    16. Axel Bruck & Luca Casamassima & Ardak Akhatova & Lukas Kranzl & Kostas Galanakis, 2022. "Creating Comparability among European Neighbourhoods to Enable the Transition of District Energy Infrastructures towards Positive Energy Districts," Energies, MDPI, vol. 15(13), pages 1-21, June.
    17. Kun Wang & Lefeng Cheng & Meng Yin & Kuozhen Zhang & Ruikun Wang & Mengya Zhang & Runbao Sun, 2025. "Evolutionary Game Theory in Energy Storage Systems: A Systematic Review of Collaborative Decision-Making, Operational Strategies, and Coordination Mechanisms for Renewable Energy Integration," Sustainability, MDPI, vol. 17(16), pages 1-153, August.
    18. Zhao, Fei & Li, Yalou & Zhou, Xiaoxin & Wang, Dandan & Wei, Yawei & Li, Fang, 2023. "Co-optimization of decarbonized operation of coal-fired power plants and seasonal storage based on green ammonia co-firing," Applied Energy, Elsevier, vol. 341(C).
    19. Tao, Laifa & Cheng, Yujie & Lu, Chen & Su, Yuzhuan & Chong, Jin & Jin, Haizu & Lin, Yongshou & Noktehdan, Azadeh, 2017. "Lithium-ion battery capacity fading dynamics modelling for formulation optimization: A stochastic approach to accelerate the design process," Applied Energy, Elsevier, vol. 202(C), pages 138-152.
    20. Hermann, Julian & Kachirayil, Febin & Lohrmann, Alena & Scheller, Fabian & Roskosch, Dennis & McKenna, Russell, 2025. "A critical reflection on modelling approaches for heat pumps and building envelope retrofits in local energy system optimisations," Applied Energy, Elsevier, vol. 400(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:399:y:2025:i:c:s030626192501222x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.