IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2606.22185.html

Impact of distribution fees on BESS scheduling and profitability

Author

Listed:
  • Katarzyna Maciejowska

Abstract

Battery energy storage systems (BESS) are expected to play an important role in electricity markets with increasing shares of renewable generation. While existing research has primarily focused on price arbitrage and ancillary services, the role of grid fees in shaping BESS operation and profitability remains insufficiently understood. This article investigates how different levels of distribution fees affect the scheduling and economic viability of BESS in the day-ahead electricity market. The analysis employs a mixed-integer linear programming model of BESS operation combined with electricity price data from the German market. Four system configurations are considered: stand-alone storage and BESS combined with consumption, generation, or both. The value of storage is measured as the difference between system profits with and without BESS. In addition, a rolling-horizon optimization framework is used to evaluate the impact of forecast uncertainty and decision horizon length on operational outcomes. The results show that grid fees significantly influence both BESS profitability and operational strategies. For stand-alone storage, higher transmission charges reduce arbitrage revenues and battery utilization. When BESS is integrated with consumption and generation units, load shifting and self-consumption become the dominant sources of value, leading to a non-monotonic relationship between grid fees and storage profitability. These findings highlight the importance of considering tariff structures when evaluating storage investments and designing regulatory frameworks for electricity markets with increasing flexibility needs.

Suggested Citation

  • Katarzyna Maciejowska, 2026. "Impact of distribution fees on BESS scheduling and profitability," Papers 2606.22185, arXiv.org.
  • Handle: RePEc:arx:papers:2606.22185
    as

    Download full text from publisher

    File URL: https://arxiv.org/pdf/2606.22185
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Beranek, Julius & Niesler, Nicole & Jochem, Patrick & Ardone, Armin & Fichtner, Wolf, 2026. "Flexibility at a cost: Industrial battery storage and the breakdown of grid fee fairness in Germany," Energy Policy, Elsevier, vol. 212(C).
    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. Selim, Alaa & Mo, Huadong & Pota, Hemanshu & Dong, Daoyi, 2026. "Adaptive optimization of BESS and grid set points: A model-free framework for energy management under dynamic tariff pricing," Energy, Elsevier, vol. 346(C).
    4. Yan, Xiaohe & Gu, Chenghong & Li, Furong & Xiang, Yue, 2018. "Network pricing for customer-operated energy storage in distribution networks," Applied Energy, Elsevier, vol. 212(C), pages 283-292.
    5. Moreno, Rodrigo & Moreira, Roberto & Strbac, Goran, 2015. "A MILP model for optimising multi-service portfolios of distributed energy storage," Applied Energy, Elsevier, vol. 137(C), pages 554-566.
    6. Adebayo, Abiola I. & Zamani-Dehkordi, Payam & Zareipour, Hamidreza & Knight, Andrew M., 2018. "Impacts of transmission tariff on price arbitrage operation of energy storage system in Alberta electricity market," Utilities Policy, Elsevier, vol. 52(C), pages 1-12.
    7. Loukatou, Angeliki & Johnson, Paul & Howell, Sydney & Duck, Peter, 2021. "Optimal valuation of wind energy projects co-located with battery storage," Applied Energy, Elsevier, vol. 283(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. 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).
    2. Pedro Crespo Del Granado & Stein Wallace & Zhan Pang, 2016. "The impact of wind uncertainty on the strategic valuation of distributed electricity storage," Computational Management Science, Springer, vol. 13(1), pages 5-27, January.
    3. Harri Aaltonen & Seppo Sierla & Rakshith Subramanya & Valeriy Vyatkin, 2021. "A Simulation Environment for Training a Reinforcement Learning Agent Trading a Battery Storage," Energies, MDPI, vol. 14(17), pages 1-20, September.
    4. Tom Brijs & Daniel Huppmann & Sauleh Siddiqui & Ronnie Belmans, 2016. "Auction-Based Allocation of Shared Electricity Storage Resources through Physical Storage Rights," Discussion Papers of DIW Berlin 1566, DIW Berlin, German Institute for Economic Research.
    5. Chen, Yang & Odukomaiya, Adewale & Kassaee, Saiid & O’Connor, Patrick & Momen, Ayyoub M. & Liu, Xiaobing & Smith, Brennan T., 2019. "Preliminary analysis of market potential for a hydropneumatic ground-level integrated diverse energy storage system," Applied Energy, Elsevier, vol. 242(C), pages 1237-1247.
    6. Hartmann, Bálint & Divényi, Dániel & Vokony, István, 2018. "Evaluation of business possibilities of energy storage at commercial and industrial consumers – A case study," Applied Energy, Elsevier, vol. 222(C), pages 59-66.
    7. Upadhyay, Subho & Sharma, M.P., 2016. "Selection of a suitable energy management strategy for a hybrid energy system in a remote rural area of India," Energy, Elsevier, vol. 94(C), pages 352-366.
    8. Tao Xu & He Meng & Jie Zhu & Wei Wei & He Zhao & Han Yang & Zijin Li & Yuhan Wu, 2021. "Optimal Capacity Allocation of Energy Storage in Distribution Networks Considering Active/Reactive Coordination," Energies, MDPI, vol. 14(6), pages 1-24, March.
    9. Pusceddu, Elian & Zakeri, Behnam & Castagneto Gissey, Giorgio, 2021. "Synergies between energy arbitrage and fast frequency response for battery energy storage systems," Applied Energy, Elsevier, vol. 283(C).
    10. Matteo Spiller & Giuliano Rancilio & Filippo Bovera & Giacomo Gorni & Stefano Mandelli & Federico Bresciani & Marco Merlo, 2023. "A Model-Aware Comprehensive Tool for Battery Energy Storage System Sizing," Energies, MDPI, vol. 16(18), pages 1-24, September.
    11. Spyros Giannelos & Predrag Djapic & Danny Pudjianto & Goran Strbac, 2020. "Quantification of the Energy Storage Contribution to Security of Supply through the F-Factor Methodology," Energies, MDPI, vol. 13(4), pages 1-15, February.
    12. Marcin Jarnut & Jacek Kaniewski & Mariusz Buciakowski, 2025. "Energy Storage Systems for Fluctuating Energy Sources and Fluctuating Loads—Analysis of Selected Cases," Energies, MDPI, vol. 18(18), pages 1-18, September.
    13. Shao, Lingjie & Wu, Junle & Ma, Jinghan & Yu, Shengjie & Li, Mengsi, 2025. "Valuation and optimal operation of power investment projects with and without volume constraints under one-factor model," Energy, Elsevier, vol. 330(C).
    14. Fan Li & Dong Liu & Ke Sun & Songheng Yang & Fangzheng Peng & Kexin Zhang & Guodong Guo & Yuan Si, 2024. "Towards a Future Hydrogen Supply Chain: A Review of Technologies and Challenges," Sustainability, MDPI, vol. 16(5), pages 1-36, February.
    15. Burger, Scott P. & Luke, Max, 2017. "Business models for distributed energy resources: A review and empirical analysis," Energy Policy, Elsevier, vol. 109(C), pages 230-248.
    16. 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).
    17. Schriever, Marlene & Halstrup, Dominik, 2018. "Exploring the adoption in transitioning markets: Empirical findings and implications on energy storage solutions-acceptance in the German manufacturing industry," Energy Policy, Elsevier, vol. 120(C), pages 460-468.
    18. Marquant, Julien F. & Evins, Ralph & Bollinger, L. Andrew & Carmeliet, Jan, 2017. "A holarchic approach for multi-scale distributed energy system optimisation," Applied Energy, Elsevier, vol. 208(C), pages 935-953.
    19. Ann-Kathrin Klaas & Hans-Peter Beck, 2021. "A MILP Model for Revenue Optimization of a Compressed Air Energy Storage Plant with Electrolysis," Energies, MDPI, vol. 14(20), pages 1-21, October.
    20. Elshurafa, Amro M. & Alatawi, Hatem & Hasanov, Fakhri J. & Algahtani, Goblan J. & Felder, Frank A., 2022. "Cost, emission, and macroeconomic implications of diesel displacement in the Saudi agricultural sector: Options and policy insights," Energy Policy, Elsevier, vol. 168(C).

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:2606.22185. 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: arXiv administrators (email available below). General contact details of provider: https://arxiv.org/ .

    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.